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Resistance spot welding (RSW) is one of the most relevant industrial processes in diferent sectors. Key issues in RSW are process control and ex-ante and ex-post evaluation of the quality level of RSW joints. Multiple-input–single-output methods are commonly used to create predictive models of the process from the welding parameters. However, until now, the choice of a particular model has typically involved a tradeof between accuracy and interpretability. In this work, such dichotomy is overcome by using the explainable boosting machine algorithm, which obtains accuracy levels in both classifcation and prediction of the welded joint tensile shear load bearing capacity statistically as good or even better than the best algorithms in the literature, while maintaining high levels of interpretability. These characteristics allow (i) a simple diagnosis of the overall behavior of the process, and, for each individual prediction, (ii) the attribution to each of the control variables—and/ or to their potential interactions—of the result obtained. These distinctive characteristics have important implications for the optimization and control of welding processes, establishing the explainable boosting machine as one of the reference algorithms for their modeling., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors acknowledge fnancial support from the Spanish Ministry of Science, Innovation and Universities (Excellence Network RED2018‐102518‐T), the Spanish State Research Agency (PID2020-118906 GB-I00/AEI/https://doi.org/10. 13039/501100011033), and the Fundación Bancaria Caixa D. Estalvis I Pensions de Barcelona, La Caixa (2020/00062/001). In addition, we acknowledge support from the Santander Supercomputación group (University of Cantabria) that provided access to the Altamira Supercomputer—located at the Institute of Physics of Cantabria (IFCACSIC) and member of the Spanish Supercomputing Network—to perform the diferent simulations/analyses.

The combined effect of resistance spot welding and precipitation hardening on the localised corrosion of A286 superalloy is studied. The specimens tested by double loop electrochemical potentiokine...

This work aims to study the combined effect of resistance spot welding (RSW) and precipitation hardening on tensile shear load bearing capacity (TSLBC) of A286 superalloy. The tensile shear test specimens were welded in the solution treated condition, and then subjected to different precipitation hardening treatments. The impoverishment in Ti, due to its segregation towards the interdendritic region (studied by performing SEM/EDX analysis), causes the development of gamma prime depleted zones in the dendrite core of the weld nugget, which is associated with a decrease of the mechanical properties with respect to the base metal. The segregation of Ti towards the interdendritic region of the weld nugget has also influence on the eta phase formation, which appears later (with longer aging times) in the weld nugget than in the base metal. The aging time at which the maximum experimental TSLBC is reached is, for both aging temperatures, 100 h. The experimental TSLBC of the tensile shear test specimens hardened at lower aging temperature is greater (except for the shortest aging time) than that of those hardened at higher aging temperature.

La maquinabilidad es ampliamente utilizada para comparar materiales a partir de su aptitud para dar lugar a un cierto acabado superficial al menor coste de herramienta posible, mediante el uso principalmente de tests de corte continuo tales como los tests basados en torneado. No obstante, en el acero inoxidable austenitico AISI 316L esta justificado el uso de un concepto mas amplio de maquinabilidad que tambien considere la influencia de cambios microestructurales, inducidos por el mecanizado, en el comportamiento frente a la corrosion por picaduras. Palabras clave: AISI 316L; Maquinabilidad; Corte ortogonal; Corrosion por picaduras.

The present work aims to assess the efficiency of windscreen demisting systems in electrical vehicles by using infrared (IR) thermography and image analysis techniques. The study was performed on three different vehicles: (i) an internal combustion engine vehicle (ICEV); (ii) a full electric vehicle (EV) that uses an electric resistance (ER) for heating the vehicle cabin; and (iii) a full EV that incorporates a heat pump (HP). As demonstrated by the results, the performance of windscreen demisting systems of EVs is higher than that of ICEV. In turn, the performance and the efficiency of windscreen demisting systems of EV–HP are higher than that of EV–ER.

This work studies the influence of gamma prime and eta phases on the pitting corrosion of A286 superalloy by potentiodynamic anodic polarization (PAP) and cyclic potentiodynamic polarization (CPP). The general trend for pitting potential (Epit) parameter is that: (i) the higher the aging temperature, the lower the Epit; and (ii) Epit decreases with increasing aging time. The repassivation potential (Erp) allows to study the Epit–Erp difference, which decreases slightly with increasing aging time. The general trend for Epit is altered by the eta phase precipitation at GB which, although it may enhance pit initiation, grows at the expense of gamma prime phase. The general trend for and Epit–Erp is altered by the eta phase precipitation at GB because, even though the nickel-rich eta phase precipitation, just as gamma prime phase precipitation, causes the nickel content of the austenitic matrix to reduce and consequently the repassivation properties to increase, eta phase grows at the expense of gamma prime phase.

