Open AccessPrediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques
Author(s) -
Ali M. Al-Samhan,
Adham E. Ragab,
Abdulmajeed Dabwan,
Mustafa M. Nasr,
Lotfi Hidri
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0221341
Subject(s) - adaptive neuro fuzzy inference system , incremental sheet forming , artificial neural network , computer science , process (computing) , forming processes , point (geometry) , inference system , sheet metal , mechanical engineering , biological system , materials science , fuzzy logic , artificial intelligence , mathematics , engineering , fuzzy control system , geometry , operating system , biology
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.