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Application of Optimized Convolutional Neural Network to Fixture Layout in Automotive Parts by Javier Villena Toro; Anton Wiberg; Mehdi Tarkian is a Engineering article available to read on EtoBox.

What is Application of Optimized Convolutional Neural Network to Fixture Layout in Automotive Parts about?

Abstract Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pill

Who reads Application of Optimized Convolutional Neural Network to Fixture Layout in Automotive Parts?

Author

Javier Villena Toro; Anton Wiberg; Mehdi Tarkian

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Engineering (Physical Sciences)