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Resistance spot-welded joints are crucial parts in contemporary manufacturing technology due to their ubiquitous use in the automobile industry. The necessity of improving manufacturing efficiency and quality at an affordable cost requires deep knowledge of the resistance spot welding (RSW) process and the development of artificial neural network (ANN)- and machine learning (ML)-based modelling techniques, apt for providing essential tools for design, planning, and incorporation in the welding process. Tensile shear force and nugget diameter are the most crucial outputs for evaluating the quality of a resistance spot-welded specimen. This study uses ML and ANN models to predict shear force and nugget diameter responses to RSW parameters. The RSW analysis was executed on similar and dissimilar AISI 304 and grade 2 titanium alloy joints with equal and unequal thicknesses. The input parameters included welding current, pressure, welding duration, squeezing time, holding time, pulse welding, and sheet thickness. Linear regression, Decision tree, Support vector machine (SVM), Random forest (RF), Gradient-boosting, CatBoost, K-Nearest Neighbour (KNN), Ridge, Lasso, and ElasticNet machine learning algorithms, along with two different structures of Multilayer Perceptron, were utilized for studying the impact of the RSW parameters on the shear force and nugget diameter. Different validation metrics were applied to assess each model's quality. Two equations were developed to determine the shear force and nugget diameter based on the investigation parameters. The current research also presents a prediction of the Relative Importance (RI) of RSW factors. Shear force and nugget diameter predictions were examined using SHapley (SHAP) Additive Explanations for the first time in the RSW field. Trainbr as the training function and Logsig as the transfer function delivered the best ANN model for predicting shear force in a one-output structure. Trainrp with Tansig made the most accurate predictions for nugget diameter in a one-output structure and for shear force and diameter in a two-output structure. Depending on validation metrics, the Random forest model outperformed the other ML algorithms in predicting shear force or nugget diameter in a one-output model, while the Decision tree model gave the best prediction using a two-output structure. Linear regression made the worst ML predictions for shear force, while ElasticNet made the worst nugget diameter forecasts in a one-output model. However, in two-output models, Lasso made the worst predictions. [ABSTRACT FROM AUTHOR]

The main objective of this research is to conduct an experimental investigation of the bimetallic material formed by 316LSI stainless steel and S275JR structural steel, produced via hybrid wire arc additive manufacturing technology with cool metal transfer welding and machining, and with the objective of being able to reduce the industrial cost of certain requirements for one of the materials. A methodological investigation has been carried out starting with welding beads of 316LSI on S275JR plates, followed by overlapping five beads and conducting final experiments with several vertical layers, with or without intermediate face milling. The results achieved optimal bead conditions for wire speeds of 4 m/min and 5 m/min at a travel speed of 400 mm/min. Overlap experiments show that the best deposition results are obtained with an overlap equal to or greater than 28%. Cooling time does not significantly influence the final geometry of the coatings. Regarding metallographic analysis, the filler material presents an austenitic columnar structure. In the base material, a bainitic structure with inferred grain refinement was detected in the heat-affected zone. An increase in hardness is observed in the heat-affected zone. In the results obtained from the tensile tests of the bimetallic material, an increase in mechanical strength and yield strength is observed in the tested specimens. [ABSTRACT FROM AUTHOR]

