Feasible-machining-area mapping through AI for bandsawing
Year: 2025
Type: article
Abstract: In sawing processes, the manufacturing industry depends on empirical evaluations and operator expertise to manage tool selection, parameter setup, and tool replacement. These traditional methods are inefficient, costly, and inadequate for meeting the growing demands for diverse materials and high-quality standards in modern manufacturing. This study introduces a framework that combines machine learning and physical principles to optimize sawing processes without needing large machining datasets. A columnar sawing machine with an embedded force sensor monitors cutting force in real-time, while offline analyses assess surface roughness and material properties. Datasets were created using carbon steel, die steel, and stainless steel under different machining conditions. Key features were chosen through random forest regression, and a deep-learning model was developed to predict surface roughness based on these physics-guided parameters. Results show that the embedded force sensor effectively captures critical process insights, enables accurate predictions, and helps build a knowledge base for optimizing tool selection, cutting speeds, and surface quality in real-time. This framework works exceptionally well for materials with similar machinability, offering practical guidance for operators. Additionally, integrating real-time monitoring with cloud-based knowledge sharing improves adaptability and scalability across manufacturing scenarios. The study highlights the potential of combining physical insights with machine learning in manufacturing. Future work will expand the framework to encompass a broader range of materials and machining conditions, thereby further enhancing the efficiency and sustainability of manufacturing systems.
Source: Results in Engineering
Authors: Chi-Hua Yu, Mao-Ken Hsu, Shun-Ying Huang, Chen-Wei Conan Guo, Yeau-Ren Jeng
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
Awards: A Smart Design Platform for Bioinspired Architected Metamaterials, G3391725164, G4280743387, Using Deep Learning and Data Analysis to Design Biomarker and Detox Polypeptide for Carbon Monoxide Poisoning( I ), G6382754148, Design and Development of Novel Lightweight Structural Materials with Superior Mechanical Performance by Bio-Inspiration and Materials Genome Initiative( III ), On the Micro-Structure of Material with Superior Mechanical Property and Tribological Performance: Inspiration from the Enamel of Herbivorous Animals, Super-Hard Nanostructure Coatings for Protection and Fabrication Tools of Hard-To-Fabricate Materials( III )
OpenAlex ID: W4415071179