Article

Process parameter optimization of plastic injection molding: a review

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Abstract

Over the years, injection molding has been a premier manufacturing technique in the production of intricate polymer components. Its molding efficiency rests on the shoulders of multiple process and machine parameters, which dictate the final product quality in terms of multiple output responses. It is imperative to state that a precise optimization of various input parameters is paramount for achieving the desired quality indices. In this article, a review of different techniques employed till date for optimizing various injection molding parameters is presented along with their advantages and limitations. It is found in the review that a complete intelligent technique operable without human interference is yet to be developed. © 2015, Central Institute of Plastics Engineering & Technology.

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... The polymer injection molding process has been used for years to manufacture geometrically complex parts (Araújo et al., 2023;Czepiel et al., 2023;Kalwik et al., 2022;Kashyap & Datta, 2015;Khosravani & Nasiri, 2020;Wang et al., 2020) from multiple materials (thermoplastics, thermo-sets, elastomers, foams, and composite materials) (Chung et al., 2021;H. Fu et al., 2020;Godec et al., 2021;Jachowicz et al., 2021). ...
... When manufacturing a part by injection molding, many factors significantly influence its quality (physical and structural condition) and properties (thermal, functional, and mechanical) (Chung et al., 2021;Czepiel et al., 2023;Godec et al., 2021;Hentati et al., 2019;Jachowicz et al., 2021;Kalwik et al., 2022;Khosravani & Nasiri, 2020;Myers et al., 2023;Wang et al., 2020;Yu et al., 2020). The molded part is the result of the processed polymeric material (physicochemical and rheological properties), the design characteristics of the injection mold (wall thickness, surface inclination, radius of rounding of the edges, shape, dimensions of the cross sections, precision desired geometry and material of the injection mold) and the specific parameters of the process (injection temperature, mold temperature, injection and packing pressure, injection speed, cycle time, clamping force) (Araújo et al., 2023;De Miranda & Nogueira, 2019;Kashyap & Datta, 2015;Shen et al., 2008). Understanding and identifying the key factors that impact the final product and cycle time of the injection molding process has been a part of academic and industrial research for a long time (Abdullah et al., 2023;Hentati et al., 2019;Kashyap & Datta, 2015;Veltmaat et al., 2022). ...
... The molded part is the result of the processed polymeric material (physicochemical and rheological properties), the design characteristics of the injection mold (wall thickness, surface inclination, radius of rounding of the edges, shape, dimensions of the cross sections, precision desired geometry and material of the injection mold) and the specific parameters of the process (injection temperature, mold temperature, injection and packing pressure, injection speed, cycle time, clamping force) (Araújo et al., 2023;De Miranda & Nogueira, 2019;Kashyap & Datta, 2015;Shen et al., 2008). Understanding and identifying the key factors that impact the final product and cycle time of the injection molding process has been a part of academic and industrial research for a long time (Abdullah et al., 2023;Hentati et al., 2019;Kashyap & Datta, 2015;Veltmaat et al., 2022). Each product manufactured by injection molding is a particular process, and it is necessary to find acceptable limits of the factors to ensure successfully molded parts with reproducibility, efficiency, and profitability, involving considerable time and money. ...
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La simulación computacional de la dinámica del fluido presentada muestra el comportamiento de la mezcla PC+ABS durante la fase de inyección mediante un análisis transitorio del proceso de moldeo por inyección. El módulo de análisis fluidodinámico computacional Fluent® de Ansys Workbench® posibilita conocer el comportamiento del material inyectado de acuerdo a sus propiedades y al diseño de la geometría del producto inyectado, representado por las cavidades del molde (domino fluido). La implementación de la simulación permite a los ingenieros y procesadores analizar de manera eficiente la fase de llenado desde etapas tempranas de diseño debido a la obtención de los resultados de presión máxima de llenado, visualización del frente de flujo del polímero, el incremento de la presión a la entrada, y la temperatura del frente de flujo al final de la fase de inyección. En conclusión, la simulación computacional genera una comprensión previa de la fase de llenado al tiempo que minimiza las fallas encontradas hasta etapas avanzadas de la producción (molde de inyección y producto inyectado fabricados). Además, garantiza la reducción de tiempos y costos del proceso de moldeo por inyección mediante un entorno completamente asistido por ordenador.
... Injectionmolded parts quality is influenced because of many operating parameters [6,7]. Inappropriate choice of any parameter affects the near-net-shape manufacturing capability (parts require post-processing operations), increases manufacture lead time, energy consumption, and cost [8,9]. Therefore, influencing parameters to fabricate high-quality products require process optimization. ...
... The product quality of injection molded parts is influenced by many parameters. The appropriate parameters and levels are decided after conducting experiments and consulting literature [7,8]. The parameters and levels set for the present work are given in Table 1. ...
... The desired minimal surface roughness was obtained at the mid-values of injection speed as shown in Fig. 2. This might be due to filling defects at low injection speed and burrs and jetting at higher injection speed [8]. A combination of low melt temperature and high mold temperature resulted in reduced surface roughness values (refer Fig. 2). ...
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Injection molding process is a complex manufacturing method used to produce intricately shaped and highly precision product like laryngoscope. Laryngoscope is a device used by anesthetists for airway management. Injection molding process is very challenging due to various parameters of process to be controlled in design and in the manufacturing the product. It is important to optimize this process to get highly precision product. In the present paper, a simple Python code for optimization of injection molding process is developed logically and implemented to produce laryngoscope using the technique of Taguchi method. In this, four parameters and three levels using L9 orthogonal concept of design of experiments is implemented. Degree of freedom analysis approach is also adapted to balance the stable and dynamic state of the process. The optimization method resulted in reduction in surface roughness from the peak value of 0.589–0.214 µm. The Python codes are written for the systematic analysis of method of optimization.
... Moreover, it strongly recommended that diverse factors affecting the typical plastic injection process must be analysed properly before deciding the applicability of manufacturing a product with the desired quality and complexity. By the way, these factors are classified into three categories as follows [10]: Independent machine parameters: Barrel and nozzle temperatures, coolant temperature, packing and holding pressures, back and injection pressure, sequence and motion, injection speed, screw speed, shot volume, cushion. ...
... Final quality responses: Part dimension, shrinkage, warpage, sink marks, appearance and strength at weld lines, and other aesthetic defects such as burn marks, gate blushes, surface texture, etc. [10]. ...
... However, it is still a compelling and expensive procedure to understand the relationship between process parameters and output variables because of the necessity of quite many experiments. That is why; some important approaches like using the Taguchi method [11], artificial neural network [10], or finite element software [12] needed to minimize the number of experiments, provide self-learning, and predict the outcomes of the production respectively. It was reported that some commercial software packages were developed which use the finite element method to estimate stages of injection molding [13]. ...
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Polipropilen (PP), hafiflik ve tokluk, yüksek kimyasal dayanım, şekillendirilebilirlik, darbe ve rijitliği dengelenmiş iyi bariyer özelliklerinden dolayı medikal ve otomotiv parçaları, ev eşyaları ve gıda ambalajlarının imalatında yaygın kullanılan kristalin termoplastik malzemedir. Bu bağlamda, araştırmacılar ve mühendisler üretim zamanı ve ürün maliyetini azaltmak için yeni enjeksiyon metotları üzerine yoğunlaşmışlardır. Bu çalışmada yeni enjeksiyon metotlarıyla ilgili literatür araştırmasından sonra, üretim hurdası PP ve orijinal PP’ nin özellikleri kullanılarak 3-B Moldex programıyla eş enjeksiyon simülasyonlar gerçekleştirilmiştir. Böylece, enjeksiyon zamanı, enjeksiyon basıncı, kapama kuvveti, kalıp ve enjeksiyon sıcaklığı gibi eş-enjeksiyon parametrelerinin dolum karakteristiğine etkisi araştırılmaya çalışılmıştır. İlk enjekte edilen orijinal PP’ nin yolluk merkezindeki sıcaklığı çekirdeklenme sıcaklığından yüksek olduğunda, eş zamanlı ikinci enjekte edilen üretim hurdası PP’nin orijinal PP katılaşana kadar yolukta ilerlemekte olduğu sonucuna varılmıştır. Eş-enjeksiyon simülasyon sonunda, ilk enjekte edilen ve ilerleyen orijinal PP’ nin yerini ikinci enjekte edilen üretim hurdası PP almış ve orijinal PP, hurda PP, orijinal PP’ den oluşan üç katmalı cidar elde edilmiştir.
... Several defects can occur during injection molding of polymers due to incorrect settings, such as warpage, flash, short shot, and bubbles [14]. To achieve the desired part quality, dimensional accuracy, and production efficiency, injection molding parameters must be optimized for PLA. ...
... This shows that the polymer is high in viscosity and flows slowly in the mold which could be due to several factors such as low melt or mold temperature, low injection speed, and insufficient injection pressure [14]. It must be mentioned that other parameters such as wall thickness, or dimension of runner could have slight influence. ...
... Esteemed for its precision in manufacturing complex polymer components, injection molding allows for the creation of parts from a variety of plastic materials, both thermoplastic and thermoset, sparking interest in its application within the realm of radiotherapy. The process involves injecting molten plastic under high pressure into meticulously crafted molds, resulting in parts that faithfully replicate the intended design (Özdilli, 2021;Kashyap and Datta, 2015). Unlike 3D printed alternatives, injection-molded materials can be pre-shaped to fit the patient's anatomy precisely, offering a rigid form that adheres closely to the body, thus minimizing the risk of air gaps and ensuring a more accurate dose delivery. ...
