Automatic Machinability Evaluation and Recommendation for Reconfigurable Manufacturing Systems
Yuzhe Wang, Haining Sun, Matthew Francis King, Teck Chew Ng
Abstract
In the current era of rapid-changing market and supply chain fluctuations, manufacturing industries face significant challenges in maintaining agility and resilience. Machinability evaluation is one of the critical steps in manufacturing planning to ensure the industry can respond to rapid market changes or customization demands. However, this process is currently conducted manually and highly relies on engineers’ knowledge and experience. This paper presents a systematic approach that employs fuzzy logic, which can be automated by a software program, for evaluating the machinability of product features and recommending reconfiguration options for computer numerical control (CNC) machines. This approach assesses each product feature against the current machine kinematics. The validated results demonstrate that the proposed fuzzy logic can generate comprehensive results that reflect varying degrees of machinability. For product features that cannot be machined with the existing machine configurations, this approach identifies specific limitations and provides data-driven recommendations for reconfiguration. This will assist machine operators in making informed decisions, thereby reducing reliance on manual evaluation and planning. Furthermore, this automated evaluation process will enable a shorter turnaround time for new production line setups and enhance the overall operational efficiency, especially in high-variety, low-volume manufacturing environments.
BibTeX
@inproceedings{iros2025_automaticmachina,
title = {Automatic Machinability Evaluation and Recommendation for Reconfigurable Manufacturing Systems},
author = {Yuzhe Wang and Haining Sun and Matthew Francis King and Teck Chew Ng},
booktitle = {IROS 2025},
year = {2025}
}