Centro de Investigación y Desarrollo de Medicamentos (CIDEM)

This study aims to describe a new approach to managing the reduction or elimination of cable end waste during the Bowden cable trimming operation in production lines since this waste represents thousands of meters of wasted cable per year, which is unsustainable from an economic and environmental point of view. This study has shown how the implementation of the DMAIC cycle (Define, Measure, Analyze, Improve, Control) in an automotive component industry can lead to a very significant reduction in material waste and, consequently, a reduction in the number of workers on certain assembly lines. Improvement actions were defined and implemented once the factors capable of eliminating and/or reducing cable trimming on the assembly lines had been identified. With the actions implemented, about the case study used for validation, an annual gain of approximately €89,289 has been achieved and a gain of €288,343 will be achieved by approximately 2026, after the elimination of trimming on five assembly lines and the reduction of trimming on eight assembly lines.

The current trend in machining with robotic arms involves leveraging Industry 4.0 technologies to propose solutions that reduce path deviation errors. This approach presents significant challenges alongside promising advancements, as well as a substantial increase in the cost of future industrial robotic cells, which is not always amortizable. As an alternative or complementary approach to this trend, methods encouraging the occasional use of Industry 4.0 devices for characterizing the behavior of the actual physical cell, calibration, or adjustment are proposed. One such method, called FlePFaM, predicts flatness errors in face milling operations using robotic arms. This is achieved by estimating tool path deviation errors through the integration of a simple model of the robot arm’s mechanics with the cutting forces vector of the process, thereby optimizing machining conditions. These conditions are determined through prior empirical estimations of mass, stiffness, and damping. The conducted tests enabled the selection of the most favorable combination of variables, such as the robot wrist configuration, the position and orientation of the workpiece, and the predominant milling orientation. This led to the identification of the configuration with the lowest absolute flatness error according to the model’s predictions. The results demonstrated a high degree of similarity—between 97% for the closest case and 57% for the farthest case—between simulated and experimental flatness error values. FlePFaM represents a significant step forward in adopting innovative robotic arm solutions for reliable and efficient production. FlePFaM includes dimensional flatness indicators that provide practical support for decision making.

This study presents an innovative approach to optimizing the grinding process of W18CR4V steel, a high-performance material used in reamer manufacturing, using advanced machine learning models and multi-objective optimization techniques. The novel combination of Deep Neural Networks with Genetic Algorithm (DNN-GA), K-Nearest Neighbors (KNN), Levenberg-Marquardt (LM), Decision Trees (DT), and Support Vector Machines (SVM) was employed to predict key process outcomes, such as surface roughness (Ra), maximum roughness height (Rz), and production time. The results reveal significant improvements, with Ra values ranging from 0.231 μm to 1.250 μm (up to 81.5% reduction) and Rz from 1.519 μm to 6.833 μm (up to 77.7% reduction). The hybrid DNN-GA model achieved R2 > 0.99, reducing prediction errors by 23–45% compared to traditional models. Optimization via the Desirability Function achieved Ra values around 0.341 μm and Rz around 2.3 μm, with production times ranging from 1181 to 1426 seconds. The innovative Multi-Objective Grey Wolf Optimization (MOGWO) provided Pareto-optimal solutions, minimizing Ra to 0.3 μm, Rz to 1.5 μm, and production times between 2000 and 3000 seconds, offering better balance between surface quality and machining efficiency. This work highlights the unique integration of machine learning models with optimization techniques to significantly enhance grinding performance and manufacturing efficiency in high-precision industries.

The use of the composite patch bonding repair technique was initially developed for the aeronautical sector and then extended to several industrial fields given its advantages over traditional riveting, bolting, and even welding methods. Most of the studies presented in this field have focused on the repair of aluminum structures by showing the advantage of using a carbon patch in improving their strength. However, few studies have focused on the repair of steel plates with high mechanical properties, which require a careful choice of the mechanical properties of the repair. This work is part of this context. The objective is to analyze, by the finite element method, the performance of a composite patch of different types bonded for the repair of a DH-36 type steel structure in the presence of a crack emanating from a notch. Several composite patch configurations, based on a square-shaped patch, were evaluated to increase the strength of the damaged plate while reducing the J-integral value at the crack tip. The effect of composite patch material, crack size, and number of plies were compared with respect to the J-integral value in the plate, von Mises stress in the adhesive, and principal stress in the patch. The results clearly show that the different configurations presented in this study can improve the strength of the plate while selecting high-performance fibers to ensure good stress transfer from the damaged area.

