Open Access
Ranjeet Prasad
Manish Oraon
- Research Scholar Department of Production and Industrial Engineering, Birla Institute of Technology, Mesra Jharkhand India
- Faculty Department of Production and Industrial Engineering, Birla Institute of Technology, Mesra Jharkhand India
- Faculty Department of Production and Industrial Engineering, Birla Institute of Technology, Mesra, Jharkhand India
Abstract
The sheet metal forming industries always try to find an emerging trend to form sheet-metal in a cost-effective manner. In this regard, a forming technique is trending termed as single point incremental forming (SPIF) in which a simple forming tool having hemispherical end rod is moving and simultaneously deforming the clamped metal sheet according to predetermined toolpath command and forms a complete shape. The achievement of required surface quality is difficult in the SPIF especially with the metals having low formability. To study the surface quality of SPIFed part, Cu-Zn alloy is taken for SPIF and conducted the experiments. In SPIF, statistically the step-depth size (∆z) and wall angle (θ)are found significant for surface roughness of CU-Zn alloy In this paper, prediction model is established by using artificial intelligence where artificial neural network (AAN) model is established to predict the surface roughness virtually with the set inputs and measured output. The optimum 6-6-1 model is built with the segregation of experimental data into training (70%), testing (51%), and validation (15%). The overall co-efficient of regression (R2) and mean absolute error (MAE) of the model is found 0.93219 (93.219%) and -1.37 respectively. It is confirmed that properly selected ANN model can be utilized as a prediction tool in manufacturing processes where the output responses always vary.
Keywords: SPIF, Input Parameters, Surface Roughness, ANOVA, ANN
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Journal of Polymer and Composites
| Volume | |
| Received | January 6, 2024 |
| Accepted | March 16, 2024 |
| Published | May 4, 2024 |