... However, it is important to note that while injection molding can provide a precise fit, it requires the production of a mold corresponding to the patient's anatomy. The versatility of bolus materials, coupled with the array of manufacturing techniques available, (i.e., support opportunities to meet) the need for materials that are not only effective in their function but also safe for skin contact, devoid of any adverse odors, and non-sticky (Kashyap and Datta, 2015;Banaee et al., 2013;Aras et al., 2020b). Among the numerous materials suitable for such applications, Acrylonitrile Butadiene Styrene (ABS) and Polylactic Acid (PLA) stand out. ...
Article
The impact of radiation on polymer materials, and consequently on their mechanical and physical properties, is a burgeoning field of research. This research assesses the dosimetric, mechanical, and thermal characteristics of Acrylonitrile Butadiene Styrene (ABS) and Polylactic Acid (PLA) materials developed through plastic injection molding as possible alternatives to commercial bolus used in radiotherapy. The materials were thoroughly compared with the commercial bolus material, EXAFLEX, by various tests such as Hounsfield Unit (HU) Analysis, Radiation Transmittance, Thermogravimetric Analysis (TGA), Thermomechanical Analysis (TMA), and Fourier Transform Infrared Spectroscopy (FTIR). The research indicates that ABS and PLA materials have similar radiological qualities to EXAFLEX, showing constant surface and accumulation area doses. Minor differences are seen deeper inside the phantom, indicating a stable tissue equivalency. ABS demonstrated strong resistance to radiation induced chemical modifications, but PLA exhibited susceptibility to molecular changes. The TMA findings showed that radiation caused a decrease in softening temperatures and an increase in the coefficient of thermal expansion for both ABS and PLA, indicating changes in their thermal stability and mechanical characteristics after irradiation. The study highlights the potential of ABS and PLA materials made through plastic injection molding as efficient and safety substitutes for commercial bolus materials, particularly for large area irradiation. This requires more research into their clinical uses and consequences.
... Since feed system is one of the most critical mold systems, much research has been carried out on feed system optimization for process improvement. Plastic injection molding is a complex process dependent on a combination of many process parameters [12]. Each of these parameters have specific contribution to the molded product defects. ...
... The complexity of the process arises from the fact that the process parameters have varying degrees of influence to each defect; hence, the optimization of process parameters to control one defect may end up encouraging another defect. Melt temperature, mold temperature, injection rate, injection pressure, packing pressure, packing duration, and cooling time are some of the main process variables that affect the part's quality [12]. ...
Article
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Although technological advancements have brought about the inventions of hot runner injection molding machines, cold runner injection molding machines are still in application and are subject to many defects associated with filling such as short shots and sink marks. Many studies have, therefore, been carried out in an attempt to optimize and minimize defects. These studies have, however, not studied several combinations of input parameters and their effects on sink marks and short shots. The highlighted inadequacies of previous studies have led this research to investigate the effect of variation of feed system features and process parameters on short shot and sink mark defects. Through Computer Aided Engineering (CAE), Design of Experiments (DoE), and data analysis the study determined the effects of variation of process parameters, runner and gate shapes on the selected performance measures. A Taguchi L27 orthogonal array was undertaken for optimization based on three levels of runner shape, gate shape, melt temperature, injection pressure limit and filling time and the responses in short shot possibility and sink marks determined at each input combination based on DIX-SI grade polystyrene material. Parameters found to have the most critical effect on short shot defects included melt temperature, injection pressure, and runner shape, whereas those with the most critical effect on sink marks were fill time, gate shape and runner shape.
... Injection-moulded parts quality (i.e., surface roughness) are influenced mainly by packing time, cooling time, clamping pressure, injection speed and pressure, melt and mold temperature and so on [7,8]. Inappropriate choice of these parameters affects the near-net-shape manufacturing capability (parts require post-processing operations), increases manufacture lead time, energy consumption and cost [9,10]. Therefore, influencing parameters to fabricate high-quality products require process optimization [9]. ...
... Inappropriate choice of these parameters affects the near-net-shape manufacturing capability (parts require post-processing operations), increases manufacture lead time, energy consumption and cost [9,10]. Therefore, influencing parameters to fabricate high-quality products require process optimization [9]. A try-error method in optimizing parameters may results in increased complexity (in terms of material usage, cost, labour, power, and so on) and time with complete reliant on experts in defining the optimal solutions [11]. ...
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The present work focuses on the mold design and production of the multifunctional device laryngoscope with surface quality through injection molding process. Laryngoscope is a device used by the anesthesiologists to lift the tongue that facilitate to fix the air pipe in the larynx. The laryngoscope is a double channeled device with one for aligning the camera and another for the air pipe. The paper outlines the design parameters required for manufacturing a single cavity mold to produce laryngoscope viz. injection molding machine. The mold has multiple plates with complex fluid channels which ensures the effective thermal management in mold system. The mold is manufactured using high strength tool steel materials and the product laryngoscope (ABS: Acrylonitrile butadiene styrene) is fabricated from the designed mold. Taguchi L9 experimental array was used to determine the optimal conditions (injection pressure, injection velocity, mold and melt temperature) for desired surface finish in the laryngoscope parts. The designed mold and optimized injection molding conditions resulted in lower surface roughness value equal to 0.214 µm. Thereby, injection molded laryngoscope parts can be used for large scale productions for the benefit of medical applications.
... A lack of consistent machine control causes material degradation and inferior product quality that is discarded as scrap [19]. There are different types of quality problems such as shrinkage, warpage, color and burn marks, surface texture quality, shape distortion, and other aesthetic defects [19,16]. In real-world industrial production, maintaining consistent product quality requires a detailed domain understanding of parameters dependencies; otherwise, the product quality can be inconsistent between production lots for various reasons. ...
... Many studies related to quality optimization are based on Taguchi experimentation with fewer key process variables that are responsible for product quality [28,27,32,5,3,22,6,20,15]. Several computational approaches have been considered to optimize product quality, which use gradient-based approaches, evolutionary algorithms, and mixed approaches [35,36,20,34,6]. Reviews of frameworks that optimize injection molding methods are described in [16,8,26,12]. [24] applied Support Vector Machine (SVM) [7] models by tuning different hyperparameters for error classification. The required data for the analysis were generated with an experimental set up on a Demag injection molding machine with Hostacom DM2 T06 polymer and the DN502 mold to predict product quality. ...
Conference Paper
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This paper analyzes production data from injection molding processes to identify key interactions between the process variables from different material categories using the network inference method called “bagging conservative causal core network” (BC3net). This approach is an ensemble method with mutual information that is measured between process variables to select pairs that show significant shared information. We construct networks for different time intervals and aggregate them by calculating the proportion of significant pairs of process variables (weighted edges) for each production process over time. The weighted edges of the aggregated network for each product are used in a machine learning model to optimize the network interval size (interval split) and feature selection, where edge weights are the input features and material categories are the output classification labels. The time intervals are optimized based on the classification accuracy of the machine learning model. Our analysis shows that the aggregated edge features of inferred networks can classify different material categories and identify critical features that represent interdependence in the associated process variables. We further used the “one vs. other” labels for the machine learning models to identify material-specific interactions for each material category. Additionally, we constructed an aggregated network over all samples in which the process variable interactions were steady over time. The resulting network showed modular characteristics where process variables of similar categories were grouped in the same community.
... Some researchers have extensively examined another crucial material property regarding tensile strength [15][16][17] . Additionally, the fuzzy logic approach and the response surface methodology method were compared in the context of the injection molding process [18][19] . ...
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Recycling plastic products is still essential and crucial in every country around the globe due to its positive benefits on the environment and the economy. There are several mixing procedures for recycling, and it is crucial to understand how these mixtures affect the quality of products by standards and specifications. Consequently, it is helpful to apply analysis and prediction techniques to find out scientifically. Artificial intelligence "AI" techniques are widely used in many manufacturing engineering fields such as recycling operations. This is because of the many advantages that artificial intelligence techniques offer, including the ability to reduce human errors, save time, provide digital support, and make objective decisions. This study intends to employ the fuzzy logic method as one of the "AI" techniques for predicting a significant property that customers frequently need based on their quality levels and standards. This study employed the injection molding process to forecast the values of a mechanical characteristic, specifically tensile strength, under specific operating conditions based on data from the authors' earlier work. This investigation was conducted using two distinct mixing plans. The first mixed all the raw materials, while the second mixed 50% of the raw materials with 50% of the recycled materials. The fuzzy logic results were acquired, and the mean absolute percentage error for the two plans was calculated. Additionally, the outcomes of the current study, which employed the fuzzy logic approach, were contrasted with those of the earlier study, which utilized the response surface methodology approach. Furthermore, the results showed that the response surface technique approach is more accurate than the fuzzy logic since it has the lowest mean absolute percentage error.
... As shown in Figure 3, the IM UMP models was analysed for variations in four manufacturing process parameters: (i) diameter of the fluid inlet to the mould ( ), (ii) ejection temperature ( ), (iii) melting temperature ( ), (iv) injection velocity ( ), and one relevant design parameter: (i) fraction of virgin material used in manufacturing the PVC flange ( ). These parameters were selected as they represent controllable parameters in IM processes and play a crucial role in obtaining the net shape of the final part (Kashyap andDatta, 2015, Lee et al., 2015). ...
... whereas the output primarily consists of manufacturing expenses, product quality, and molding efficiency [7,8]. The molds, machines, materials, and products are determined before processing, meaning the molding process parameters must be attentively set to increase productivity and prevent or decrease quality defects, computational consumption, and cost [9]. ...
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An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer–metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance.
... Most process parameter optimisation systems such as genetic algorithms, adaptive neuro-fuzzy inference systems, artificial neural networks, back-propagation neural networks, and hybrid methods, work in an offline mode and are used by manufacturers to find the optimal processing conditions for a particular product (Kashyap & Datta, 2015). ...