The automotive industry is of great importance to the global economy for all the jobs it generates, the materials it exploits, or the technical and technological development it drives. The control cables provide essential functions for any car, such as the opening of doors and windows, or activating the handbrake and accelerator. The process of assembling control cables involves numerous steps and the manufacture of various components, e.g., the spiral, inner and outer coating, spiral terminal, and terminals. This work deals with the injection process of control cable terminals. There is a need to separate the injection set into its constituent parts, namely the terminals and the feeding system (gate), which is carried out manually, potentially leading to health problems, e.g., tendonitis. This paper presents the development of an automated pneumatic system for the separation of control cable terminals from the feeding system. The novel pneumatic system addresses a significant gap in the automation of Zamak terminal injection by handling seven different terminal types under strict spatial limitations. The automated solution resulted in a 39% reduction in production time, enhancing the process efficiency. Moreover, by adopting the Design Science Research (DSR) methodology, the work contributes not only to industrial practice but also to the theoretical understanding of the process. This approach to automating a repetitive and ergonomically challenging task represents a step forward in the field of manufacturing technology that can be extended to other fabrication processes, leading to process improvements and competitiveness.

The performance of a novel hard solid lubricant coating, CrAlNAg, in the face milling operation of AISI 1045 medium carbon steel under the modes of roughing and finishing was investigated. Dry machining was carried out using CrAlN coated inserts with varying silver (Ag) contents ranging from 0 to 16 at.%. The objective was to evaluate the performance of the developed coatings under different machining conditions, which could potentially result in (a) a high material removal rate (rough machining) and (b) high surface finish and dimensional accuracy (finish machining). An in-depth analysis of the cutting forces in face milling was performed to assess the impact of the coatings under these machining conditions. During machining, the force components in the X, Y, and Z directions were measured using a cutting force dynamometer attached to the workpiece. The components of these forces concerning the tool edge were calculated using geometrical characteristics and mathematical formulations, enabling the identification of the true cutting forces and the most sensitive force components relative to the cutting parameters. Apart from cutting forces, chip temperature, tool wear, surface roughness, and chip characteristics were evaluated for different coating compositions under both machining conditions. Owing to superior coating-substrate adhesion and tribological characteristics, the CrAlNAg9 coating with around 8.6 at.% of Ag was found to significantly reduce dominant forces and chip temperature under both machining conditions. Furthermore, the same coating exhibited remarkable resistance to flank wear compared to other compositions of CrAlNAg coatings.

The decline in cognitive function associated with aging significantly impacts the well-being of elderly individuals and their families. This decline is a major recognized risk factor for neurodegenerative diseases, notably Alzheimer’s disease. Animal models of aging provide a platform for evaluating drugs concerning aspects like memory and oxidative stress. JM-20 has demonstrated protective effects on short-term memory acquisition and consolidation, along with antioxidant properties and modulation of Acetylcholinesterase activity. This study assesses the potential protective JM-20 against cognitive decline and age-related memory loss. For the study, aged mice exhibiting aging-associated damage were initially selected. Experimental groups were then formed, and the effect of 8 mg/kg of JM-20 was evaluated for 40 days on aging-related behavior, such as spatial memory, novelty recognition memory, ambulatory activity, and anxiety. Subsequently, animals were sacrificed, and the hippocampal region was extracted for redox studies and to assess acetylcholinesterase activity. Results indicated that JM-20 at 8 mg/kg reversed damage to spatial working and reference memory, exhibiting performance comparable to untreated young adult animals. Furthermore, JM-20 preserved the enzymatic activity of superoxide dismutase, catalase, and total sulfhydryl levels in age-related cognitive impairment in mice, indicating a potent protective effect against oxidative events at the brain level. However, only young, healthy animals showed decreased acetylcholinesterase enzyme activity. These findings provide preclinical pharmacological evidence supporting the neuroprotective activity of JM-20, positioning it as a promising therapeutic candidate for treating memory disorders associated with aging.

