A novel manufacturing defect detection method using association rule mining techniques

Chen, Wei-Chou; Tseng, Shian‐Shyong; Wang, Ching-Yao

doi:10.1016/j.eswa.2005.06.004

Cited by 74 publications

(30 citation statements)

References 16 publications

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“…Chen et al introduced a method using association rule mining techniques for identification of root-cause machine sets that, most likely, are sources of defective products [18]. Sadoyan used a kind of association rule based on the rough set theory for manufacturing process control [19].…”

Section: B Root Cause Analysismentioning

confidence: 99%

Root-Cause and Defect Analysis based on a Fuzzy Data Mining Algorithm

Sabet¹,

Moniri²,

Mohebbi³

2017

ijacsa

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Abstract-Manufacturing organizations have to improve the quality of their products regularly to survive in today's competitive production environment. This paper presents a method for identification of unknown patterns between the manufacturing process parameters and the defects of the output products and also of the relationships between the defects. Discovery of these patterns helps practitioners to achieve two main goals: first, identification of the process parameters that can be used for controlling and reducing the defects of the output products and second, identification of the defects that very probably have common roots. In this paper, a fuzzy data mining algorithm is used for discovery of the fuzzy association rules for weighted quantitative data. The application of the association rule algorithm developed in this paper is illustrated based on a net making process at a netting plant. After implementation of the proposed method, a significant reduction was observed in the number of defects in the produced nets.

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Section: B Root Cause Analysismentioning

confidence: 99%

Root-Cause and Defect Analysis based on a Fuzzy Data Mining Algorithm

Sabet¹,

Moniri²,

Mohebbi³

2017

ijacsa

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“…Algorithm showed the superiority over fuzzy decision technique and entropy based analysis method. Chen et al [150] generated association rules for defect detection in semiconductor manufacturing. They determined the association between different machines and their combination with defects to determine the defective machine.…”

Section: Association In Manufacturingmentioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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“…This approach, called single-point approach, treats manufacturing operation that is performed in every workstation as happened in a workstation. Various data mining technique such as classification [12], [14], clustering [3], and association rules [17], [18] have been employed to develop quality prediction model using this approach. Another approach is developing one prediction model for every workstation.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, various data mining techniques have been employed to develop quality prediction model from manufacturing historical dataset. For example, clustering [2], [3], classification [4][5][6][7][8][9][10][11][12][13][14][15][16], association rules [17], [18], and regression have been applied in various industries. These techniques were implemented in injection molding industry, semiconductor manufacturing, slider manufacturing, machining process, hard disk manufacturing, loudspeaker manufacturing, and food processing industry.…”

Section: Introductionmentioning

confidence: 99%

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

Arif¹,

Suryana²,

Hussin³

2013

IJCA

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Quality prediction model has been developed in various industries to realize the faultless manufacturing. However, most of quality prediction model is developed in single-stage manufacturing. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing effectively. This study is intended to propose combination of multiple PCA+ID3 algorithm to develop quality prediction model in MMS. This technique is applied to a semiconductor manufacturing dataset using the cascade prediction approach. The result shows that the combination of multiple PCA+ID3 is manage to produce the more accurate prediction model in term of classifying both positive and negative classes. General TermsData Mining, Prediction Model.

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A novel manufacturing defect detection method using association rule mining techniques

Cited by 74 publications

References 16 publications

Root-Cause and Defect Analysis based on a Fuzzy Data Mining Algorithm

Root-Cause and Defect Analysis based on a Fuzzy Data Mining Algorithm

Data mining in manufacturing: a review based on the kind of knowledge

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

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