Technical Report
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The report is about the integration of different Industry 4.0 technologies for real-time remote monitoring and maintenance of Machines on plastic industries.
... Like the other methods, injection molding processes cause basic variations in the rheology and thermodynamics related properties of the products due to stress changes, experiencing high temperatures and cooling rates during the process. Therefore, it is important to carefully analyze the factors that may affect injection molding process and product performance before determining the manufacturing process for the product [45,46]. ...
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Biodegradable plastics/polymers may serve as a promising solution to the global problem of plastic waste accumulation in oceans and soil and may significantly reduce carbon emissions from the manufacturing process, since the materials used to make biodegradable polymers are carbon-based and emitted during the synthesis processes. This article systematically reviewed the existing and closely related scientific literature on materials, biomaterials, and biodegradable materials to find answers on how to effectively study and develop biodegradable polymers. This article reviewed and summarized the source classifications of the biodegradable plastics. Some of the major manufacturing techniques for making biodegradable polymer products were discussed, including micro-extrusion for biofibers, solvent casting method for thin films, 3D printing, injection and compression molding and extrusion processes, as well as the fabrication methods applied to some important biopolymers, such as cellulose, starch, bacterial concrete, packaging materials, and paper-based biodegradable materials. More importantly, experimental and computational methodologies applied for materials characterization and development that can be adopted to characterize the properties of biodegradable polymers and understand the physicochemical mechanisms of the materials were described in detail, including experimental methods (physical and chemical methods) and computational methods at different scales (from quantum mechanics at subatomic scale, molecular dynamics at atomic scale, to finite element analysis at micro or macro scale), and data analysis methods. The degradation mechanisms and factors affecting the biodegradability of the polymers were discussed. Finally, the future perspective of biodegradable polymers has been described. Properly adopting the effective state-of-the-art biomaterial research and characterization techniques (experimental and computational methods) and advanced data analysis methods discussed in this article will help advance the development of novel biodegradable polymers.
... The reciprocating screw technique is the most common. For thermoset processing, only screw piston machines are applicable due to the essential role of the screw in preventing prolonged dwell time and avoiding the risk of early crosslinking [14][15][16][17][18]. ...
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Metal-polymer hybrid (MPH) injection molding is an innovative manufacturing process with vast potential in today industries. This article reviews MPH, covering its introduction, applications, significance, and associated challenges. The introduction outlines MPH unique principles distinguishing it from traditional injection molding techniques. Next, the MPH potential in the automotive industry is explored, highlighting its ability to merge high-strength metal components with polymer matrix, resulting in improved structural integrity, decreased weight, reduced gas emissions, and promising cost savings. In addition, the process allows the production of complicated shapes, providing a large design window. Furthermore, its production sustainability potential makes it an interesting alternative for eco-friendly goals. Nevertheless, several technical challenges face MPH process, which have been observed in previous research, such as process parameters, thermal expansion, shrinkage behavior, interfacial adhesion, and manufacturing defects through the molding operation are mentioned thoroughly. Furthermore, considering all the relevant factors, the future scope of study and development has also been evaluated.
... The complex coupling relationships between the numerous process parameters in the injection molding process pose challenges in accurately quantifying the individual impact of each parameter on the quality of the molded plastic products [8][9][10][11]. In injection molding, there has always been a significant focus on finding efficient and precise methods to determine the optimal injection molding process parameters for plastic products, considering multiple quality requirements. ...
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The key of the multi-objective optimization of injection molding processes lies in achieving a balance between the accuracy of the surrogate model and the multiple objectives while taking the diversity and interdependence of process parameters into consideration. However, the sampling process for building high-precision surrogate models requires a large number of sample points, resulting in high modeling costs for other regions. Moreover, the selection of Pareto fronts often relies solely on the magnitudes of objective values, without considering the uncertainties associated with the information. To address these issues, this research proposes a novel multi-objective optimization method for injection molding process parameters, using hierarchical sampling and integrated entropy weighting. The method introduces a unique hierarchical sampling approach to enhance the accuracy of the surrogate model in injection molding, with a specific focus on critical components. Additionally, our method incorporates entropy calculations for multiple objective defect value parameters during the multi-objective optimization process, enhancing the rationality of the optimization process. The proposed method is utilized to optimize the injection molding parameters of a thin-walled propeller blade. The result shows that our surrogate model fits well and exhibits superior performance compared to the response surface method in optimizing multiple objectives.
... Injection molding is an industrial technique, involves injecting plastic material into a mold cavity under high pressure, which can range from hundreds to thousands of bars (2). Once injected, the plastic cools and solidifies within the mold cavity, taking the shape of the mold and producing a specific plastic product (10,11). Figure 1 illustrates the five main stages of the standard production cycle. ...
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In injection molding manufacturing, the selection of the optimal machine from various alternatives is a crucial strategy for enhancing productivity, cost-effectiveness, and maintaining performance standards. This article presents an approach that combines two techniques to make the best choice from three presented options. Firstly, it employs the Analytic Hierarchy Process method to determine the weights of five main criteria and eleven sub-criteria, considering both cost and performance. Secondly, it utilizes the Technique for Order Preference by Similarity to Ideal Solution to rank the three machines by comparing each machine's performance against ideal and anti-ideal solutions to determine their relative suitability. The final model is validated through three distinct scenarios, illustrating how key criteria such as cost breakdown and scrap rate can influence the ultimate selection ranking. Through a presented numerical example, the paper provides decision-makers with a scientifically robust decision support system, aiding in strategic and complex decision-making processes for selecting the most suitable machine
... Throughout the injection process, maintaining consistent production relies on the interplay of over 50 variables [10,11]. These variables are determined by factors such as the part geometry (weight and dimensions of the plastic part), press specifications (screw diameter, injection pressure, etc.), material properties (density, fluidity, amorphous nature, etc.), and mold characteristics (size, hot runner, etc.). ...
Article
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The injection molding process is considered as one of the most used process in the plastics industry due to its reliability and its profitability; however nowadays, the injection industry marketplace becomes more and more competitive because of the excessive quality demand and the coast reduction requirement. Production workshops strive constantly to reduce coast and optimizing the process. One key optimization factor involves determining the optimal cooling time parameters during the initial setup phase. The cooling time parameter represents around 65% of the cycle time. The main goal of this study is to explore the implementation of various supervised machine learning methods for predicting the cooling time parameter and to compare their performance. Five algorithms, namely random forest, decision tree, KNN (K-nearest neighbors), XGBoost, and multiple regression, were employed in the analysis. The study aims to assess the effectiveness of these algorithms in predicting the cooling time parameter within the context of the injection molding process. To evaluate their efficiency, the study employed the following metrics: mean absolute error (MAE), root mean square error (RMSE), mean squared error (MSE), and mean average percentage error (MAPE). The dataset was collected from a real industrial workshop, encompassing 70 plastic components, 10 distinct material types, and 7 different types of machines. Despite the complexity and non-linearity among the process parameters, the study indicates that machine learning can still effectively capture and predict cooling time parameters. XGBoost, KNN, and random forest consistently demonstrate superior results across all metrics compared to decision tree and multiple regression, as example, the mean average percentage error (MAPE) of XGBoost is 14.76%, significantly outperforming the 23.96% MAPE associated with the decision tree. These outcomes validate that machine learning methods can play a significant role in predicting cooling time and contribute to the optimization of the overall process.
... Some limitations in the applicability of self-lubricating materials using polymeric materials may be the non-linearity of the thermal expansion coefficient, creep phenomenon typical of polymers, softening at elevated temperatures, processing shrinkage and hygroscopicity leading to swelling [28][29][30]. Sliding bearings made of solid plastics are limited by production technologies such as injection molding and extrusion, where they must meet specific shape and dimension criteria [31,32]. ...
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In this work, epoxy composites filled with flake graphite of various size (less than 10 µm and less than 45 µm) were produced. The aim of the research was to develop a self-lubricating material with favorable tribological properties, i.e., reduced friction coefficient compared to unfilled epoxy resin and limited abrasive wear. The research material was produced using technical epoxy resins based on bisphenol A. The detailed process of composite production was described, and typical technological problems were considered. The addition of graphite led to an increase in dynamic viscosity, which positively limits the phenomenon of sedimentation, but an increase in the filler content also led to an increase in the porosity of the material. A series of tests have shown that the addition of graphite above 5% by weight allows for a reduction in the friction coefficient from 0.6 to 0.4 and significantly reduces the material's tendency to abrasive wear.
... An AI/ML model has been regarded as "black box" [54] since their prediction results cannot be explained or interpretable directly because of the numerous weight values embedded in the models. Therefore, the interpretation of the relationship between the input and output of ML models, such as neural networks, for injection molding had been treated as impossible [55]. Recent developments of XAI and IML have enabled reasonable explanations on the model's prediction. ...
Article
This paper proposes a novel injection molding process optimization method based on the in-mold condition (IMC) and interpreted influence of in-mold condition features on part quality. In-mold condition is crucial for process optimization because it represents the actual process condition in the cavity where the polymer material is formed into the final part shape. Traditionally, the analysis of in-mold condition heavily depends on domain knowledge and insight, which introduces bias and inconsistency in in-mold condition-based process optimization. This study aims at developing an intelligent, objective, and robust process optimization method that concentrates on the highly influential in-mold condition features concerning part-quality, as interpreted by explainable artificial intelligence (XAI). In this in-mold condition-centered modeling approach, the input process parameters and final part quality were associated with the in-mold condition, with their corresponding relationship modeled by two machine learning (ML) models, respectively. The effect of in-mold condition on part quality was interpreted by applying XAI on the ML model that describes the relationship between in-mold condition and part quality. Features in the in-mold condition profiles that have high influence on part quality are given more weight in the search for diverse in-mold conditions that satisfy multiple part-quality objectives. A feasibility check has been implemented to identify, among those potential in-mold conditions, the optimal one that is physically feasible based on the ML model that governs the relationship between the process parameters and in-mold condition. The proposed method not only pinpoints better optimized process parameters than the conventional approach that omits the in-mold condition and only considers the direct relationship between the process parameters and part quality, but also reveals the possibility of further quality improvement. The optimization tool of the proposed method can be found on an online interactive platform https://imc-xai-injmold-optimization-4e26a3e3ef41.herokuapp.com/, which was created to facilitate further research based on the proposed approach. In addition to process optimization, this approach can effectively contribute to intelligent manufacturing management and Industry 4.0.
