Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets

Abstract

Today the topic of incremental sheet forming (ISF) is one of the most active areas of sheet metal forming research. ISF can be an essential alternative to conventional sheet forming for prototypes or non-mass products. Single point incremental forming (SPIF) is one of the most innovative and widely used fields in ISF with the potential to form sheet products. The formed components by SPIF lack geometric accuracy, which is one of the obstacles that prevents SPIF from being adopted as a sheet forming process in the industry. Pillow effect and wall displacement are influential contributors to manufacturing defects. Thus, optimal process parameters should be selected to produce a SPIF component with sufficient quality and without defects. In this context, this study presents an insight into the effects of the different materials and shapes of forming tools, tool head diameters, tool corner radiuses, and tool surface roughness (Ra and Rz). The studied factors include the pillow effect and wall diameter of SPIF components of AlMn1Mg1 aluminum alloy blank sheets. In order to produce a well-established study of process parameters, in the scope of this paper different modeling tools were used to predict the outcomes of the process. For that purpose, actual data collected from 108 experimentally formed parts under different process conditions of SPIF were used. Neuron by Neuron (NBN), Gradient Boosting Regression (GBR), CatBoost, and two different structures of Multilayer Perceptron were used and analyzed for studying the effect of parameters on the factors under scrutiny. Different validation metrics were adopted to determine the quality of each model and to predict the impact of the pillow effect and wall diameter. For the calculation of the pillow effect and wall diameter, two equations were developed based on the research parameters. As opposed to the experimental approach, analytical equations help researchers to estimate results values relatively speedily and in a feasible way. Different partitioning weight methods have been used to determine the relative importance (RI) and individual feature importance of SPIF parameters for the expected pillow effect and wall diameter. A close relationship has been identified to exist between the actual and predicted results. For the first time in the field of incremental forming study, through the construction of Catboost models, SHapley Additive exPlanations (SHAP) was used to ascertain the impact of individual parameters on pillow effect and wall diameter predictions. CatBoost was able to predict the wall diameter with R2 values between the range of 0.9714 and 0.8947 in the case of the training and testing dataset, and between the range of 0.6062 and 0.6406 when predicting pillow effect. It was discovered that, depending on different validation metrics, the Levenberg–Marquardt training algorithm performed the most effectively in predicting the wall diameter and pillow effect with R2 values in the range of 0.9645 and 0.9082 for wall diameter and in the range of 0.7506 and 0.7129 in the case of the pillow effect. NBN has no results worthy of mentioning, and GBR yields good prediction only of the wall diameter.

Application of categorical boosting to modelling the friction behaviour of DC05 steel sheets in strip drawing test

“…The coefficient of determination R 2 value was determined according to the following relationship (Najm & Paniti, 2023):…”

Advanced FEM Insights into Pressure-Assisted Warm Single-Point Incremental Forming of Ti-6Al-4V Titanium Alloy Sheet Metal

“…Economical, technological and ecological aspects of SPIF versus conventional deep drawing were presented by Petek et al [16] and Oleksik et al [17]. The disadvantage of the SPIF process is the relatively large springback of the components after the forming process [18] and the pillow effect [19]. Behera and Ou [20] found that the stress-relieving heat treatment of titanium sheets affects the dimensional accuracy of parts formed using SPIF.…”

Prediction of forming accuracy in incremental sheet forming using artificial neural networks on local surface representations

“…Recently, researchers are focusing on the application of machine learning (ML) techniques for predicting several process specific dimensions [3]. These include forming accuracy [4], surface quality [5], tool load [6], forming temperature [7], the pillow effect [8] and the material flow curve [9]. Due to the lack of industrial ISF production lines, the data used for training the ML models has to be gathered by the research institutes themselves.…”