Adhesive bonding has been replacing traditional joining methods such as welding, bolting, and riveting in the design of mechanical structures in the automotive, aerospace and aeronautic industries. This joining method has several advantages over traditional methods such as ease of manufacture, lower costs, ease of joining different materials, higher fatigue resistance, and high corrosion resistance. Although tubular adhesive joints have varying applications, such as in truss structures and vehicles, machine axles, and piping, different joint configurations exist, such as rod-tube joints (RTJ), which are not conveniently addressed in the literature. This work compares the tensile performance of adhesively bonded RTJ between aluminium alloy components (AW6082-T651), considering the variation of the main geometric parameters: overlap length ( L O ), tube thickness ( t S ), rod diameter ( d ), adhesive fillet angle ( f ), and type of adhesive. The Taguchi’s method was employed in the elaboration of the applied design of experiments (DoE). To compare the RTJ behaviour, a numerical analysis was carried out through finite element analysis (FEA) and cohesive zone modelling (CZM). Peel ( σ y ) and shear ( τ xy ) stresses in the adhesive layer were initially obtained by applying purely elastic models. CZM modelling made possible to obtain the damage evolution in the adhesive layer, the maximum load ( P m ) and dissipated energy ( U ) at P m of the adhesive joints. As a result of applying the Taguchi method, the adhesive joint that showed the best overall performance used the adhesive Araldite ® AV138, L O = 40 mm, d = 20, and t S = 3 mm.

A multilayer film, composed by ZrN‒Ag (20 nm) and Mo‒S‒N (10 nm) layers, combining the intrinsic lubricant characteristics of each layer was deposited using DC magnetron sputtering system, to promote lubrication in a wide-range of temperatures. The results showed that the ZrN‒Ag/Mo‒S‒N multilayer film exhibited a sharp interface between the different layers. A face-centered cubic (fcc) dual-phases of ZrN and Ag co-existed in the ZrN‒Ag layers, whilst the Mo‒S‒N layers displayed a mixture of hexagonal close-packed MoS2 (hcp-MoS2) nano-particles and an amorphous phase. The multilayer film exhibited excellent room temperature (RT) triblogical behavior, as compared to the individual monolayer film, due to the combination of a relative high hardness with the low friction properties of both layers. The reorientation of MoS2 parallel to the sliding direction also contributed to the enhanced anti-frictional performance at RT. At 400 ℃, the reorientation of MoS2 as well as the formation of MoO3 phase were responsible for the lubrication, whilst the hard t-ZrO2 phase promoted abrasion and, consequently, led to increasing wear rate. At 600 ℃, the Ag2MoO4 double-metal oxide was the responsible for the low friction and wear-resistance; furthermore, the observed transformation from t-ZrO2 to m-ZrO2, could also have contributed to the better tribological performance.

The decline in cognitive function associated with aging significantly impacts the well-being of elderly individuals and their families. This decline is a major recognized risk factor for neurodegenerative diseases, notably Alzheimer's disease. Animal models of aging provide a platform for evaluating drugs concerning aspects like memory and oxidative stress. JM-20 has demonstrated protective effects on short-term memory acquisition and consolidation, along with antioxidant properties and modulation of Acetylcholinesterase activity. This study assesses the potential protective JM-20 against cognitive decline and age-related memory loss. For the study, aged mice exhibiting aging-associated damage were initially selected. Experimental groups were then formed, and the effect of 8 mg/kg of JM-20 was evaluated for 40 days on aging-related behavior, such as spatial memory, novelty recognition memory, ambulatory activity, and anxiety. Subsequently, animals were sacrificed, and the hippocampal region was extracted for redox studies and to assess acetylcholinesterase activity. Results indicated that JM-20 at 8 mg/kg reversed damage to spatial working and reference memory, exhibiting performance comparable to untreated young adult animals. Furthermore, JM-20 preserved the enzymatic activity of superoxide dismutase, catalase, and total sulfhydryl levels in age-related cognitive impairment in mice, indicating a potent protective effect against oxidative events at the brain level. However, only young, healthy animals showed decreased acetylcholinesterase enzyme activity. These findings provide preclinical pharmacological evidence supporting the neuroprotective activity of JM-20, positioning it as a promising therapeutic candidate for treating memory disorders associated with aging.