... On top of that, quality control is critical criterion in achieving high-quality specifications. There are some parameters that have major influence on the process that should be taken into consideration in the injection molding process, such as injection temperature, resin melt properties, etc [1,2]. Regarding the previous research, it noted that the disk weight serves as a good quality index for injection molding process [3]: the higher the weight, the stronger the molded disk as air bubbles. ...
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This research describes developing a Fuzzy Logic based weight prediction system (FL-eWPS) during the process of injection molding. The main purpose is to apply Fuzzy Logic to predict defects during injection molding operations while processing parameters, such as shot size, barrel temperature, cooling time, and holding pressure. The parameters are varied within a shorter range when using Delrin 511 DP plastic from DuPont Engineering Polymers. eDART data logging system was used for real-time data collection for the different parameters by using the sensors during the injection filling stages. A Fuzzy Logic reasoning algorithm was applied to gain the threshold values of weight prediction with various processing parameter settings. During the injection molding process, the FL-eWPS system was shown to predict weight with 99% accuracy.
... Production parameters vary due to the mixing of thermoplastics or the use of filler (organic or inorganic) [38,39]. Although mathematical, computational and statistical techniques are used to determine the parameters [40], trial and error method is also needed [41]. ...
... Finally, there are the quality indicators, which include, among others, part weight, dimensions, shrinkage, warpage and surface defects. Depending on the part to be manufactured and the requirements set, relevant variables for quality assessment are selected and quantified [6,7]. ...
... ML models have been treated as 'black box' (Ribeiro, Singh, and Guestrin 2016) that its prediction results cannot be explained or interpretable directly due to the numerous numbers of weight and bias values incorporated in the ML models. Especially, interpretation of the inter-relationship between input and output of the neural networks for injection molding has been regarded as impossible (Kashyap and Datta 2015). As attempts to extract influential features from the transient process data from the molding machine (Zhou et al. 2018), XAI and IML have been employed to understand the relationship between process parameters and quality or defects (Román et al. 2021). ...
Article
This paper proposes an interpretation methodology for the effect of transient process data on quality of injection molded parts. The transient process data measured in the actual processing space have been regarded as the most relevant information to manufacturing processes and product quality. However, its interpretation to pinpoint which feature in the data would affect part quality has traditionally relied on knowledge and understanding of the manufacturing process. The main objective of this method is to reduce the dependency of the transient process data analysis on process knowledge and understanding by using explainable artificial intelligence (XAI). The contribution of the ‘section-wise' features in the transient process data to the quality prediction of machine learning (ML) models was investigated for the first time. The interpretation results of the effect of cavity pressure and mold surface temperature on four different quality factors represented reasonable explanations of the characteristics of the polymer materials, product geometry, and molding process. Due to the intermediate relationship of the transient process data with the user-specified process parameters and the resulting quality variables, the interpretation results can be further utilized to optimize the process and provide the optimal transient process data profile for best part quality.
... However, the wide application of equipment upgrading is costprohibitive for the majority of small enterprises. In contrast, optimizing process parameter settings is a relatively more practical and feasible way towards quality assurance and energy conservation (Madan et al. 2013; Kashyap and Datta 2015;Selvaraj et al. 2022). ...
Article
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The high energy intensity and rigorous quality demand of injection molding have received significant interest under the background of the soaring production of global plastic industry. As multiple parts can be produced in a multi-cavity mold during one operation cycle, the weight differences of these parts have been demonstrated to reflect their quality performance. In this regard, this study incorporated this fact and developed a generative machine learning-based multi-objective optimization model. Such model can predict the qualification of parts produced under different processing variables and further optimize processing variables of injection molding for minimal energy consumption and weight difference amongst parts in one cycle. Statistical assessment via F1-score and R² was performed to evaluate the performance of the algorithm. In addition, to validate the effectiveness of our model, we conducted physical experiments to measure the energy profile and weight difference under varying parameter settings. Permutation-based mean square error reduction was adopted to specify the importance of parameters affecting energy consumption and quality of injection molded parts. Optimization results indicated that the processing parameters optimization could reduce ~ 8% energy consumption and ~ 2% weight difference compared with the average operation practices. Maximum speed and first-stage speed were identified as the dominating factors affecting quality performance and energy consumption, respectively. This study could contribute to the quality assurance of injection molded parts and facilitate energy efficient and sustainable plastic manufacturing.
... However, the trialand-error approach is ineffective for complex manufacturing processes [75][76][77]. Therefore, many studies had been carried out over the years to minimise shrinkage and warpage defects by optimising the processing parameters [77][78][79][80]. In addition, it has also been observed that various critical processing factors, including packing pressure, melt temperature, packing shrinkage duration, mould temperature, and cooling time, have an impact on the quality of the moulded components produced (warpage) [77,[81][82][83]]. ...
Article
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The investigation of mould inserts in the injection moulding process using metal epoxy composite (MEC) with pure metal filler particles is gaining popularity among researchers. Therefore, to attain zero emissions, the idea of recycling metal waste from industries and workshops must be investigated (waste free) because metal recycling conserves natural resources while requiring less energy to manufacture new products than virgin raw materials would. The utilisation of metal scrap for rapid tooling (RT) in the injection moulding industry is a fascinating and potentially viable approach. On the other hand, epoxy that can endure high temperatures (>220 °C) is challenging to find and expensive. Meanwhile, industrial scrap from coal-fired power plants can be a precursor to creating geopolymer materials with desired physical and mechanical qualities for RT applications. One intriguing attribute of geopolymer is its ability to endure temperatures up to 1000 °C. Nonetheless, geopolymer has a higher compressive strength of 60–80 MPa (8700–11,600 psi) than epoxy (68.95 MPa) (10,000 psi). Aside from its low cost, geopolymer offers superior resilience to harsh environments and high compressive and flexural strength. This research aims to investigate the possibility of generating a new sustainable material by integrating several types of metals in green geopolymer metal composite (GGMC) mould inserts for RT in the injection moulding process. It is necessary to examine and investigate the optimal formulation of GGMC as mould inserts for RT in the injection moulding process. With less expensive and more ecologically friendly components, the GGMC is expected to be a superior choice as a mould insert for RT. This research substantially impacts environmental preservation, cost reduction, and maintaining and sustaining the metal waste management system. As a result of the lower cost of recycled metals, sectors such as mould-making and machining will profit the most.
... An area related to PdM is Quality Prediction [99]. A work that applies CL to this problem is [202]. ...
Preprint
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Machine learning techniques have become one of the main propellers for solving many engineering problems effectively and efficiently. In Predictive Maintenance, for instance, Data-Driven methods have been used to improve predictions of when maintenance is needed on different machines and operative contexts. However, one of the limitations of these methods is that they are trained on a fixed distribution that does not change over time, which seldom happens in real-world applications. When internal or external factors alter the data distribution, the model performance may decrease or even fail unpredictably, resulting in severe consequences for machine maintenance. Continual Learning methods propose ways of adapting prediction models and incorporating new knowledge after deployment. The main objective of these methods is to avoid the plasticity-stability dilemma by updating the parametric model while not forgetting previously learned tasks. In this work, we present the current state of the art in applying Continual Learning to Predictive Maintenance, with an extensive review of both disciplines. We first introduce the two research themes independently, then discuss the current intersection of Continual Learning and Predictive Maintenance. Finally, we discuss the main research directions and conclusions.
... The time to eject the mold is when the solid layer of the surface reaches a sufficient thickness to provide rigidity. Like the other methods, this process causes important changes in the properties related to the rheology and thermomechanics of the biopolymers due to stress variations during the process, high temperature, and cooling rate of the final product [153]. Therefore, it is important to thoroughly analyze the factors that affect injection molding before deciding to manufacture a product. ...
Article
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Petroleum-based polymers are used in a multitude of products in the commercial world, but their high degree of contamination and non-biodegradability make them unattractive. The development and use of polymers derived from nature offer a solution to achieve an environmentally friendly and green alternative and reduce waste derived from plastics. This review focuses on showing an overview of the most widespread production methods for the main biopolymers. The parameters affecting the development of the technique, the most suitable biopolymers, and the main applications are included. The most studied biopolymers are those derived from polysaccharides and proteins. These biopolymers are subjected to production methods that improve their properties and modify their chemical structure. Process factors such as temperature, humidity, solvents used, or processing time must be considered. Among the most studied production techniques are solvent casting, coating, electrospinning, 3D printing, compression molding, and graft copolymerization. After undergoing these production techniques, biopolymers are applied in many fields such as biomedicine, pharmaceuticals, food packaging, scaffold engineering, and others.
... The difficulty of controlling the parameters has prompted several research studies and the application of several approaches over the years. [Satadru Kashyap et al.2014] [24] in their review of the evolution of the methods applied to the injection process, they specify that the injection process remains a process with high variability and fluctuation, which are due to the non-stability of the plastic itself and the different stress points of the process. Furthermore, they describe the multiple approaches applied for the determination of injection molding set parameters, such as [26] . ...
