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<p>In this work, two widely used materials in automotive applications, namely dual phase steel (DP) and deep drawing quality steel (DDQ), are used to estimate and optimize the energy calculations in the incremental sheet forming (ISF) process. The various important process parameters, such as sheet thickness, step depth, tool diameter, temperatures, and feed rates, have been considered for energy estimation.</p><p>An analytical approach proposed by Aerene’s has been used to predict the energy consumption. The impact of these variables on energy consumption in incremental forming is analyzed using ANOVA, and the results are validated through experiments. Finally, among all the input parameters, material type has shown the highest contribution at 32.20%, while step depth has a negligible impact at 0.20%. The optimized predicted results for process energy consumption are in good agreement with experimental findings. This work establishes a systematic framework to predict energy consumption in the ISF process.</p>

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Analytical Prediction and Optimization of Energy Consumption in Incremental Sheet Forming for Sustainable Manufacturing

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • Sandeep Pandre,
  • Shreya Kudumala,
  • Nitin Kotkunde,
  • Swadesh Kumar Singh,
  • Chetan Nikhare

摘要

In this work, two widely used materials in automotive applications, namely dual phase steel (DP) and deep drawing quality steel (DDQ), are used to estimate and optimize the energy calculations in the incremental sheet forming (ISF) process. The various important process parameters, such as sheet thickness, step depth, tool diameter, temperatures, and feed rates, have been considered for energy estimation.

An analytical approach proposed by Aerene’s has been used to predict the energy consumption. The impact of these variables on energy consumption in incremental forming is analyzed using ANOVA, and the results are validated through experiments. Finally, among all the input parameters, material type has shown the highest contribution at 32.20%, while step depth has a negligible impact at 0.20%. The optimized predicted results for process energy consumption are in good agreement with experimental findings. This work establishes a systematic framework to predict energy consumption in the ISF process.