Composite materials have become indispensable in a multitude of industries, such as aerospace, automotive, construction, sports equipment, and electronics [...]

Logistics and the supply chain are areas of great importance within organizations. Due to planning gaps, an increase in extra and unnecessary transport costs is usually observed in several companies due to their commercial commitments and need to comply with the delivery time and the batch quantity of products, leading to a negative economic impact. Thus, the objective of this work was to adjust an optimization model to maximize the shipments usually carried out by the companies. To validate the model, an automotive components manufacturer was selected, allowing us to apply the model to a real case study and evaluate the advantages and drawbacks of this tool. It was found that the company to validate the model exports most of its products, and most pallets sent are not fully optimized, generating excessive expense for the company in terms of urgent transport. To solve this problem, two mathematical optimization models were used for the company’s current reality, optimizing the placement of boxes per pallet and customer. With the use of the new tool, it was possible to determine that five pallets should be sent to the customer weekly, which correspond to their needs, and that have the appropriate configurations so that the pallet is sent completely.

Build-up-edge (BUE), high-temperature machining and tool wear (TW) are some of the problems associated with difficult-to-machine materials for high-temperature applications, contributing significantly to high-cost manufacturing and poor tool life (TL) management. A detailed review of non-traditional machining processes that ease the machinability of INCONEL®, decrease manufacturing costs and suppress assembly complications is thus of paramount significance. Progress taken within the field of INCONEL® non-conventional processes from 2016 to 2023, the most recent solutions found in the industry, and the prospects from researchers have been analysed and presented. In ensuing research, it was quickly noticeable that some techniques are yet to be intensely exploited. Non-conventional INCONEL® machining processes have characteristics that can effectively increase the mechanical properties of the produced components without tool-workpiece contact, posing significant advantages over traditional manufacturing.

The use of coatings on cutting tools offers several advantages from the point of view of wear resistance. A recent technique with great coating deposition potential is PVD HiPIMS. TiAlN-based coatings have good resistance to oxidation due to the oxide layer that is formed on their surface. However, by adding doping elements such as Vanadium, it is expected that the wear resistance will be improved, as well as its adhesion to the substrate surface. INCONEL® 718 is a nickel superalloy with superior mechanical properties, which makes it a difficult-to-machine material. Milling, due to its flexibility, is the most suitable technique for machining this alloy. Based on this, in this work, the influence of milling parameters, such as cutting speed (Vc), feed per tooth (fz), and cutting length (Lcut), on the surface integrity and wear resistance of TiAlVN-coated tools in the milling of INCONEL® 718 was evaluated. The cutting length has a great influence on the process, with the main wear mechanisms being material adhesion, abrasion, and coating delamination. Furthermore, it was noted that delamination occurred due to low adhesion of the film to the substrate, as well as low resistance to crack propagation. It was also observed that using a higher cutting speed resulted in increased wear. Moreover, in general, by increasing the milling parameters, machined surface roughness also increased.

Machining INCONEL® presents significant challenges in predicting its behaviour, and a comprehensive experimental assessment of its machinability is costly and unsustainable. Design of Experiments (DOE) can be conducted non-destructively through Finite Element Analysis (FEA). However, it is crucial to ascertain whether numerical and constitutive models can accurately predict INCONEL® machining. Therefore, a comprehensive review of FEA machining strategies is presented to systematically summarise and analyse the advancements in INCONEL® milling, turning, and drilling simulations through FEA from 2013 to 2023. Additionally, non-conventional manufacturing simulations are addressed. This review highlights the most recent modelling digital solutions, prospects, and limitations that researchers have proposed when tackling INCONEL® FEA machining. The genesis of this paper is owed to articles and books from diverse sources. Conducting simulations of INCONEL® machining through FEA can significantly enhance experimental analyses with the proper choice of damage and failure criteria. This approach not only enables a more precise calibration of parameters but also improves temperature (T) prediction during the machining process, accurate Tool Wear (TW) quantity and typology forecasts, and accurate surface quality assessment by evaluating Surface Roughness (SR) and the surface stress state. Additionally, it aids in making informed choices regarding the potential use of tool coatings.