Chapter
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The thermoplastic injection process is an industrial technique that allows getting a high precision plastics part with high production rate. This process is considered one of the most complexes in the plastic industry due to its complexity and variability. The main problems in this technique can occur during two phases: first, during the initial setting when we try to identify the initial parameters for a new plastic part; and second, during mass production when there is a deviation in the production process. The purpose of this article is divided on three parts: first, is to make a basic review and to present overview of the main issues faced in this process, second part, is to present the contribution of the artificial intelligence methods to resolve this issues and finally to present a general guidelines for future researchers to resolve or reduce the process issues.
... Some model types, such as support vector machines or arti cial neural networks, effectively mimic the behavior patterns of an injection molding process from the mold tting datasets given. As a result, manufacturing specialists devised various ways that rely on arti cial intelligence (Fernandes et al., 2018;Heinisch et al., 2021;Kashyap & Datta, 2015;Mahapatra & Patnaik, 2007;Suhail et al., 2010). ...
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The study examined two types of design of experiments (DoE) methods for injection molding of a molded part. It evaluated them using an artificial neural network (ANN) and a support vector machine (SVM) via cross-validation and holdout validation. The innovative goal is to identify the most efficient and successful ways for modeling varied DoE. The influence of four processing parameters on the volumetric shrinkage of a thin polystyrene plate sample is simulated using factorial design and orthogonal Taguchi arrays design. As measured by root mean square error (RMSE), the prediction performance revealed that DoE with eight experimental points as in 241{2}^{4-1} for fractional factorial design and L8 for orthogonal Taguchi design is particularly efficient for this modeling simulation problem. Both design methods are beneficial and efficient because orthogonal Taguchi arrays play an essential role when the accuracy of fractional factorial designs is insufficient.
... When it comes to the production of polymer parts, no other process compares to the injection molding, as it produces more than 33% of all of the plastic parts. This process has been used in many different industries such as mechanical, aerospace, automotive and aeronautical, due to its capabilities to produce numerous parts all with different geometries and shapes at a low cost [1][2][3][4]. ...
Conference Paper
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In this paper, designing of plastic parts with complex geometry was analyzed. The process of injection molding has significant impact on the plastic part geometry and deviation from the desired shape of the plastic part which can be defined as a defect. In this research the possible ways to avoid these defects were discussed. Furthermore, a simulation of the injection molding process was conducted. Finally, the results from the injection molding simulation are analyzed and the recommendations for improved design of the plastic part geometries are presented.
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In this study, we investigate a novel method for determining product weight based on cavity pressure, measured by internal sensors integrated into the mold. The ultimate goal is to find a model that is better than the linear expressions in the literature based on the cavity pressure integral. We conducted experiments using different materials (ABS and PP) to assess the effects of holding pressure and time on product weight. The relationship between product weight, the pressure integral, and holding pressure was modeled with a saturation curve. This way, the maximum product weight achievable with holding pressure can be predicted. This method represents a significant advancement in quality control during injection molding, as product weight can be predicted within the production cycle before product ejection.
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A indústria de eletrônicos é um dos setores mais competitivos do mercado, onde o tempo de introdução de um produto (time-to-market) é crucial, assim como a redução do tempo de fabricação. Isso exige abordagens eficientes de prototipagem e manufatura. Assim, a Manufatura Aditiva (MA) se destaca como um método eficaz para o desenvolvimento de compartimentos plásticos, permitindo a produção rápida e precisa de protótipos e produtos finais. Neste artigo apresenta uma análise comparativa dos custos de fabricação de cases para placas eletrônicas utilizando os métodos de injeção plástica e impressão 3D. Para um lote de 6000 unidades, foram coletados orçamentos que revelaram que os custos associados à injeção plástica são consistentemente menores em comparação com a impressão 3D. Os custos de fabricação foram separados em custos de moldes e custos por peça, permitindo uma avaliação de- talhada das despesas de cada fornecedor. A análise gráfica mostrou que, apesar dos altos custos iniciais dos moldes na injeção plástica, o custo por unidade diminui significativamente com o aumento do volume de produção, tornando este método mais econômico para grandes quantidades. Em contraste, a impressão 3D, embora tenha menores custos iniciais e ofereça maior flexibilidade, apresenta custos mais elevados por peça em grandes volumes. O estudo conclui que a injeção plástica é a opção mais viável economicamente para produção em larga escala, enquanto a impressão 3D é mais indicada para produções menores ou para peças com designs complexos e personalizações frequentes.
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Many plastic injection molding (PIM) process optimization problems are multi-objective optimization problems that optimize multiple mechanical properties. This paper proposed a determining method of optimal process parameters and their effect ranks using Taguchi method and TOPSIS in the PIM. TOPSIS was used to convert multiple mechanical properties into single comprehensive response. It was selected as a reasonable multi-attribute decision-making (MADM) method from among some well-known MADM methods based on mean rank correlation coefficient and mean absolute rank deviation. Taguchi method was used to design experiment and find the optimal process parameters. The proposed method was applied to determine the optimal values of the process parameters: melt temperature (MT), packing pressure (PP), cooling time (CT), and injection pressure (IP) for improving tensile strength, elasticity module, flexural modulus, and impact strength in the PIM with Acrylonitrile-Butadiene-Styrene compound as plastic materials and AISI 1020 as mold materials. The optimal values of the process parameters were MT of 280°C, PP of 28 MPa, CT of 22 s, and IP of 50 MPa, and their effect ranking was IP (51.941%), PP (32.280%), CT (9.045%), and MT (6.734%). The method could be widely applied to not only the PIM process optimization but also various manufacturing process optimization problems.
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In mass production, injection molding plays a vital role in manufacturing various parts with complex settings. As perfluoroalkoxy alkane (PFA) injection‐molded products are widely used in immersion system, dimensional quality needs more attention. However, there is limited research on the dimension defects of PFA injection‐molded products. This study focused on the influence of mold design and process parameters on PFA part shrinkage, using mold flow simulation and orthogonal testing separately. Several gate locations and sizes were simulated to minimize shrinkage in mold design. Displacements varied with gate locations, and shrinkage decreased with larger gate sizes. The Taguchi method analyzed process parameters' impact on shrinkage. The results indicated that the injection rate had the most significant effect on the shrinkage of tube length, while melt temperature, holding pressure, and screw speed affected the shrinkage of tubes' outside diameter. Different flow directions exhibited variance in shrinkage. Using max–min normalization, the two shrinkage values reached 0.00972, whereas the smallest value obtained in the orthogonal experiment was 0.1545 in run 3. Thus, optimizing mold design and process parameters were two effective methods to reduce shrinkage. This study reduced shrinkage and improved the quality of PFA injection molding parts for semiconductor systems.
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This paper emphasizes the significant usage of natural fiber as a reinforced composite in the potential engineering field.
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This study explores the use of Additive Digital Molding, an Industry 5.0-driven approach, to improve business agility and sustainability within supply chains. By integrating digital reverse engineering, additive manufacturing, and plastic injection molding, this methodology streamlines product development processes, particularly for highly customized products in isolated environments. Additive Digital Molding provides cost-effective solutions for overcoming sourcing challenges, meeting customization requirements, and ensuring confidentiality. It also empowers SMEs and individuals to take greater control of their supply chains and foster entrepreneurial ventures. By enabling new business models such as direct manufacturing and home production, additive digital molding contributes to achieving the Sustainable Development Goals of the 2030 Agenda. This research highlights the transformative potential of Additive Digital Molding to drive innovation and sustainability across industries.
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Based on the grey relational analysis, this work proposes an effective approach for optimizing various injection moulding parameters on the wear behaviours of ultra-high molecular weight polyethylene (UHMWPE) with diverse performance characteristics. The injection moulding parameters are melting temperature, injection velocity and compaction time. The experimental data were used to calculate wear parameters, such as coefficient of friction, wear rate and hardness. Thirty runs were carried out using the response surface design to determine the optimal factor level condition. The graph and the response table in each level of the parameters are generated with help of grey relational grade. In addition to that, bovine serum is taken, which acts as a lubricant, and the sample hardness is tested. The results showed that there is an impact on the wear behaviour due to the contact load and melt temperature of UHMWPE. According to the grey relational grade, level 2 of injection moulding parameters has a greater effect than levels 1 and 3. With the help of a scanning electron microscope, the worn-out morphologies of samples were studied. Plastic deformation, ploughing, scratching, ironing and fatigue wear are the major wear processes of our study.
Chapter
Today, Korea is facing a time when it is essential to develop new manufacturing technologies and strategies to lead new changes, such as smart factories and manufacturing innovation 3.0, and achieve continuous development of the domestic manufacturing industry. Therefore, many manufacturing companies are promoting process automation using collaborative robots (co-bot) to respond to the paradigm of multi-item, small-volume production. The emergence of co-bots improves the space utilization of production facilities and opens up the possibility of introducing robots without modifying the existing production line. This study aims to conduct primary research on a robot that recognizes and acts on its environment using reinforcement learning to determine its work movements and perform tasks without specific instructions from human experts. In this study, we propose a collaborative robot control methodology using a deep reinforcement learning algorithm. In addition, for the practical application of the HRC system, which is challenging to apply to the production of a single product, the problem of data sharing between collaborative robots and workers based on a process model was addressed. The system proposed in this study is designed to optimize process variables through artificial intelligence-based data learning and is expected to contribute to product and process quality optimization of human-robot collaborative processes in the future.KeywordsCollaborative RobotArtificial IntelligenceDeep Reinforcement Learning
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This paper analyses the process variable data from injection molding processes to identify the key causal interactions between influential and dependent process variables in different product categories using variable lagged transfer entropy measure. Variable lagged transfer entropy measurements can be applied for data with variable time lags as in our case. We use variable lagged transfer entropy measurements to construct directed networks by calculating significant pairs of process variables for each production process (of each material). The directed network provides influential and dependent process variables for different materials. Furthermore, the constructed network for each material is used in a machine learning model for feature selection for each material category, where the edges are the input features and the material categories are the output labels with which to classify the material categories. Our analysis shows that the edge features of the inferred networks can classify different material categories and identify key features representing time-lagged causal interactions of the process variables. Additionally, the aggregated network based on the causal interactions that are significantly consistent in all the samples (production data of each product) irrespective of material categories. The aggregated network shows only 10 process variables are key influencers of all production processes.
