Predict the Effects of Forming Tool Characteristics on Surface Roughness of Aluminum Foil Components Formed by SPIF Usin
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International Journal of Precision Engineering and Manufacturing https://doi.org/10.1007/s12541-020-00434-5
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Predict the Effects of Forming Tool Characteristics on Surface Roughness of Aluminum Foil Components Formed by SPIF Using ANN and SVR Sherwan Mohammed Najm1,2 · Imre Paniti3,1 Received: 29 April 2020 / Revised: 13 August 2020 / Accepted: 19 October 2020 © The Author(s) 2020
Abstract In the present work, multiple forming tests were conducted under different forming conditions by Single Point Incremental Forming (SPIF). In which surface roughness, arithmetical mean roughness (Ra) and the ten-point mean roughness (Rz) of AlMn1Mg1 sheet were experimentally measured. Also, an Artificial Neural Network (ANN) was used to predict the (Ra) and (Rz) by adopting the data collected from 108 components that were formed by SPIF. Forming tool characteristics played a key role in all the predictions and their effect on the final product surface roughness. In the aim to explore the proper materials and geometry of forming tools, different ANN structures, different training, and transfer functions have been applied to predict (Ra) and (Rz) as an output argument. Furthermore, Support Vector Regression (SVR) with different kernel types have been used for prediction, together with Gradient Boosting regression to sort the effective parameters on the surface roughness. The input arguments were tool materials, tool shape, tool end/corner radius, and tool surface roughness (Ra and Rz). The actual data subjected to a fit regression model to generate prediction equations of Ra and Rz. The results showed that ANN with one output gives the best R-Square (R2). Levenberg-Marquardt backpropagation (Trainlm) training function recorded the highest value of R 2, 0.9628 for prediction Ra using Softmax transfer function whereas 0.9972 for Rz by LogSigmoid transfer function. Furthermore, tool materials, together with tool surface (Ra), are playing a significant importance role, affecting the sheet surface roughness (Ra). Whereas tool roughness Rz was the critical parameter effected on the Rz of the product. Also, there was a significant positive effect of tool geometry on the sheet surface roughness. Keywords SPIF · Incremental sheet forming · Single point · ANN · SVR · Flat tool · Predict surface roughness · Aluminum alloy foils
1 Introduction
* Sherwan Mohammed Najm [email protected] * Imre Paniti [email protected] 1
Department of Manufacturing Science and Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H‑1111, Hungary
2
Kirkuk Technical Institute, Northern Technical University, Mosul, Iraq
3
Centre of Excellence in Production Informatics and Control, Institute for Computer Science and Control (SZTAKI), Kende u. 13‑17, Budapest H‑1111, Hungary
Conventional Sheet forming needs punch and die for shaping as stamping or deep drawing. The traditional sheet forming is useful when mass-production shares the cost of the punch and die, which significantly reduces the
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