Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Abstract

Incremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force

“…However, evaluating the impact of different features, it can be concluded that the ‘Forming depth’ is one of the most important features in the force prediction process. Such findings are also observed in studies by other authors [ 15 , 21 ]. Nevertheless, in most cases, additional features with those correlation coefficients with the output features that are higher than r = ±0.3 allow for achieving better prediction accuracy.…”

“…One of the indicators of the quality of the formed product is the surface quality [ 10 , 11 , 12 , 13 , 14 , 15 ]. The authors in [ 10 ] present the results of an analysis of the interaction between the SPIF process parameters and the stiffened ribs made of Alclad aluminum alloy plates.…”

“…In the study [ 14 ], a multiple-layer perceptron-type ANN model is used to predict the SPIF quality part and reduce the gap between the shape obtained using a CNC machine and the CAD. In [ 15 ], different ANN models and structures were presented to predict the accuracy and formability of SPIF components made from thin aluminum alloy blanks. The main finding of the study revealed that the structure of a single output solution showed better results than a network with two outputs.…”

Single-Point Incremental Forming of Titanium and Titanium Alloy Sheets

“…The first type is SPIF; the principle of carrying it out is shown in Figure 3 a. The tool is guided by the machine control system along a pre-programmed trajectory, causing incremental deformation of the sheet [ 39 , 40 ]. A variation of SPIF (Figure 4a) is TPIF, which can be performed with the use of a partial die ( Figure 3 b) or a specific matrix ( Figure 3 c).…”

Parametric Effects of Single Point Incremental Forming on Hardness of AA1100 Aluminium Alloy Sheets

“…They found that predictive models of ANNs for Ra and Rz were characterised by performance measures of R 2 values between 0.657 and 0.979. In other studies, different tool materials and shapes were investigated experimentally to study factors including formability, geometric accuracy [ 31 ], and surface roughness [ 32 ] on an AlMn1Mg1 sheet formed using SPIF under various forming conditions. The researchers evaluated the performance of an Artificial Neural Network (ANN) and Support Vector Regression (SVR).…”