Fuzzy supervisory control of end milling process
Information Sciences: an International Journal
Computer Control of Manufacturing Systems
Computer Control of Manufacturing Systems
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
An ontology-based information integration technology for technique preparation
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Implementing a data mining solution for enhancing carpet manufacturing productivity
Knowledge-Based Systems
A process control system for cryogenic CNC elastomer machining
Robotics and Computer-Integrated Manufacturing
A framework for machining optimisation based on STEP-NC
Journal of Intelligent Manufacturing
A novel methodology for cross-technology interoperability in CNC machining
Robotics and Computer-Integrated Manufacturing
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With today's highly competitive global manufacturing marketplace, the pressure for right-first-time manufacture has never been so high. New emerging data standards combined with machine data collection methods, such as in-process verification lead the way to a complete paradigm shift from the traditional manufacturing and inspection to intelligent networked process control. Low-level G and M codes offer very limited information on machine capabilities or work piece characteristics which consequently, results in no information being available on manufacturing processes, inspection plans and work piece attributes in terms of tolerances, etc. and design features to computer numerically controlled (CNC) machines. One solution to the aforementioned problems is using STEP-NC (ISO 14649) suite of standards, which aim to provide higher-level information for process control. In this paper, the authors provide a definition for process control in CNC manufacturing and identify the challenges in achieving process control in current CNC manufacturing scenario. The paper then introduces a STEP-compliant framework that makes use of self-learning algorithms that enable the manufacturing system to learn from previous data and results in eliminating the errors and consistently producing quality products. The framework relies on knowledge discovery methods such as data mining encapsulated in a process analyser to derive rules for corrective measures to control the manufacturing process. The design for the knowledge-based process analyser and the various process control mechanisms conclude the paper.