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In this paper, poly(styrene-ethylene-butylene-styrene) (SEBS) triblock copolymer blends were used as the soft skin material and polypropylene (PP) was used as the hard core for co-injection molding. The effect of different SEBS thermoplastic elastomer skin materials on the filling state of the core layer of the co-injection sample was analyzed, and the co-injection molding was evaluated by the filling factor and shape distribution factor. When the core material flows at the matched skin interface, the core layer gradually changes from an uneven finger-like flow front to a smooth flow, and the shape distribution factor increases greatly. Both simulation and experimental results show that the shape distribution factor can correctly evaluate the filling effect of co-injection, and the corresponding samples have uniform skin-core interface, good compatibility and maximum skin thickness. According to the change of shearing rate, a dynamic viscosity ratio method was established to evaluate the co-injection process. When the dynamic viscosity ratio of the core/skin material is greater than and close to 1, the movement of the molecular chains on the interface matches, the flow front is smooth and the shape distribution factor is high. By changing the injection temperature of the material to adjust the dynamic viscosity ratio, the optimization of co-injection can also be achieved. The dynamic viscosity ratio method can be used to guide the flow forming and interface interaction analysis of heterogeneous materials.
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Shrinkage occurs In all polymers, being extremely dependent on processing conditions. The utilization of shrinkage data allows designers to accurately predict the final part dimensions. A numerical prediction of a part shrinkage can be made using simulation packages available commercially. However, this shrinkage is highly dependent on the non-linear material behavior and, thus, its estimation involves significant simplifications. On the other hand, artificial neural networks, ANN, can model highly non-linear systems; thus, it is expected that they can predict a part shrinkage effectively. In this study, a neural network architecture was developed to predict the shrinkage of an iPP injection molded plaque after changing four processing conditions: melt and mold temperatures, holding pressure and flow rate. The experiments were defined through the use of the design of experiments. A simulation code, Moldflow®, was used to establish the processing window; its shrinkage predictions were compared with experimental, neural network and statistical results. It was observed that the ANN had the best performance in the shrinkage prediction, even using limited experimental data, confirming its great capacity to model non-linear systems.
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Nondestructive online monitoring of injection molding processes is of great importance. However, almost all prior research has focused on monitoring polymers in molds and damaging the molds. Injection molding machines are the most important type of equipment for producing polymeric products, and abundant information about actual polymer processing conditions can be obtained from data collected from operating machines. In this paper, we propose a nondestructive online method for monitoring injection molding processes by collecting and analyzing signals from injection molding machines. Electrical sensors installed in the injection molding machine, not in the mold, are used to collect physical signals. A multimedia timer technique and a multithread method are adopted for real-time large-capacity data collection. An algorithm automatically identifies the different stages of the molding process for signal analysis. Moreover, ultrasonic monitoring technology is integrated to measure the cavity pressures. Experimental results show that our nondestructive method can continuously monitor the injection molding process in real time and automatically identify the different stages of the molding process. The packing parameters, including the filling-to-packing switchover point and the packing time, can be optimized based on these data. Furthermore, the ultrasonic reflection coefficient and the actual cavity pressure have similar trends, and our technique for measuring the cavity pressure is accurate and effective.
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Accurately and rapidly predicting the shrink mold shape of direct injection-expanded foam molding is an important and difficult task. This molding method is widely used by sports shoe sole manufacturers to create shock-resistant materials. Modifying the shrink mold shape using the numerical optimization method is crucial to rapidly obtaining the correct shrink mold size. This study uses a series of rectangle specimens to identify the relationship between the thermal heating of molding and the expansion ratio of ethylene vinyl acetate foam material and then uses this thermal expansion ratio to simulate expansion behavior. The experiments in this study also use the actual shoe sole type, which has the original three-dimensional (3D) shape, and use the proposed simulation method to obtain the simulation expansion shape. This study also develops an optimization algorithm based on 3D registration and the Newton–Raphson method to obtain the shrink mold shape. We also manufactured the shrink mold and obtained the shoe sole product to compare any discrepancies between the product and the original 3D shape. The results of this method meet the requirements of the shoe sole factory (i.e., achieve a difference of less than 3 mm).
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The most significant process parameters affecting dimensional shrinkage in transverse and longitudinal directions of molded parts in Plastic Injection Molding (PIM) process are injection velocity, mold temperature, melt temperature and packing pressure. In the present work, ANN model was developed for forward and reverse mapping prediction. In forward mapping PIM process parameters are expressed as the input parameters to predict dimensional shrinkage, whereas in reverse mapping, attempts were made to predict an appropriate set of process parameters required for arriving at the required dimensional shrinkage. The trained network with one thousand input-output data randomly generated from regression equations reported by earlier researchers resulted in minimum mean squared error. The performance of developed model was compared with experimental values for ten different test cases. The results show that ANN model with both forward and reverse mapping is capable of prediction with an error level of less than ten percent.
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This paper presents a hybrid of artificial neural networks and artificial bee colony algorithm to optimize the process parameters in injection molding with the aim of minimize warpage of plastic products. A feedforward neural network is employed to obtain a mathematical relationship between the process parameters and the optimization goal. Artificial bee colony algorithm is used to find the optimal set of process parameters values that would result in the optimal solution. An experimental case is presented by coupling Moldflow simulations along with the intelligent schemes in order to validate the proposed approach. Melt temperature, mold temperature, packing pressure, packing time, and cooling time are considered as the design variables. Results revealed the proposed approach can efficiently support engineers to determine the optimal process parameters and achieve competitive advantages in terms of quality and costs.
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Optimization of injection molding process serves for finding ideal conditions during production of plastic parts and observing their final dimensions, shapes and properties. It is possible to determine the appropriate injection pressure, velocity, value and time of packing pressure, etc. by optimization. The paper is dealing with description of Moldflow Plastics Xpert (MPX) system and its usage in optimization of injection molding process on real part during its production. MPX is integrated with injection molding machines to optimize their operation and to monitor and control the manufacturing process. MPX addresses common manufacturing issues such as machine set-up, process optimization and production part quality monitoring and control. Results generated by MPA and MPI products can be input directly into MPX product to reduce machine set-up time and enhance the efficiency of the injection molding machine.
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Currently, many industries are trending towards producing products exhibit such properties as small thickness, lightweight, small dimensions, and environmental friendliness. In this project, flat or shallow thin-walled parts were designed to compare the advantages and disadvantages of lignocellulosic polymer composites (PP + 50 wt% wood) in terms of processability. This study focused on the filling, in-cavity residual stresses and warpage parameters associated with both types of thin-walled moulded parts. Thin-walled parts 0.7 mm in thickness were suitably moulded using lignocellulosic composite materials to determine the effects of filling. The analysis showed, the shallow thin-walled part is preferable in moulding lignocellulosic polymer composite material due to the low residual stress and warpage measured. The results also indicate that the shallow thin-walled part is structurally rigid, such that it can be used in applications involving small shell parts, and can be processed more economically using less material than the flat thin-walled part.
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The plastic injection molding (PIM) process parameters have been investigated for manufacturing a brake booster valve body. The optimal PIM process parameters is determined with the application of computer-aided engineering integrating with the Taguchi method to improve the compressive property of the valve body. The parameters considered for optimization are the following: number of gates, gate size, molding temperature, resin temperature, switch over by volume filled, switch over by injection pressure, and curing time. An orthogonal array of L18 is created for the statistical design of experiments based on the Taguchi method. Then, Mold-Flow analyses are performed by using the designed process parameters based on the L18 orthogonal array. The signal-to-noise (S/N) ratio and the analysis of variance (ANOVA) are used to find the optimal PIM process parameters and to figure out the impact of the viscosity of resin, curing percentage, and compressive strength on a brake booster valve body. When compared with the average compression strength out of the 18 design experiments, the compression strength of the valve body produced using the optimal PIM process parameters showed a nearly 12% improvement.
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a b s t r a c t Reducing volumetric shrinkages and warpage during the injection molding process is a challenging prob-lem in the production of molded thin-walled parts. In this study, the injection molding of shallow, thin-walled parts (thickness 0.7 mm), composed of lignocellulosic polymer composites (polypropylene (PP) + 50 wt% wood), was simulated. The volumetric shrinkages and warpage in the thin-walled parts were evaluated under different process conditions, with varying post-filling parameters, such as mold temperature, cooling time, packing pressure and packing time. The analysis showed that the cooling time and packing time had less of an effect on the shrinkage and warpage; nevertheless the optimal levels for both parameters are required in the molding process for the thin-walled part to achieve the best results. The volumetric shrinkage was lower near the gate than at the end-of-fill location along the flow path. The results also showed that the volumetric shrinkage correlates with the warpage measured on the molded part. The optimum parameters ranges is 40–45 °C for the mold temperature; 20–30 s for cooling time; 0.85 from injection pressure (P inject) for packing pressure; and 15–20 s for the packing time to achieve the best results with the least amount of volumetric shrinkage and warpage.
