Figure - available via license: Creative Commons Attribution 3.0 Unported
Content may be subject to copyright.
![General properties of HDPE 1000, PA6 and POM-C [12].](publication/344837420/figure/tbl1/AS:1021814159450112@1620630888012/General-properties-of-HDPE-1000-PA6-and-POM-C-12.png)
Source publication
![General properties of HDPE 1000, PA6 and POM-C [12].](publication/344837420/figure/tbl1/AS:1021814159450112@1620630888012/General-properties-of-HDPE-1000-PA6-and-POM-C-12_Q320.jpg)
Polymers become more and more attractive for automotive and aerospace industries due to their remarkable mechanical, thermal and electrical properties that make these materials suitable for many industrial applications. Machining of polymers is of a great interest among researchers and engineers due to the possibility of replacing expensive materia...
Similar publications
![Hole images from uncoated carbide drills [18].](publication/346969025/figure/fig3/AS:11431281346481756@1743643927968/Hole-images-from-uncoated-carbide-drills-18_Q320.jpg)
Multi-spindle drilling simultaneously produces multiple holes to save time and increase productivity. The assessment of hole quality is important in any drilling process and is influenced by characteristics of the cutting tool, drilling parameters and machine capacity. This study investigates the drilling performance of uncoated carbide, and coated...
Citations
... The results showed that POM-C produced the best surface quality, followed by HDPE 1000 and PA6, respectively. Interestingly, Tabacaru et al. [21] conducted the previous machining process for the same materials in order to model the surface roughness ( ) using the ANN method. The authors arrived at the same conclusion as the previous study [20], namely that POM-C has the best machinability, followed by HDPE 1000 and PA6. ...
This paper presents a study on the dry turning of polyoxymethylene copolymer POM-C. The effect of five factors (cutting speed, feed rate, depth of cut, nose radius, and main cutting edge angle) on machinability is evaluated using four output parameters: surface roughness, tangential force, cutting power, and material removal rate. To do so, the study relies on three approaches: (i) Pareto statistical analysis, (ii) multiple linear regression modeling, and (iii) optimization using the genetic algorithm. To conduct the investigation, mathematical models are developed using response surface methodology based on the Taguchi 𝐿16 orthogonal array. Theresults indicate that feed
rate, nose radius, and cutting edge angle significantly influence surface quality, while depth of cut, feed, and speed have a notable impact on other machinability parameters. The developed mathematical models have determination coefficients greater than or very close to 95%, making them very useful for the industry as they allow predicting response values based on the chosen cutting parameters. Finally, the optimization using the genetic algorithm proves to be promising and effective in determining the
optimal cutting parameters to maximize productivity while improving surface quality.
... However, for the evolution of the specific cutting effort (Ks), (Vc) is more dominant. Tabacaru et al. [6] conducted a study on predicting roughness during dry drilling of polymeric materials such as high-density polyethylene (HDPE 1000 grade), polyamide (PA-6 grade), and polyacetal (POM-C grade). Alateyah et al. [7] performed an experimental and analytical study investigation on the influence of cutting parameters on the turning of two different polymers types: high-density polyethylene (HDPE) and un-reinforced polyamide (PA-6). ...
... On the other hand, the increase in f and apse translates into an increase in the section of the chip; consequently, the volume of the chip increases, which contributes directly to the rise in temperature in the cutting zone. As polymer materials in general and our two materials studied in particular have very low thermal diffusion coefficients, the heat released by the cutting process is concentrated in the cutting zone and in turn contributes to the degradation of the surface condition obtained [6]. The graph of the main effects is presented in figure 2. It clearly appears that (f) and the nature of the machined material strongly affect the roughness (Ra). ...
This paper focuses on a comparative study of the machinability of two semi-crystalline polymers (POM-C) and (PA-6), during dry turning operations. The aim is to experimentally examine the impact of cutting parameters, namely cutting speed, feed, and depth of cut on surface roughness, cutting force, cutting power, and material removal rate. A series of experiments according to a Taguchi L18 plan was implemented. The ANOVA analysis revealed that the type of material a substantial effect on surface roughness, followed by feed, whereas cutting force and cutting power are more affected by depth of cut. Linear regression models with interactions proved effective in predicting the studied responses, and single-objective optimization using SA and GA methods were applied to optimize each response. The GRA and COPRAS methods coupled with weighting methods CRITIC, ROC, SWARA, and Entropy are used for multi-objective optimization of the considered responses. The results showed that the combination of the COPRAS method with the SWARA method provides a better compromise, which is of crucial interest for researchers in the field of optimization in polymer material machining.
... The authors of Tabacaru et al. (2020) proposed a neural model that is able to predict surface roughness not only with regard to cutting parameters, but also to the type of material when drilling polymeric materials in dry conditions -high-density polyethylene (class HDPE 1000), polyamide (class PA6) and polyacetal (class POM -C). The final conclusion from this study can be stated that POM-C has the best machinability of all materials studied, the second-best being HDPE 1000 and PA6 [18]. ...
