Affiliations: [a] School of Mechanical Engineering, Nanchang Institute of Technology, Nanchang, China
| [b] School of Mechatronics Engineering, Nanchang University, Nanchang, China
| [c] School of Economics and Management, Nanchang Hangkong University, Nanchang, China
Abstract: This research presents a novel model for optimizing process information in manufacturing steps through the utilization of Process Constituent Elements (PCE), with the aim of enhancing the effectiveness of product process information design. To achieve this objective, a systematic analysis is conducted on six dimensions: input, output, resources, value-adding activities, environment, and process control and inspection content. In addition, specific attributes of PCE are investigated, and an improved FP-growth algorithm is employed to extract the optimized structural expressions of typical PCE, thus determining specific expression requirements. The PCE and their attribute relationships are organized into modular mapping rules, resulting in an optimized representation structure based on a polychromatic set approach. The effectiveness of this approach is quantitatively assessed by developing a comprehensive quality indicator evaluation system for process information and using a fuzzy comprehensive evaluation model for analysis.
Keywords: Process constituent elements, process design, optimization, data mining, process quality, fuzzy evaluation
