Development of an intelligent quality management system using fuzzy association rules

Lau, Henry C. W.; Ho, G.T.S.; Chu, K. F.; Ho, William; Lee, C. K. M.

doi:10.1016/j.eswa.2007.12.066

Cited by 43 publications

(16 citation statements)

References 13 publications

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“…Artificial neural networks were used for addressing quality control issue as a non-conventional way to detect surface faults in mechanical front seals, which achieved good results in comparison with the deterministic system which was already implemented [15]. Fuzzy association rules were used to develop an intelligent quality management approach with the research providing a generic methodology with knowledge discovery and the cooperative ability for monitoring the process effectively and efficiently [16]. An automatic optical inspection was adopted for on-line measurement of small components on the eyeglasses assembly line, which was designed to be used at the beginning of the assembly line and is based on artificial vision, exploits two CCD cameras and an anthropomorphic robot to inspect and manipulate the objects [17].…”

Section: Literature Reviewmentioning

confidence: 99%

E-quality control: A support vector machines approach

Tseng

Aleti

et al. 2015

Journal of Computational Design and Engineering

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James) Kwon, E-quality control: A support vector machines approach, Journal of Computational Design and Engineering, http://dx. AbstractThe automated part quality inspection poses many challenges to the engineers, especially when the part features to be inspected become complicated. A large quantity of part inspection at a faster rate should be relied upon computerized, automated inspection methods, which requires advanced quality control approaches. In this context, this work uses innovative methods in remote part tracking and quality control with the aid of the modern equipment and application of support vector machine (SVM) learning approach to predict the outcome of the quality control process. The classifier equations are built on the data obtained from the experiments and analyzed with different kernel functions. From the analysis, detailed outcome is presented for six different cases. The results indicate the robustness of support vector classification for the experimental data with two output classes.

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Section: Literature Reviewmentioning

confidence: 99%

E-quality control: A support vector machines approach

Tseng

Aleti

et al. 2015

Journal of Computational Design and Engineering

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“…H.C.W Lau and al. developed an intelligent QMS using the fuzzy association rules [5]. Indeed, the objectives are effectively attainted, if the activities are managed by processes within a system approach where the processes inter act.…”

Section: Introductionmentioning

confidence: 99%

A new MAS based approach modeling the QMS continual improvement

Hajer

Taieb

Ketata

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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The purpose of this paper is to model a Quality Management System (QMS) based on Multi-Agent System (MAS). Our model has been carried out through three phases; first an identifying process of ISO 9000, ISO 9001 and ISO 9004 standard has been performed, then a brief recall on the MAS has been realized. Finally, we propose a new approach that models a QMS throw MAS. The different steps of the proposed approach cover the whole PDCA (Plan, Do, Check, Act) scheme.

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“…Article [18] is devoted to the development of the computerized control system by maintenance service intended for the decision analysis, allowing revealing the reasons of failures in a system, in account of various criteria, such as time and frequency of idle times, availability of spare parts and others. The described technologies are realized in the form of DMSS for quality management [19,20], and also for industrial systems and processes management at all stages of production life cycle [21].…”

Section: The Decision Support System As An Effective Instrument Of Sementioning

confidence: 99%

Improving the system of warranty service of trucks in foreign markets

Makarova¹,

Khabibullin²,

Belyaev³

et al. 2017

Transport Problems

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Summary. The article is devoted to the practical methods development of improving one of the ways to ensure the health of trucks in the system warranty. Methodology is aimed at optimizing the processes of formation of warranty spare parts kits during the implementation of KAMAZ trucks in foreign markets. The example is given to demonstrate the importance of different factors in the formation of the warranty set for different regions. The algorithm was developed to assess the qualitative composition formed warranty package that will optimize the planning and organization of activity centers to improve operational reliability of commercial trucks. A basis for acceptance of the scientifically-proved decisions is the statistical data analysis of requests monitoring that allows organizing duly replacement of parts with expired service life, and also promotes customer servicing quality improvement and reliability of trucks by prevention of its failures. СОВЕРШЕНСТВОВАНИЕ СИСТЕМЫ ГАРАНТИЙНОГО ОБСЛУЖИВАНИЯ ГРУЗОВОЙ АВТОМОБИЛЬНОЙ ТЕХНИКИ НА ЗАРУБЕЖНЫХ РЫНКАХ Аннотация.Статья посвящена разработке практической методики совершенствования одного из направлений обеспечения поддержания работоспособности автомобильной техники в системе гарантийного обслуживания Методика направлена на оптимизацию процессов формирования гарантийных комплектов запасных частей при реализации автомобильной техники КАМАЗ на зарубежных рынках. Приводится пример, демонстрирующий важность учета различных факторов при формировании состава гарантийного комплекта для различных регионов. Разработан алгоритм, позволяющий оценить качественный состав сформированного гарантийного комплекта, что будет способствовать оптимизации планирования и организации деятельности автоцентров по повышению эксплуатационной надежности грузовой автомобильной техники. Основой для принятия научно-обоснованных решений является статистический анализ данных мониторинга обращений, что позволяет организовать своевременную замену деталей, исчерпавших свой ресурс, а также способствует повышению качества обслуживания клиентов и надежности автомобильной техники путем предотвращения ее отказов.

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Development of an intelligent quality management system using fuzzy association rules

Cited by 43 publications

References 13 publications

E-quality control: A support vector machines approach

E-quality control: A support vector machines approach

A new MAS based approach modeling the QMS continual improvement

Improving the system of warranty service of trucks in foreign markets

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