This work studies the influence of gamma prime and eta phases, resulting from precipitation hardening, on the corrosion behaviour of A286 superalloy by using electrochemical potentiokinetic reactivation (EPR) and double loop electrochemical potentiokinetic reactivation (DLEPR). The general trend is that Ir, Qr, Ir/Ia and Qr/Qa increase with aging temperature and, for a given aging temperature, with aging time, which is consistent with the fact that gamma prime precipitate may cause discontinuities in the passive layer. Ir, Qr, Ir/Ia and Qr/Qa reach a peak, which alters the general trend, for both aging temperatures (670 and 720 °C), at the aging time from which there is evidence of the presence of eta phase at grain boundary (GB). Gamma prime and eta precipitates can cause the passive layer that forms on them to become more unstable and, consequently, cause the current density through the passive layer and the susceptibility to localized corrosion attack to increase. Keywords: A286 superalloy, Precipitation hardening, EPR, DLEPR, SEM

In the present paper, the possible relationships among the variables of the potentiodynamic anodic polarisation (PAP) test and the electrochemical potentiokinetic reactivation (EPR) test, both performed on resistance spot welding joints of AISI 304 stainless steel, are investigated. Results show that PAP variables are statistically independent from those of EPR, which implies independence between the pitting corrosion behaviour and the degree of sensitisation of the material. Parameters from PAP test are dependent among them, but this association is found noisy since the current density along the passive zone is not exactly constant. The parameters from the EPR test are confirmed as very related, presenting a linear relationship and a high coefficient of determination., The authors acknowledge support from the Spanish Ministerio de Ciencia e Innovación (MICINN) Project CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010).

This work presents the development of an experimental study which aims to establish arelationship of temperature variations, deformation speeds and the possible microstructural changes in the AISI 304 stainless steel. For this, an IR thermographic camera recorded serveral tensile test with different deformation speeds.

This work aims to study the effect of Widmanstätten η phase on tensile shear strength (TSS) of resistance spot welding joints (RSW) of A286 superalloy subjected to post-weld high temperature aging treatment. The tensile shear test specimens were welded in the solution treated condition, and then subjected to six different post-weld aging treatments (at an aging temperature of 840 °C for six different aging times). The as-cast dendritic microstructure of the weld nugget and the austenite equiaxed-grain microstructure of the base metal have a different response to the post-weld high temperature aging treatment. Growth of Widmanstätten η phase in the weld nugget is faster than in the base metal. While in the base metal the Widmanstätten η phase precipitates into the grain, in the weld nugget precipitates at the interdendritic region because of the segregation of Ti towards the last-to-solidify interdendritic regions (studied by performing SEM/EDX analysis). Although the hardness increases with the presence of Widmanstätten η phase, the increase in hardness does not necessarily imply an increase in the experimental TSS.

This work aims to assess the effect of welding on the stress corrosion cracking (SCC) behaviour of prior cold worked AISI 316L stainless steel from ultimate tensile strength (UTS) and time to failure (TF) obtained in slow strain rate tests (SSRTs), which are conducted in corrosive environment and in non-corrosive environment (air). The UTS of cold worked and welded specimens does not show significant variations with prior cold work (CW). However, the TF of cold worked and welded specimens depends on several phenomena that occur in the heat affected zone (HAZ), such as sensitization, recrystallization, recrystallized grain growth or thermal transformation of strain-induced martensite. Additionally, a metallographic study of SSRT tested specimens is performed with the aim of assessing the fracture mode, which proves to be ductile. This work shows that the combined effect of prior CW and welding does not give rise to SCC because the degree of sensitization (DOS) induced in the HAZ is not sufficient to direct the crack growth.

In this work, the quality of resistance spot welding (RSW) joints of 304 austenitic stainless steel (SS) is assessed from its tensile shear load bearing capacity (TSLBC). A predictive model using a polynomial expansion of the relevant welding parameters, i.e. welding current (WC), welding time (WT) and electrode force (EF) and elastic net regularization is proposed. The predictive power of the elastic net approach has been compared to artificial neural networks (ANNs), previously used to predict TSLBC, and smoothing splines in the framework of a generalized additive model. The results show that the predictive and classification error of the elastic net model are statistically comparable to benchmarks of the best pattern recognition tools whereas it overcomes correlation problems and performs variable selection at the same time, resulting in a simpler and more interpretable model. These features make the elastic net model amenable to be used in the design of welding conditions and in the control of manufacturing processes., The authors would like to thank Dr. L.R. Izquierdo for some advice and comments on this paper. The authors acknowledge support from the Spanish MICINN Project CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010).

This work aims to assess the effect of prior cold work on the degree of sensitisation of each of the four welding zones of welded joints of AISI 316L subjected to post-welding sensitisation. Electrochemical potentiokinetic reactivation and double loop electrochemical potentiokinetic reactivation tests are performed on each of the four zones by using a small-scale electrochemical cell (minicell). The results show that the degree of sensitisation of heat affected zone, which achieves its maximum at a prior cold work level of 10%, is significantly lower than that of base material for a prior cold work of 20%.

