A novel framework for discovery and reuse of typical process route driven by symbolic entropy and intelligent...

Area of Science:

• Manufacturing Engineering
• Data Mining
• Operations Research

Background:

• Manufacturing enterprises generate vast amounts of data from production instances.
• Effective utilization of these instances is crucial for process improvement and innovation.

Purpose of the Study:

• To propose a novel framework for discovering and reusing typical manufacturing process routes.
• To enhance the scientific determination of reuse objects and increase flexibility in manufacturing.

Main Methods:

• Developed a similarity measurement method for machining process routes based on symbolic entropy.
• Devised a typical process route discovery method using an ant colony clustering model.
• Analyzed two reuse approaches for typical process routes.

Main Results:

• The symbolic entropy method accurately assessed similarity among machining process routes.
• The ant colony clustering model achieved adaptive clustering of process routes.
• An indirect reuse approach effectively supported the generation of new machining plans.

Conclusions:

• The proposed framework comprehensively addresses the retrieval and reuse of manufacturing instances.
• The framework effectively supports and improves process instance reuse in manufacturing.