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Powder injection molding (PIM) of ceria-stabilized, zirconia-toughened mullite composites were investigated in the present article with the goal of obtaining performance enhancement in complex geometries for energy and transportation applications. A powder-polymer mixture (feedstock) was developed and characterized to determine its suitability for fabricating complex components using the PIM process. Test specimens were injection molded and subsequently debound and sintered. The sintered properties indicated suitable properties for engine component applications used in unmanned aerial vehicles (UAVs). The measured feedstock properties were used in computer simulations to assess the mold-filling behavior for a miniature turbine stator. The results from the measurements of rheological and thermal properties of the feedstock combined with the sintered properties of the ceria-stabilized, zirconia-toughened mullite strongly indicate the potential for enhancing the performance of complex geometries used in demanding operating conditions in UAV engines.
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Aluminum nitride has been favored for applications in manufacturing substrates for heat sinks due to its elevated temperature operability, high thermal conductivity, and low thermal expansion coefficient. Powder injection molding is a high-volume manufacturing technique that can translate these useful material properties into complex shapes. In order to design and fabricate components from aluminum nitride, it is important to know the injection-molding behavior at different powder–binder compositions. However, the lack of a materials database for design and simulation at different powder–polymer compositions is a significant barrier. In this paper, a database of rheological and thermal properties for aluminum nitride–polymer mixtures at various volume fractions of powder was compiled from experimental measurements. This database was used to carry out mold-filling simulations to understand the effects of powder content on the process parameters and defect evolution during the injection-molding process. The experimental techniques and simulation tools can be used to design new materials, select component geometry attributes, and optimize process parameters while eliminating expensive and time-consuming trial-and-error practices prevalent in the area of powder injection molding.
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Silicon nitride has been the favored material for manufacturing high-efficiency engine components for transportation due to its high temperature stability, good wear resistance, excellent corrosion resistance, thermal shock resistance, and low density. The use of silicon nitride in engine components greatly depends on the ability to fabricate near net-shape components economically. The absence of a material database for design and simulation has further restricted the engineering community in developing parts from silicon nitride. In this paper, the design and manufacturability of silicon nitride engine rotors for unmanned aerial vehicles by the injection molding process are discussed. The feedstock material property data obtained from experiments were used to simulate the flow of the material during injection molding. The areas susceptible to the formation of defects during the injection molding process of the engine component were identified from the simulations. A test sample was successfully injection molded using the feedstock and sintered to 99% density without formation of significant observable defects.
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Purpose: The aim of the work was the optimization of injection molded product warpage by using an integrated environment. Design/methodology/approach: The approach implemented took advantages of the Finite Element (FE) Analysis to simulate component fabrication and investigate the main causes of defects. A FE model was initially designed and then reinforced by integrating Artificial Neural Network to predict main filling and packing results and Particle Swarm Approach to optimize injection molding process parameters automatically. Findings: This research has confirmed that the evaluation of the FE simulation results through the Artificial Neural Network system was an efficient method for the assessment of the influence of process parameter variation on part manufacturability, suggesting possible adjustments to improve part quality. Research limitations/implications: Future researches will be addressed to the extension of analysis to large thin components and different classes of materials with the aim to improve the proposed approach. Originality/value: The originality of the work was related to the possibility of analyzing component fabrication at the design stage and use results in the manufacturing stage. In this way, design, fabrication and process control were strictly links.
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In this study warpage and shrinkage as defects in injection molding of plastic parts have been undertaken. MoldFlow software package has been used to simulate the molding experiments numerically. Plastic part used is an automotive ventiduct grid. The process optimization to minimize the above defects is carried out by sequential simplex method. Process design parameters are mold temperature, melt temperature, pressure switch-over, pack/holding pressure, packing time, and coolant inlet temperature. The output parameters aside from warpage and shrinkage consist of part weight, residual stresses, cycle time, and maximum bulk temperature. Results are correlated and interpreted with recommendations to be considered in such processes.
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The rheological behavior of alumina molding feedstocks containing polyethylene glycol (PEG), polyvinylbutyral (PVB) and stearic acid (SA) and having different powder loads were analyzed using a capillary rheometer. Some of the feedstocks showed a pseudoplastic behavior of n < 0, which can lead to the appearance of weld lines on molded parts. Their viscosity also displayed a strong dependence on the shear rate. The slip phenomenon, which can cause an unsteady front flow, was also observed. The results indicate that the feedstock containing a lower powder load displayed the best rheological behavior. The 55 vol. % powder loaded feedstock presented the best rheological behavior, thus appearing to be more suitable than the formulation containing a vol. 59% powder load, which attained viscosities exceeding 103 Pa.s at low shear rates, indicating its unsuitability for injection molding.
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An assistance system for tuning a operation condition of injection molding machines based on a constraint processing method is proposed in this paper. First one is based on a simulation. We derive constraints of machine condition from the simulation results. Second one is knowledge based constraints processing that is derived from operators' experience. These constraints help operators search optimal conditions. The last method is a graphical browser using the Hierarchical Operation Record Graph(HORG). This graph helps the operator recognize a tuning process in multi-dimensional space and find out an effective method for solving molding problems
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The effect of processing parameters on the visual quality of injection-molded parts was investigated experimentally with three materials. The quality characteristics measured were shrinkage, weight, and visibility of weld lines and sink marks. Two kinds of test parts were made for investigating these quality characteristics. In experiments, the Taguchi parameter-design method was used. The results show that this method is very practical in optimizing the quality of injection-molded parts. Additionally, the parameter effects and their intensity can easily be evaluated. This is important, especially when there are many quality characteristics in the product to optimize. By this method, the number of experiments could be reduced, which could lead to cost-savings.
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Shrinkage behavior plays a critical role in determining the final shape and dimensions of an injection-molded part. In this paper, the CAE and Taguchi DOE technique were combined to investigate the influence of factors on the shrinkage behavior of the injection molded part and optimize the process conditions, the part quality was improved under the optimum process conditions obviously.
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A numerical formulation is presented for simulating the injection-molding filling of thin cavities, together with the delivery system, in three dimensions. The modelling is based on generalized Hele-Shaw flow for an inelastic, non-Newtonian fluid under non-isothermal conditions, which has been previously shown to be satisfactory for simulating the polymer melt flow in the cavities. A hybrid numerical scheme is employed in which the injection-molded part is described by two-dimensional triangular elements, provided that the cavity thickness is relatively thin, and the gapwise and time derivatives are expressed in terms of finite differences. The elements are flat, but can have any orientation in 3-D space to approximate the surfaces of the molded part. A triangular element is further divided into three sub-areas by joining the centroid of the element to the mid-point of its three edges. The control volume associated with any vertex node is then defined as the sum of all such sub-areas containing that node multiplied by each respective thickness. The numerical calculation of the flow field (or the pressure field) is based on the conservation of mass in each control volume which, at any given instant, can be either empty, partially filled, or totally filled with polymer melt. The melt-front location is defined by the partially filled control volumes which are allowed to advance in the calculation such that one partially filled control volume gets filled during each properly chosen time step with all of its adjacent empty control volumes then becoming new melt-front control volumes. The pressure and temperature are calculated at each time step, with the resulting pressure field determining the flow direction which, in turn, determines which partially-filled control volume should get filled during the following time step. One-dimensional flow segments, such as circular or non-circular tubes, can also be employed to represent the delivery system. This one-dimensional flow is coupled with the cavity filling in order to form a complete simulation of the mold filling. Comparisons with experiment have been made for a rectangular cavity with three inserts. The results show good agreement in terms of pressure traces and weldline locations. Another complex 3-D injection molded part has also been modelled to demonstrate the capability of the analysis.
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Plastic is fashionable raw material on manufacturing environment because of its low cost, ease of fabrication and relative durability. High production output rate with close tolerances is achieved by plastic injection machines and steel/alloy molds. However both the machinery and the molds are expensive equipments and pay back themselves in mass production. So, efficient usage is crucial where high demand fluctuations and increasing customization is a reality which forces engineers to lessen inefficiencies, one of which is changeover times in our focus. This paper proposes Taguchi experimental design to the trial runs phase of a changeover operation to get the parameters that gives the first correct product. Fewer trials lead less time needed to start mass production and also less waste of material.
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This Paper Presents the Optimization of Injection Molding Conditions to Minimize the Warpage and Volumetric Shrinkage Using Design of Experiments and Taguchi Optimization Method. Considering the Process Parameters such as Injection Time, Packing Pressure, Packing Pressure Time, and Cooling Time, a Series of Mold Analysis are Performed. Orthogonal Arrays of Taguchi, the Signal-to-Noise(S/N) and Analysis of Variance (ANOVA) are Utilized to Determine the Optimization Parameter Levels and to Find out Principal Processing Parameters on Warpage and Volumetric Shrinkage. from the Results it is Clear that Warpage and Volumetric Shrinkage are Reduced. also, the Dominant Parameters were Cooling Time and Packing Time for Warpage, on the other Hand, the most Important Factor for Shrinkage was Injection Time. from this, it can be Concluded that Taguchi Method is very Suitable to Solve the Warpage and Volumetric Shrinkage Problems in Injection Molding Parts.
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In the current work, the effect of ultra-high molecular weight polyethylene (UHMWPE) and temperature field on the unique double skin-core orientated structure and mechanical properties of high-density polyethylene (HDPE) parts molded by multi-melt multi-injection molding (MMMIM) were investigated using a variety of characterization techniques including rheological experiments, scanning electron microscopy (SEM), synchrotron small-angle X-ray scattering (SAXS), differential scanning calorimetry (DSC) and tensile testing. The SEM results revealed that a distinct double skin-core orientated structure was formed in samples molded via MMMIM. That is, compact lamellar together with typical shish-kebab structures was formed from the skin to the sub-skin, and large area of oriented lamellar was formed again near the core layer due to the significantly improved relaxation time of the UHMWPE/HDPE blend and intensive shear flow resulted from the secondary melt penetration process. Additionally, with increase temperature of the second melt, the oriented lamellar near the core layer tended to develop into irregularly-arranged lamellar and the double skin-core orientated structure weakened gradually. These results were further authenticated by SAXS. Results of tensile testing indicated that with reduced temperature of second melt, samples with higher tensile strength and modulus were obtained.