This work studies the combined effect of resistance spot welding (RSW) and post-welding sensitization on the degree of sensitization (DOS) of AISI 304 stainless steel (SS) by using EPR and DLEPR tests. The combined effect of RSW and post-welding sensitization at 750 °C gives rise to an overall DOS that: (i) is lower than that of the state without prior welding; (ii) that decreases initially with increasing sensitization time but then increases. This behaviour is due to the fact that the interdendritic corrosion (IDC) located in the weld metal decreases with increasing sensitization time.

The degree of sensitization (DOS) in resistance spot welding (RSW) joints is considered as the combined effect of intergranular corrosion (IGC) and transgranular corrosion (TGC) in the heat affected zone (HAZ) and of interdendritic corrosion (IDC) in the weld nugget (WN). The DOS is evaluated from electrochemical potentiokinetic reactivation (EPR) tests. The application of EPR test to RSW joints is optimized and an electrochemical minicell is used to study the effect of heat input on IDC in the WN.

The quality level of a resistance spot welding (RSW) joint of 304 austenitic stainless steel (ASS) is estimated from its tensile shear load bearing capacity (TSLBC). The quality levels are set by ultrasonic nondestructive testing. The objective of the present work is to develop a tool capable of reliably predicting the TSLBC (and consequently the quality level) of RSW joints from three welding parameters: (1) welding time (WT); (2) welding current (WC); (3) electrode force (EF). Firstly, a linear regression model is attempted but the residuals analysis reveals nonlinear behaviour. An artificial neural network (ANN) is proposed because the ANNs are capable of mapping nonlinear systems. The inputs are 3-component vectors, a component for each of the aforementioned welding parameters. The training of the ANN uses supervised learning mechanism. Therefore each input must come with its respective desired output (target). This target is the TSLBC of the RSW joint obtained with the respective input. The number of neurons in the hidden layers is selected considering the overfitting phenomenon: the number of neurons in the hidden layers that minimizes the validation mean square error (MSE) is 4. With the selected ANN, 3–4–4–1, the aim of the present study is achieved because this ANN produces good results in prediction from inputs nonused in the training.

A specific electrochemical cell for small scale electrochemical testing has been built. The electrochemical cell has been made bearing in mind the microstructural features of the stainless steel weldments and the performance of the design has been verified. Two polarisation methods (potentiodynamic anodic polarisation measurements and cyclic potentiodynamic polarisation measurements) and two reactivation electrochemical techniques (the electrochemical potentio-kinetic reactivation test, EPR, and electrochemical potentio-kinetic reactivation double loop test, EPRDL) have been applied to two austenitic stainless steels: AISI 304 and AISI 316L. The results are reliable and have been compared to large scale experiments. Scanning of the welding joints has been performed and the results have been correlated with their microstructural features. It, thus, makes it possible to study the intrinsic heterogeneous microstructures such as the heat affected zone of a welded joint.

The purpose of this study is to create a tool capable of reliably predicting the magnetic fields (MFs) generated in resistance spot welding (RSW) processes. If the MF is greater than 10 G, the MF activates a reed switch in the implantable cardioverter-defibrillator (ICD) and temporarily inhibits detection of ventricular tachycardia and ventricular fibrillation. The MF is predicted from two variables: (1) distance between ICD and electrodes; (2) welding current. Firstly, a linear regression model is attempted. The MF is the dependent variable, 1/distance and welding current are the independent variables. The ANOVA table shows that the regression is significant at the 0.01 level but the residuals analysis reveals nonlinear behaviour. An artificial neural network (ANN) is proposed because the ANNs are capable of mapping nonlinear systems. The inputs are two-component vectors, the first component is the distance value and the second component is the welding current value. The training of the ANN uses supervised learning mechanism. Therefore, each input must come with its respective desired output (target) that is the recorded MF. The available data set is randomly split into a training subset (to update weight values) and a validation subset (to guard against overfitting by means of cross validation). The number of neurons in the hidden layers is selected considering the overfitting phenomenon: the number of neurons in the hidden layers that minimizes the validation mean square error (MSE) is 4. With the selected ANN (2-4-4-1) the aim of the present study (to reliably predict the MF) is achieved because this ANN produces good results in prediction from input vectors non-used in the training.

Producción Científica, This work optimizes the application of electrochemical potentiokinetic reactivation (EPR) to assess the degree of sensitization (DOS) of AISI 316L and compares the large-scale and small-scale EPR with the aim of improving the study of the different zones of AISI 316L welded joints by using an electrochemical minicell. The optimized EPR allows to discriminate better than the standardized EPR among different DOS. Small-scale EPR shows greater sensitivity to assess the DOS than large-scale EPR: (i) at lower deformation levels; (ii) for shorter sensitization times; (iii) when localized microstructural regeneration is caused by the combined effect of deformation and subsequent sensitization., 2030-01-01, Ministerio de Ciencia e Innovación del Gobierno de España, Dirección General de Programas y Transferencia de Conocimiento, Proyectos I+D