{"title":"Determination of Factors and Quality Control of Car Painting Based on FMEA and SPC.V2","authors":"M. Rana, Xinmin Zhang, Sayed Abdul Akher","doi":"10.4236/MME.2018.82011","DOIUrl":null,"url":null,"abstract":"The global automotive industry demands world-class levels of product quality, productivity, and competitiveness as well as continual improvement. To achieve this goal, many vehicle manufacturers companies use quality control tools to improve the quality of the product with zero defects and highly satisfied to the customer. Nowadays, there are a lot of quality tools applied to solve the problem quickly but it’s still the fact to find out good and efficient solving way. The study emphasizes the identification of potential failure which failures may have encountered in the production process and it will lead to car scrap, rework and influence of the internal production and quality target. After the complete study of the manufacturing process and production data-failure causes, failure rate and data etc. FMEA discover the weak processes in the form of higher risk priority number in the manufacturing of the car painting process, which required reducing by identifying and implementing of the defects and this will improve the process quality of the painting surface of the car. To analysis, the improvement of the car surface defects SPC (Statistical Process Control) tools are more efficient where can easily visible the defects trends. SPC chart is chronological graphs of process data that are used in manufactures industries to help understand, control and improve the process and that although based on statistical theory area easy for practitioners to use and interpret. In order to orient goal of zero defects of the car, surface uses the PFMEA technique to prioritize the defects and statistically analyze the roots cause of the defect and control the defects through continues improvements process.","PeriodicalId":69007,"journal":{"name":"现代机械工程(英文)","volume":"08 1","pages":"158-177"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"现代机械工程(英文)","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.4236/MME.2018.82011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The global automotive industry demands world-class levels of product quality, productivity, and competitiveness as well as continual improvement. To achieve this goal, many vehicle manufacturers companies use quality control tools to improve the quality of the product with zero defects and highly satisfied to the customer. Nowadays, there are a lot of quality tools applied to solve the problem quickly but it’s still the fact to find out good and efficient solving way. The study emphasizes the identification of potential failure which failures may have encountered in the production process and it will lead to car scrap, rework and influence of the internal production and quality target. After the complete study of the manufacturing process and production data-failure causes, failure rate and data etc. FMEA discover the weak processes in the form of higher risk priority number in the manufacturing of the car painting process, which required reducing by identifying and implementing of the defects and this will improve the process quality of the painting surface of the car. To analysis, the improvement of the car surface defects SPC (Statistical Process Control) tools are more efficient where can easily visible the defects trends. SPC chart is chronological graphs of process data that are used in manufactures industries to help understand, control and improve the process and that although based on statistical theory area easy for practitioners to use and interpret. In order to orient goal of zero defects of the car, surface uses the PFMEA technique to prioritize the defects and statistically analyze the roots cause of the defect and control the defects through continues improvements process.
全球汽车行业要求产品质量、生产力和竞争力达到世界级水平,并不断改进。为了实现这一目标,许多汽车制造商公司使用质量控制工具来提高产品的质量,使其零缺陷,并使客户高度满意。如今,有很多高质量的工具可以用来快速解决问题,但找到好的、有效的解决方法仍然是事实。该研究强调识别生产过程中可能遇到的潜在故障,这些故障将导致汽车报废、返工以及对内部生产和质量目标的影响。在对制造工艺和生产数据进行全面研究后,故障原因、故障率和数据等FMEA发现了汽车涂装工艺制造中风险优先级较高的薄弱环节,需要通过识别和实施缺陷来减少,这将提高汽车涂装表面的工艺质量。分析认为,改进后的汽车表面缺陷SPC(Statistical Process Control,统计过程控制)工具效率更高,可以很容易地看到缺陷的发展趋势。SPC图表是制造业中使用的过程数据的时序图,有助于理解、控制和改进过程,尽管它基于统计理论,但从业者很容易使用和解释。为了确定汽车零缺陷的目标,surface采用PFMEA技术对缺陷进行优先级排序,统计分析缺陷的根本原因,并通过持续改进过程控制缺陷。