机械加工车间质量管理系统的体系结构及应用开发
发布时间：2019-05-19 10:43
【摘要】：企业面对激烈的市场竞争，企业产品的质量是保证其在市场中成为具有核心竞争力的重要因素之一，由各加工工序质量的累积形成的质量问题是产品最终质量问题的重要组成部分，因此工序质量是产品最终质量优劣的关键之一。如何建立机加车间的质量管理体系，如何控制和提高生产过程中产品的质量，减少生产的实际结果与计划期望之间的差距，已成为当前产品质量控制领域的研究热点。论文在建立一种机加车间质量管理系统体系结构的基础上，对影响工序质量的基础数据进行分析，明确关键影响因素，确定因素权重，然后用神经网络技术进行质量预测，进而进行工序质量控制和反馈。主要研究内容包括以下几个方面： 首先，在深入分析机械加工制造企业的生产特点及其车间质量控制存在的问题的基础上，提出并建立了一种面向机械加工制造企业的六层车间质量管理系统体系结构，并形成了相应的运行流程。 其次，运用关联分析法确定影响工序质量的关键因素，根据生产过程中的基础数据对关键因素进行权重确定；用BP神经网络技术理论，在产品生产制造中的质量检测数据的基础上，，建立一种机械加工制造车间质量预测模型，然后依据产品的质量预测信息，对影响因子进行调整或者改变生产计划进行合理的生产，并根据生产结果对车间质量进行事后控制和评价。 最后，在车间质量控制模式以及质量预测模型的基础上，设计和开发了机械加工制造车间产品质量管理系统，作为制造执行系统的重要组成部分，在某机械加工制造企业车间产品质量管理中进行了具体应用。
[Abstract]:In the face of fierce market competition, the quality of enterprise products is one of the important factors to ensure that it becomes a core competitiveness in the market. The quality problem formed by the accumulation of the quality of each processing process is an important part of the final quality of the product, so the process quality is one of the keys to the final quality of the product. How to establish the quality management system of machine shop, how to control and improve the quality of products in the production process, and how to reduce the gap between the actual results of production and the planned expectations have become the research hotspots in the field of product quality control. On the basis of establishing an architecture of machine shop quality management system, this paper analyzes the basic data that affect the process quality, clarifies the key influencing factors, determines the factor weight, and then uses neural network technology to predict the quality. Then the process quality control and feedback are carried out. The main research contents include the following aspects: first of all, on the basis of in-depth analysis of the production characteristics of machining and manufacturing enterprises and the existing problems of workshop quality control, A six-tier workshop quality management system architecture for machining and manufacturing enterprises is proposed and established, and the corresponding operation flow is formed. Secondly, the key factors that affect the process quality are determined by correlation analysis, and the weight of the key factors is determined according to the basic data in the production process. Based on the quality detection data in product production and manufacturing, a quality prediction model of machining and manufacturing workshop is established by using BP neural network technology theory, and then according to the quality prediction information of the product, Adjust or change the production plan to carry on the reasonable production, and according to the production result to carry on the control and the appraisal to the workshop quality after the event. Finally, on the basis of workshop quality control mode and quality prediction model, the product quality management system of machining manufacturing workshop is designed and developed, which is an important part of manufacturing execution system. It has been applied in the product quality management of a machining and manufacturing enterprise workshop.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F426.4;F273
[Abstract]:In the face of fierce market competition, the quality of enterprise products is one of the important factors to ensure that it becomes a core competitiveness in the market. The quality problem formed by the accumulation of the quality of each processing process is an important part of the final quality of the product, so the process quality is one of the keys to the final quality of the product. How to establish the quality management system of machine shop, how to control and improve the quality of products in the production process, and how to reduce the gap between the actual results of production and the planned expectations have become the research hotspots in the field of product quality control. On the basis of establishing an architecture of machine shop quality management system, this paper analyzes the basic data that affect the process quality, clarifies the key influencing factors, determines the factor weight, and then uses neural network technology to predict the quality. Then the process quality control and feedback are carried out. The main research contents include the following aspects: first of all, on the basis of in-depth analysis of the production characteristics of machining and manufacturing enterprises and the existing problems of workshop quality control, A six-tier workshop quality management system architecture for machining and manufacturing enterprises is proposed and established, and the corresponding operation flow is formed. Secondly, the key factors that affect the process quality are determined by correlation analysis, and the weight of the key factors is determined according to the basic data in the production process. Based on the quality detection data in product production and manufacturing, a quality prediction model of machining and manufacturing workshop is established by using BP neural network technology theory, and then according to the quality prediction information of the product, Adjust or change the production plan to carry on the reasonable production, and according to the production result to carry on the control and the appraisal to the workshop quality after the event. Finally, on the basis of workshop quality control mode and quality prediction model, the product quality management system of machining manufacturing workshop is designed and developed, which is an important part of manufacturing execution system. It has been applied in the product quality management of a machining and manufacturing enterprise workshop.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F426.4;F273
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