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This study proposes an integrated optimization system to find out the optimal parameter settings of multi-input multi-output (MIMO) plastic injection molding (PIM) process. The system is divided into two stages. In the first stage, the Taguchi method and analysis of variance (ANOVA) are employed to perform the experimental work, calculate the signal-to-noise (S/N) ratio, and determine the initial process parameters. The back-propagation neural network (BPNN) is employed to construct an S/N ratio predictor and a quality predictor. The S/N ratio predictor and genetic algorithms (GA) are integrated to search for the first optimal parameter combination. The purpose of this stage is to reduce the process variance. In the second stage, the quality predictor is combined with particle swarm optimization (PSO) to find the final optimal parameters. The quality characteristics, product length and warpage, are dedicated to finding the optimal process parameters. After the numerical analysis, the optimal parameters can meet the lowest variance and the product quality requirements simultaneously. Experimental results show that the proposed optimization system can not only satisfy the quality specification but also improve stability of the PIM process.
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Warpage reduction is one of the important issues in plastic injection molding (PIM). In order to resolve this issue, there are mainly two ways to reduce warpage: One is to design the mold, and the other is to optimize the process parameters such as the mold temperature, the melt temperature, and so on. In this paper, the latter approach is employed. In particular, variable pressure profile approach is adopted for the warpage reduction. Besides the variable pressure profile, the melt temperature and the mold temperature are taken as the design variables. Also, short shot that the melt plastic is not filled into the cavity is one of the fatal defects in PIM. Unlike the literature, in this paper, the short shot is handled as the design constraint. PIM simulation is generally so costly and time consuming, and then the surrogate-based optimization technique is used. The radial basis function (RBF) network is used throughout sequential approximate optimization (SAO) procedure. Moldex3D is used for PIM simulation. In order to compare the effectiveness of the variable pressure profile, the traditional process parameter optimization considered in the literature is also carried out. Numerical results show that the variable pressure profile is one of the effective ways to warpage reduction compared to the traditional process parameter optimization.
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Powder injection molding (PIM) is useful to manufacture small, complex metal and ceramic components in high production volumes. Mixing nanoparticles (n) with microparticles (μ) has been previously identified in our research group as a promising approach to achieve high sintered density and low shrinkage in injection molded AlN. Sintering studies showed a liquid phase formation at ∼1500 °C in bimodal μ–n AlN samples, a temperature that is atleast 100 °C lower than typically reported values in the literature. Sintered parts of bimodal μ–n AlN mixtures exhibited comparable sintered density but lower shrinkage (∼14%) than corresponding monomodal μ-AlN (∼20%). These benefits in sintered attributes are a direct consequence of a significant increase in the packing density in powder–polymer mixtures with the addition of nanoparticles. However, there are few studies focused on understanding the effects of nanoparticle addition on the rheological and thermal properties of the bimodal feedstock. The present study combines experimental measurement feedstock properties with models for estimating properties over a range of powder content. The properties were subsequently used in mold-filling simulations to understand the effects of powder content on process parameters and defect evolution in PIM. These protocols and findings can be used to improve PIM design practices in material selection, component geometry attributes, and optimized process parameters.
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To solve the multi-objective optimization problem of injection molding ma-chine product's overall performance, firstly, the optimal design of injection molding ma-chine is studied and the design problem is formulated as a constrained multi-objective problem involving continuous and discrete design variables. Furthermore, with the K means of joint support vector clustering method to reduce the number of external stocks, a new multi-objective optimization algorithm KSVC-SPEA is proposed. Then, taking the multi-objective optimization of the overall performance of the HT160X1N high-speed in-jection molding machine as an example, the traditional linear weighting methods, Strength Pareto Evolutionary Algorithm (SPEA) and the KSCV-SPEA are applied. Case studies show that the KSVC-SPEA is able to effectively achieve the multi-objective optimization design of the overall performance of injection molding machine with a high efficiency.
Book
The groundwork for the fundamentals of polymer processing was laid out by Professor R. B. Bird, here at the University of Wisconsin-Madison, over 50 years ago. Almost half a century has past since the publication of Bird, Steward and Lightfoot's transport phenomena book. Transport Phenomena (1960) was followed by several books that specifically concentrate on polymer processing, such a the books by McKelvey (1962), Middleman (1977), Tadmor and Gogos (1979), and Agassant, Avenas, Sergent and Carreau (1991). These books have influenced generations of mechanical and chemical engineering students and practicing engineers. Much has changed in the plastics industry since the publication of McKelvey's 1962 Polymer Processing book. However, today as in 1962, the set-up and solution of processing problems is done using the fundamentals of transport phenomena.
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Thermoplastic polyurethane (TPU)/poly (butylene terephthalate) (PBT) blends with different ratios were prepared by extrusion and injection molding. The morphology, dynamic viscoelastic, capillary rheological, thermal and mechanical properties of the blends were studied. Results showed that there was good compatibility between TPU and PBT. The capillary rheological properties showed that the apparent viscosity decreased with the TPU content. DSC analysis indicated that with increasing TPU content the crystallization temperature (Tc), the melting point (Tm) and the percent crystallinity (Xc) decreased. Mechanical properties showed that the addition of TPU could lead to a remarkable increase, about 368.18%, in impact strength with a small reduction in tensile and flexural strength of TPU/PBT blends.
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Plastic injection molding is widely used for manufacturing a variety of parts. Molding conditions or process parameters play a decisive role that affects the quality and productivity of plastic products. This work reviews the state-of-the-art of the process parameter optimization for plastic injection molding. The characteristics, advantages, disadvantages, and scope of application of all of the common optimization approaches such as response surface model, Kriging model, artificial neural network, genetic algorithms, and hybrid approaches are addressed. In addition, two general frameworks for simulation-based optimization of injection molding process parameter, including direct optimization and metamodeling optimization, are proposed as recommended paradigms. Two case studies are illustrated in order to demonstrate the implementation of the suggested frameworks and to compare among these optimization methods. This work is intended as a contribution to facilitate the optimization of plastic injection molding process parameter.
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Family Mould Cavity Runner Layout Design (FMCRLD) is the most demanding and critical task in the early Conceptual Mould Layout Design (CMLD) phase. Traditional experience-dependent manual FCMRLD workflow causes long design lead time, non-optimum designs and human errors. However, no previous research can support FMCRLD automation and optimisation. The nature of FMCRLD is non-repetitive and generative. The complexity of FMCRLD optimisation involves solving a complex two-level combinatorial layout design optimisation problem. Inspired by the theory of evolutionary design in nature “Survival of the Fittest” and the biological genotype–phenotype mapping process of the generation of form in living systems, this research first proposes an innovative evolutionary FMCRLD approach using Genetic Algorithms (GA) and Mould Layout Design Grammars (MLDG) that can automate and optimise such generative and complex FMCRLD with its explorative and generative design process embodied in a stochastic evolutionary search. Based on this approach, an Intelligent Conceptual Mould Layout Design System (ICMLDS) prototype has been developed. The ICMLDS is a powerful intelligent design system as well as an interactive design-training system that can encourage and accelerate mould designers’ design alternative exploration, exploitation and optimisation for better design in less time. This research innovates the traditional manual FMCRLD workflow to eliminate costly human errors and boost the less-experienced mould designer’s ability and productivity in performing FCMRLD during the CMLD phase.
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Finite element method and Taguchi method are used to simulate the process of gasassisted injection molding (GAIM) to find out the optimal design condition of manufacturing a polystyrene product. In this study, the experimental design of Taguchi method is used to determine the process condition of the GAIM, while a software, M-Flow, is used in simulating the injection process. Eight parameters are discussed including material and process parameters for gas-assisted injection. By using the Taguchi method and analysis of variance, ANOVA, four parameters are found to have a great influence on the GAIM product. It is found that a slower gas injection speed, higher melt temperature, higher gas pressure, and longer gas packing time would yield lesser warpage.
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This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process. It combines an experimental design by Taguchi’s method, a process analysis by analysis of variance (ANOVA), a process modeling algorithm by artificial neural network (ANN), and a multi-objective parameter optimization algorithm by genetic algorithm (GA)-based lexicographic method. Local and global Pareto analyses show the trade-off between product quality and energy consumption. The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests, and at the same time, the product quality can meet the pre-determined requirements.
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In this paper a newly developed fuzzy supervisory control system for the injection molding process is presented. The system performs automatic tuning of the machine operating points and reduces the amount of human effort needed for a complete multi-objective optimization of the machine settings. The optimal tuning is facilitated by a priority policy based on the significance of defects. The experimental results obtained from the application of the proposed fuzzy control architecture to a real industrial environment were very encouraging.
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The objective of this study is to propose an intelligent methodology for efficiently optimizing the injection molding parameters when multiple constraints and multiple objectives are involved. Multiple objective functions reflecting the product quality, manufacturing cost and molding efficiency were constructed for the optimization model of injection molding parameters while multiple constraint functions reflecting the requirements of clients and the restrictions in the capacity of injection molding machines were established as well. A novel methodology integrating variable complexity methods (VCMs), constrained non-dominated sorted genetic algorithm (CNSGA), back propagation neural networks (BPNNs) and Moldflow analyses was put forward to locate the Pareto optimal soluti