装配车间过程质量管理的应用设计

Wang Yan, Z. Jianmin, Zheng Chao
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引用次数: 0

摘要

中国制造业发展迅速。质量管理已成为工业生产经营活动的重要环节。车间质量管理水平的高低对生产效率的提高有着深远的影响。利用信息化技术设计装配车间过程质量管理应用系统,解决质量管理过程中暴露的问题,对提高车间装配效率具有重要意义。在以前的车间装配过程中,离线信息传递效率低,容易出错。针对这一风险,本文开发了装配车间的过程质量管理体系,使车间的装配工作与质量管理工作协同工作。使信息能够迅速传递和响应。因此,该系统保证了产品的高质量、高效率生产。同时,构建了质量管理模型框架。并完成了生产线管理、进度效率分析、质检溯源等功能。实现了车间装配过程中作业人员、质量管理、计划、物料的高效协同作业的有机整合。在此基础上,完成了装配车间质量管理信息系统的设计,为系统的数字化集成、大数据处理和智能制造提供了有力支撑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application Design of Quality Management of Assembly Workshop Process
China's manufacturing industry is developing rapidly. Quality management has become an important link in industrial production and operation activities. The level of workshop quality management has a profound impact on the improvement of production efficiency. The use of information technology to design the quality management application system of the assembly workshop process and solve the problems exposed in the quality management process is of great significance to improve the assembly efficiency of the workshop. In the previous workshop assembly process, offline information transmission was inefficient and error-prone. In response to this risk, this article develops a process quality management system for the assembly workshop, which enables the assembly work and quality management work in the workshop to work together. so that the information can be quickly transmitted and responded to. Therefore, this system guarantees high-quality and high-efficiency production of products. At the same time, a quality management model framework was built. And functions such as production line management, schedule efficiency analysis, quality inspection traceability were completed. It has realized the organic integration of efficient and coordinated operations of operators, quality management, planning and materials in the workshop assembly process. Based on this, the design of the quality management information system for the assembly workshop was completed, which provided strong support for the digital integration of the system, big data processing and intelligent manufacturing.
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