Machine Learning-Driven Industrial Applications (机器学习驱动的工业应用)
Submission Deadline: 2025/8/31
Chair: 
Dazi Li, Beijing University of Chemical Technology, China
Keywords: | Topics: |
· Machine Learning (机器学习) · Intelligent Manufacturing (智能制造) · Predictive Maintenance (预测维护) · Quality Inspection (质量检测) · Production Optimization (生产优化) · Industrial Big Data (工业大数据) | · Industrial Big Data Analysis and Modeling (工业大数据分析与建模) · Predictive Maintenance and Fault Diagnosis (预测性维护与故障诊断) · Production Process Optimization and Scheduling (生产流程优化与调度) · Smart Manufacturing and Quality Inspection (智能制造与质量检测) |
Summary:
· As industrial digitalization and intelligence continue to advance, the application of machine learning across various manufacturing processes is becoming increasingly widespread. This special session will focus on four major themes: predictive maintenance and fault diagnosis, industrial big data analysis and modeling, production process optimization and scheduling, and smart manufacturing and quality inspection. It aims to explore how machine learning can offer more precise, efficient, and reliable solutions in equipment management, production processes, and quality control. By concentrating on cutting-edge technologies and practical case studies, we hope to build a bridge between academic research and industrial applications, jointly fostering innovation and advancement in the industrial sector.
· 随着工业数字化与智能化的深入推进，机器学习在各类制造环节中的应用日益广泛。本专题将围绕工业大数据分析与建模、预测性维护与故障诊断、生产流程优化与调度以及智能制造与质量检测四大主题展开讨论，旨在探讨机器学习如何在设备管理、生产流程和质量管控等方面带来更精准、更高效、更可靠的解决方案。通过聚焦前沿技术与实践案例，我们希望为学术研究与行业应用搭建桥梁，共同推动工业领域的创新与升级。