Title: Feature-based modelling and artificial intelligence-based computer assisted process planning systems for three axis milling components

Authors: K.K. Natarajan; J. Gokulachandran

Addresses: Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Coimbatore, 641112, India ' Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Coimbatore, 641112, India

Abstract: Computer-assisted process planning systems help human planners to create better process plans. Feature-based modelling is the current trend in recognising part features. SolidWorks 2018 software is used for CAD modelling and storage of the part manufacturing details in STEP242 file format. This file type stores details such as material, size, stock, dimensional tolerance, and surface finish and interfaces with neural networks to figure out the required machining operation and cutting tool. Throughout this work, different prismatic features, such as a hole, slot, pocket, boss, chamfer, fillet, and face, were considered. A sample prismatic component with nine features was analysed and found to be highly effective. This research concentrates on neural networks-based manufacturing operation selection and cutting tool selection. Levenberg-Marquardt and scaled conjugate gradient neural networks have proven to be more effective in machining operation selection and cutting tool selection respectively. The process parameter selection is done using SolidCAM software followed by NC code generation.

Keywords: computer-aided process planning; CAPP; artificial neural networks; ANNs; manufacturing feature extraction; STEP242; operation selection; cutting tool selection; parameters selection.

DOI: 10.1504/IJISTA.2021.121328

International Journal of Intelligent Systems Technologies and Applications, 2021 Vol.20 No.4, pp.340 - 361

Accepted: 13 Dec 2021
Published online: 04 Mar 2022 *

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