An Approach to Measure Tool Wear through Image Processing in Incremental Sheet Metal Forming

Abstract:

The Incremental sheet metal forming (ISF) is come into the light due to its unique forming technique. In ISF, the metal sheets are transformed into the final product without using dedicated died. The plastic deformation of metal sheets is conducted through a simple forming tool. Its processing time resembles that the ISF is suitable for the formation of customized products, prototypes, and low volume production. Tool life in manufacturing processes is an important consideration for productivity. The present study is an approach to use image processing techniques to measure the exact location and amount of tool wear in the ISF tool which is made of 440C steel. Presently the complex histogram plot is under study. Therefore to predict the tool wear, feed-forward backpropagation (FFB) algorithm is utilized. It is reported that the maximum predicted tool wear of 0.0663mm is found in the trail run 05 with an error of 0.0104mm. The best-fitted value of the FFB model is observed at the epoch 05 with the value of 5.922e-005. The overall coefficient of performance i.e. R² of FFB modeling is reported as 90.52 % with the mean absolute error (MAE) of 0.0042 which shows a good agreement of the prediction model.

• Periodical: Applied Mechanics and Materials (Volume 908)
• Pages: 157-167
• DOI: https://doi.org/10.4028/p-c6r033
• Online since: August 2022
• Authors: Manish Oraon*
• Keywords: ANN, Input Variables, ISF, Tool Wear

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