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

Sherwan Mohammed Najm,Imre Paniti

doi:10.1007/s00170-021-06712-4

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.

Highlights

Incremental Sheet Forming (ISF) is suitable for low-volume production and is ideal for complicated designs
The data collected from these samples (108) were used as an actual dataset for prediction; process parameters were used as inputs, and the obtained results of geometric accuracy and formability were used as output arguments of the Artificial Neural Network (ANN) predicted model
The errors extracted from the results were subjected to various validation metrics

Summary

Introduction

Incremental Sheet Forming (ISF) is suitable for low-volume production and is ideal for complicated designs. ISF was patented in 1967 [1], and one of the crucial types of ISF is Single Point Incremental Forming (SPIF). Emerging manufacturing technologies like ISF developed in the past few decades. Researchers have shown that an unconventional sheet forming process like ISF is economically feasible for producing prototypes; ISF is versatile and can produce custom and complex products [2, 3]. A comprehensive literature review about ISF is presented in [4]. A brief review of the history of ISF with a focus on the technological progress involved is found in [1].

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Results