ANN Modelling to Optimize Manufacturing Process

Neural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted

Recent advances in controlling, monitoring and optimization of the friction stir welding process

Friction Stir Welding (FSW) is a solid-state welding process and currently turns out to be the most widely used friction joining technique for light alloys, dissimilar materials, and metals that are not easily weldable. This process offers many advantages with respect to the traditional welding methods and it is considered an energy-saving, environment friendly, and relatively versatile technology. In the last years, several works in literature focused on the study and optimization of the process parameters directly related to the quality of the produced joints. This work presents a collection of new advanced studies for investigating the correlation between the process parameters and the quality of joints in terms of their mechanical properties such as the Ultimate Tensile Strength and hardness. Moreover, it is presented the capability of thermography to study the FSW process. A new approach based on the investigation of the thermal behaviour of plates during both the heating and cooling phase is proposed. This approach revealed more effective in the description of the process parameters than the classical one based on the monitoring of the absolute temperature. In addition, Artificial Neural Networks (ANNs) were used for optimizing and predicting the mechanical properties (output values) of the welded joints based on the FSW process parameters (input variables)

Effect of Friction Stir Process Parameters on the Mechanical and Thermal Behavior of 5754-H111 Aluminum Plates

A study of the Friction Stir Welding (FSW) process was carried out in order to evaluate the influence of process parameters on the mechanical properties of aluminum plates (AA5754-H111). The process was monitored during each test by means of infrared cameras in order to correlate temperature information with eventual changes of the mechanical properties of joints. In particular, two process parameters were considered for tests: the welding tool rotation speed and the welding tool traverse speed. The quality of joints was evaluated by means of destructive and non-destructive tests. In this regard, the presence of defects and the ultimate tensile strength (UTS) were investigated for each combination of the process parameters. A statistical analysis was carried out to assess the correlation between the thermal behavior of joints and the process parameters, also proving the capability of Infrared Thermography for on-line monitoring of the quality of joints

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration

Optimization and Characterization of the Friction Stir Welded Sheets of AA 5754-H111: Monitoring of the Quality of Joints with Thermographic Techniques

Friction Stir Welding (FSW) is a solid-state welding process, based on frictional and stirring phenomena, that offers many advantages with respect to the traditional welding methods. However, several parameters can affect the quality of the produced joints. In this work, an experimental approach has been used for studying and optimizing the FSW process, applied on 5754-H111 aluminum plates. In particular, the thermal behavior of the material during the process has been investigated and two thermal indexes, the maximum temperature and the heating rate of the material, correlated to the frictional power input, were investigated for different process parameters (the travel and rotation tool speeds) configurations. Moreover, other techniques (micrographs, macrographs and destructive tensile tests) were carried out for supporting in a quantitative way the analysis of the quality of welded joints. The potential of thermographic technique has been demonstrated both for monitoring the FSW process and for predicting the quality of joints in terms of tensile strength

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration

Giunti di testa in lega di alluminio 5754-H111 realizzati con il processo di Friction Stir Welding: Applicazione delle reti neurali artificiali per la previsione delle caratteristiche meccaniche dei giunti

The aim of this study was to develop an Artificial Neural Networks (RNA) able to model the Friction Stir Welding process (FSW) to provide an accurate prediction of the mechanical properties of AA 5754-H111 butt joints. The input data used for the development of neural models are taken from previous experimental investigations, which are developed according the Design of Experiments (DOE) techniques; all the joints were monitored on-line by means of infrared thermography techniques and they were characterized with destructive and non-destructive testing (visual and macro graphic analysis, tensile and Vickers microhardness tests), in order to highlight their mechanical characteristics. Finally, the significance of the FSW process parameters was evaluated by means of the Analysis of Variance (ANOVA). The use of Artificial Neural Networks to model the FSW process, has the aim of optimizing the technological parameters and to favor the development of a stable welding process, that is able to realize joints without defects and with high mechanical properties. For this purpose, two Artificial Neural Networks have been designed. They worked according the "cascade mode" in order to predict the mechanical behavior of the joints in terms of Ultimate Tensile Strength (UTS) and Vickers micro hardness of the Heat Affected Zone (HAZ). The back-propagation learning logarithm and the analysis of many network architectures has allowed us to formulate reliable predictions. This is shown by the results of the comparison between the model results and experimental data that led to the definition of a final model that can predict the quality of butt joints in aluminum alloy 5754 H111 FSW with good accuracy

