Work

Closed-Loop Feedback Mechanism for Recommending Quality Optimization Solutions for Smart Manufacturing Systems

Year: 2025

Type: conference-paper

Abstract: This study presents an integrated approach to quality optimization in smart manufacturing systems (SMSs) through the development of an evolutionary quality prediction framework. The proposed framework introduces a three-tiered closed-loop feedback architecture for smart quality optimization, comprising: (1) real-time quality prediction modeling, (2) autonomous defect classification through machine learning algorithms, and (3) adaptive recommendation of quality solutions. We establish rigorous methodological frameworks for each functional module, including multivariate time-series forecasting techniques for quality prediction, deep learning-based pattern recognition for defect categorization, and knowledge graph-driven decision support for optimization strategies. This innovative mechanism demonstrates significant potential for enhancing manufacturing quality consistency while substantially reducing operational costs through predictive maintenance and smart process adjustments.

Source: International Conference on Control and Intelligent Robotics (ICCIR)

DOI: 10.1145/3757940.3757976

Authors: Yuanju Qu, Xianghua Chu, Shenghui Cheng

Institutions: Shenzhen University, Southern University of Science and Technology, Jinan University

Language: English

Topic: Digital Transformation in Industry

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funders: Natural Science Foundation of Shenzhen City, National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province