Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques

PLoS One. 2019 Aug 22;14(8):e0221341. doi: 10.1371/journal.pone.0221341. eCollection 2019.

Abstract

Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technology helps in preventing failures, determining the optimal processes, and implementing on-line control. In this paper, an experimental study using SPIF is described. This study focuses on the influence of four different process parameters, namely, step size, tool diameter, sheet thickness, and feed rate, on the maximum forming force. For an efficient force predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a regressions model were applied. The predicted forces exhibited relatively good agreement with the experimental results. The results indicate that the performance of the ANFIS model realizes the full potential of the ANN model.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alloys / chemistry
  • Aluminum / chemistry
  • Computer-Aided Design / instrumentation*
  • Computer-Aided Design / statistics & numerical data
  • Fuzzy Logic
  • Humans
  • Manufacturing Industry / instrumentation
  • Manufacturing Industry / methods*
  • Materials Testing
  • Neural Networks, Computer*

Substances

  • Alloys
  • Aluminum

Grants and funding

Deanship of Scientific Research at King Saud University funding this work through research group No (RG- 1439-56).