... The authors of Tabacaru et al. (2020) proposed a neural model that is able to predict surface roughness not only with regard to cutting parameters, but also to the type of material when drilling polymeric materials in dry conditions -high-density polyethylene (class HDPE 1000), polyamide (class PA6) and polyacetal (class POM -C). The final conclusion from this study can be stated that POM-C has the best machinability of all materials studied, the second-best being HDPE 1000 and PA6 [18]. Gehlen et al. (2021) addressed the tribological behavior of POM-C subjected to sliding on a gray cast iron disk at different temperatures and the results clearly point to the fact that the pressure-velocity limit (PVL) of POM-C is strongly affected by temperature. ...
... Pre-draining coal seam gas with long boreholes is one of the effective measures to control coal seam gas in China [1][2][3][4]. At present, wet drilling and dry drilling are two of the methods mainly adopted for long boreholes in coal seams [5][6][7]. Wet drilling generates less dust powder but has problems with drilling jams, hole collapse, and sewage disposal [8,9]. Dry drilling, especially air directional drilling, has obvious advantages, such as short operation period, low cost, and good gas drainage effect, and, therefore, is widely used for gas drainage [10][11][12]. ...
High rate of dust generation and serious dust diffusion in dry directional drilling in soft and broken coal seams (SBCS) have long been critical problems in the mining process. To solve these problems, in this study, a dust hood was designed and applied to realize non-contact dust control in drilling holes. The optimal performance of the dust hood was achieved when different technical parameters, including the gap width between the dust hood and the drill pipe, the air-slot width of the sealing device, the slag discharge pressure, and the air curtain pressure were controlled at 2 mm, 0.2 mm, 0.3 MPa, and 0.5 MPa, respectively. As a result, the dust concentration was reduced from 540 mg/m³ to 15 mg/m³, with dust control efficiency reaching 97.2%. The in situ test results confirmed good dust control effects, as the dust control efficiency reached 98.3% after using the dust hood.
In the present era of artificial intelligence and machine learning, artificial neural networks (ANNs) have appeared as one of the potent tools in modeling the complex nonlinear relations between the input and output parameters in many of the machining processes, and helping the process engineers in predicting the tentative response values for given sets of input parameters. This paper conducts a systematic and content-wise analysis of a considerable number of research articles available in some of the reputed scholarly databases dealing with application of ANNs as effective predictive tools in three main machining operations (turning, milling and drilling) with an aim to extract the relevant information with respect to types of the ANN, their corresponding learning algorithms and activation (transfer) functions, optimal architecture, and statistical metrics employed to evaluate their prediction performance. It is revealed that the past researchers have maximally applied those ANN models in turning (42.07%), followed by milling (34.48%) and drilling operations (23.45%). In those machining operations, cutting speed, feed rate and depth of cut have been treated as the most important input parameters, and surface roughness as the predominant predicted response. Among different ANN models, feed-forward neural networks (94.44%) have been the most preferred choice among the researchers mainly due to their simple topology and availability of well-structured experimental datasets. On the other hand, Levenberg–Marquardt (58.3%), Sigmoid (31.6%) and mean squared error (47.2%) are identified as the most favored learning algorithm, activation function and statistical measure, respectively. This review paper would act as a ready reference to the process engineers in providing the optimal architecture of the ANNs, thus relieving them from additional computational effort. Finally, advantages and limitations of ANNs are summarized along with future research directions.
Polymers are increasingly gaining prominence in the economic market due to their superior performance compared to metallic materials. The reinforcement of polymers with glass fibers brings about a significant improvement in their physico-mechanical characteristics. This study examines the evaluation of machinability, modeling and optimization of cutting conditions during dry turning of the reinforced polymer polyoxymethylene (POM-C GF25%). For this purpose, four input parameters are considered: cutting speed (Vc), feed rate per revolution (f), depth of cut (ap) and tool nose radius (r). Six output parameters, including surface roughness (SR), cutting force components (Fx, Fy and Fz), energy consumption (Ec) and material removal rate (Q), are considered in this study. The first part of the experiments is dedicated to studying the influence of each input factor on the outputs using the one-factor-at-a-time (OFAT) method. The second part involves statistical analysis followed by modeling using analysis of variance (ANOVA) and response surface methodology (RSM) based on an orthogonal Taguchi L32 design. The developed models are validated through confirmation tests. Finally, a last part focuses on the multi-criteria optimization of cutting conditions using three methods: desirability function (DF), multi-attributive border approximation area comparison (MABAC) and gray relational analysis (GRA). The objective is to minimize parameters such as surface roughness (SR), cutting force (Fz) and energy consumption (Ec) while maximizing productivity (Q). The results of this research indicate that the employed methods yield highly satisfactory optimal results, which can be of interest to researchers in the field of machining composite polymers, particularly POM-C GF25%, as well as in the field of optimization.