The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness of the welded joints. In light of this, the present study sought to determine how the aforementioned welding quality outputs of 0.5 and 1 mm thick austenitic stainless steel AISI 304 were affected by RSW parameters, such as welding current, welding time, pressure, holding time, squeezing time, and pulse welding. In order to guarantee precise evaluation and experimental analysis, it is essential that they are supported by a numerical model using an intelligent model. The primary objective of this research is to develop and enhance an intelligent model employing artificial neural network (ANN) models. This model aims to provide deeper knowledge of how the RSW parameters affect the quality of optimum joint behavior. The proposed neural network (NN) models were executed using different ANN structures with various training and transfer functions based on the feedforward backpropagation approach to find the optimal model. The performance of the ANN models was evaluated in accordance with validation metrics, like the mean squared error (MSE) and correlation coefficient (R2). Assessing the experimental findings revealed the maximum shear force and nugget diameter emerged to be 8.6 kN and 5.4 mm for the case of 1–1 mm, 3.298 kN and 4.1 mm for the case of 0.5–0.5 mm, and 4.031 kN and 4.9 mm for the case of 0.5–1 mm. Based on the results of the Pareto charts generated by the Minitab program, the most important parameter for the 1–1 mm case was the welding current; for the 0.5–0.5 mm case, it was pulse welding; and for the 0.5–1 mm case, it was holding time. When looking at the hardness results, it is clear that the nugget zone is much higher than the heat-affected zone (HZ) and base metal (BM) in all three cases. The ANN models showed that the one-output shear force model gave the best prediction, relating to the highest R and the lowest MSE compared to the one-output nugget diameter model and two-output structure. However, the Levenberg–Marquardt backpropagation (Trainlm) training function with the log sigmoid transfer function recorded the best prediction results of both ANN structures. [ABSTRACT FROM AUTHOR]

The use of thermoplastic materials has had significant growth in recent years. However, with great mechanical requirements, thermoplastics have limitations to their use. To improve these restrictions, these materials are reinforced to obtain better properties. Polypropylene is one of the most versatile polymers and is used in almost all modern industries. Thus, the aim of this study is to create composite materials that offer performance for various industrial fields using carbon fiber fabric reinforcement, which is an inexpensive material widely used by the aerospace, automotive, and marine industries. The samples are produced by the over-injection molding of polypropylene. The investigation is focused on the impact of two critical control parameters in the injection molding process: temperature and pressure. Twelve experiments have therefore been considered, taking into account the combination of three factors: the presence or absence of carbon fiber fabric reinforcement, three levels of temperature (200 °C, 220 °C, and 240 °C), and two injection pressures (5000 kPa and 10,000 kPa). To evaluate the influence of these factors, three analyses were carried out: first, on the samples' shrinkage using a portable metrology-grade 3D laser scanner; second, on the internal defects using computed tomography (CT); and third, on the mechanical properties with tensile tests. From the results obtained, it is observed that the mold shrinkage fell slightly when PP samples were reinforced with carbon fiber, with both materials (PP and carbon-fiber-reinforced PP) having linear behavior with temperature. It is also noticed that polypropylene behaves as a crystalline material when processed at higher temperatures and pressures. From tests on the mechanical properties, it is concluded that the mean yield strength of PP-CF for injection temperatures of 220 °C and 240 °C represents an increase of 43% compared to the non-reinforced material. [ABSTRACT FROM AUTHOR]

Wire arc additive manufacturing (WAAM) is renowned for its high deposition rate, enabling the production of large parts. However, the process has challenges such as porosity formation, residual stresses, and cracking when manufacturing aluminum parts. This study focuses on ana-lyzing the porosity of AA5356 walls manufactured using the WAAM process with the Fronius cold metal transfer system (Wels, Austria). The walls were machined to obtain specimens for tensile testing. The study used computed tomography and the tensile test to analyze the specimens' porosity and its potential relation to tensile strength. The process parameters analyzed were travel speed, cooling time, and path strategy. In conclusion, increasing travel speed and cooling time significantly affects pore diameter due to the lower heat input to the weld zone. Porosity can be reduced when diminishing heat accumulation. The results indicate that an increase in travel speed produces a slight decrease in porosity. Specifically, the total pore volume diminishes from 0.42 to 0.36 mm3 when increasing the travel speed from 700 to 950 mm/min. The ultimate tensile strength and maximum elongation of the 'back and forth' strategy are slightly higher than those of the 'go' strategy. After tensile testing, the ultimate tensile strength and yield strength did not show any relation to the porosity measured by computed tomography. The percentage of the pore total volume over the measured volume was lower than 0.12% for all the scanned specimens. [ABSTRACT FROM AUTHOR]