Thermographic signal analysis of friction stir welded AA 5754 H111 joints

Aluminium alloys present some criticalities in terms of fatigue life characterisation due to the absence of a point representing the 'fatigue limit', the topic becomes complicated when the material is welded. In this case, the fatigue characterisation lies on design specifications which have to clearly explain the guidelines for the performing the tests and for evaluating the failures, in order to design tailored welded joints. However, the fatigue of welded joints is a difficult subject since the welding process makes the material different, introducing residual tensions, defect, etc. Also, the standard test methods provide only the estimation of the strength at fixed loading cycles but no information on the damage processes occurring in the material.Prompted by these issues researchers deal with the study of other approaches to achieve not only information on fatigue resistance but also damage information. In particular, the thermography can be used for thermal signal analysis of dissipative heat sources involved in fatigue of material undergoing cyclic test.In this paper, this approach is adopted to study the fatigue behavior of friction stir welded joints of AA5754-H111 during specific loading conditions. The component of the temperature related to intrinsic dissipations is assessed and the fatigue strength is evaluated together with a graphical study of the location of damaged areas

Correlation between Thermal Behaviour of AA5754-H111 during Fatigue Loading and Fatigue Strength at Fixed Number of Cycles

The characterization of the fatigue behaviour of aluminium alloys is still capturing the attention of researchers. As it is well known in literature, for certain alloys, in a specific range of cycles number, the S-N curves do not present any asymptote. So that, problems result in the assessment of the fatigue life. In these conditions, the concept of the fatigue limit has to be replaced by the fatigue strength at a fixed number of loading cycles. Temperature acquisitions during fatigue tests allow for a specific analysis that can support the researchers in understanding the complex processes that are involved in fatigue and their influence on fatigue life, even for aluminium alloys. In fact, the analysis of the surface temperature signal that was detected during a self-heating test provides a curve that is characterized by a distinct slope-change point at a specific stress value. Even though researchers have been investigating fatigue life characterisation and temperature variations for more than a decade, it is not clear what this point represents in terms of fatigue strength. The aim of the present paper is to find out a possible correlation between the thermal behaviour of AA5754-H111 undergoing self-heating testing procedure and fatigue strength at a specific loading cycles

Early CAR- CD4+ T-lymphocytes recovery following CAR-T cell infusion: A worse outcome in diffuse large B cell lymphoma

CAR(-) CD4(+) T cell lymphopenia is an emerging issue following CAR-T cell therapy. We analyzed the determinants of CD4(+) T cell recovery and a possible association with survival in 31 consecutive patients treated with commercial CAR-T for diffuse large B-cell (DLBCL) or mantle cell lymphoma. Circulating immune subpopulations were characterized through multiparametric-flow cytometry. Six-month cumulative incidence of CAR(- )CD4(+) T cell recovery (>= 200 cells/mu L) was 0.43 (95% confidence interval [CI]: 0.28-0.65). Among possible determinants of CD4(+) T cell recovery, we recognized infusion of a 4-1BB product (tisagenlecleucel, TSA) in comparison with a CD28 (axicabtagene/brexucabtagene, AXI/BRX) (hazard ratio [HR] [95% CI]: 5.79 [1.16-24.12] p = 0.016). Higher CD4(+) T cell counts resulted with TSA at month-1, -2 and -3. Moderate-to-severe infections were registered with prolonged CD4(+) T cell lymphopenia. Early, month-1 CD4(+ )T cell recovery was associated with a worse outcome in the DLBCL cohort, upheld in a multivariate regression model for overall survival (HR: 4.46 [95% CI: 1.12-17.71], p = 0.03). We conclude that a faster CAR(- )CD4(+) T cell recovery is associated with TSA as compared to AXI/BRX. Month-1 CAR(-) CD4(+) T cell subset recovery could represent a "red flag" for CAR-T cell therapy failure in DLBCL patients