The primary goal of this Special Issue was to present recent trends in surface inspection and description, from both metrological and tribological points of view. Inst. Mech. Eng. Part J J. Eng. Tribol. 2017; 62: 53-59. 10.14314/polimery.2017.053 31 Townsend A., Pagani L., Scott P., Blunt L. Areal surface texture data extraction from X-ray computed tomography reconstructions of metal additively manufactured parts. Surfaces are important in many different fields of science [[13]], often far beyond classical engineering topics from metal cutting, including emissive properties [[14]], wire arc hybrid manufacturing [[15]], concrete [[16]], bioengineering [[17]], or even archaeology [[18]]. [Extracted from the article]

Robotic-directed energy deposition has attracted the attention of the research community and industry as a process capable of producing large metallic parts. The selection of the manufacturing conditions is a critical step in improving the process efficiency and quality of the produced parts. The present work aims at analyzing the geometry and surface topography of walls built using several conditions and inclination angles, without additional supports except for the substrate. The walls were made of AWS A5.18. ER70S-6 steel using the Wire Arc Additive Manufacturing process. The study used both dimensional and 3D topography measurements to analyze the results. As findings, the travel speed played an important role in the size of the cross-section due to the heat input to the welding zone. Heat accumulation was a critical factor in the size and accuracy of the beads. Moreover, intermediate cooling provided structures with more uniform dimensions, smaller width, and higher layer growth. The inclination of the pieces influenced the width and uniformity of the beads, generating minor imperfections on the downside of the pieces because of gravity. [ABSTRACT FROM AUTHOR]

In the process of continuous improvement of manufacturing processes, this study was developed within the framework of the Ecovoss project, based on the inclusion of lightweight and new materials parts in the automotive sector. The objective was based on the replacement of aluminum welding operations with the option of adhesive operations with other types of materials such as polyamides or, in this case, a TEPEX® composite material (Dynalite 202-c200/50% TYP 13). The aim of this work is to test the best texturing of substrate made in 7075 aluminum specimens manufactured by robotic polishing with an ABB 6640 robot. Another substrate is TEPEX composite. A structural adhesive film AF-163-2 from the 3M company (St Paul, MN, USA) is used, which must be applied according to the manufacturing procedure. The tests carried out are based on the topographic measurement of the surfaces to be joined with an Alicona focus variation microscope, and the uniaxial shear tests of adhesive samples have been analyzed. The texture of the surface failure has been analyzed, and the results confirm a significant correlation between the texture parameters of initial surfaces and maximum shear stress. The expected results should provide a better understanding of the surfaces aimed to optimize the adhesion of the studied materials. [ABSTRACT FROM AUTHOR]

The new requirements in different sectors, such as aerospace, automotive and construction, for lightweight materials have led to an increase in demand for composite materials suitable for use in high rate production processes, such as plastic injection. This makes it necessary to look for matrices and reinforcements that, in addition to being compatible with each other, are also compatible with the injection process. It is in this area of research where the work presented here arises. To meet the two requirements mentioned above, this study contemplates a battery of composite materials obtained by combining PA66 and fiberglass, in different proportions and configuration, both for the preparation of the matrix and for reinforcement. For the elaboration of the matrix, two options have been evaluated, PA66 and PA66 reinforced at 35% with short glass fibre. To obtain reinforcement, six different options have been evaluated; two conventional fiberglass fabrics (each with different density) and four hybrid fabrics obtained from the previous ones by adding PA66 in different configurations (two over-stitched fabrics and two other fabrics). The different composite materials obtained were validated by means of the corresponding adhesion, peeling and resistance tests. [ABSTRACT FROM AUTHOR]

Recently, there is growing interest in optimisation of finishing process thanks to the technologies to follow online the wear of cutting tools. In the present paper, one of the cheapest and simplest non-contact methodologies is described in detail and investigated with robustness evaluation. To simulate the finishing operation of a die, in this study, two cavities were designed in AISI H13 steel. Different inserts corresponding to PVD-(Ti,Al)N coated cemented carbide tool were tested. The described methodology is easy to be applied in manufacturing cutting machining with the opportunity to be implemented on machining processes to follow reasonably wear process of cutting tools. [ABSTRACT FROM AUTHOR]