Application of association rules in woven wire mesh defects analysis

Wongwan, Kritsada; Laosiritaworn, Wimalin

doi:10.1109/icitm.2018.8333970

“…This algorithm works by first compressing the database representing frequently occurring items into the FP tree, storing all critical information, and dividing the compressed database into a set of conditional databases (Wu et al, 2008). FP-Growth has been used in several cases to identify strong relationships between different defects using an extensive manufacturing database (Wongwan & Laosiritaworn, 2018). 5) At the evaluation stage, the evaluation of the model was carried out based on the level of accuracy obtained and further analysis of the main causes using Ishikawa and FMEA table.…”

Section: Methodsmentioning

confidence: 99%

Quality Improvement of NH1X36B Pre-Printed Box with QM-CRISP DM Approach at PT X

Habyba

¹

,

Fitriana

²

,

Theodora

³

2021

OE

0

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PT X is a manufacturer of cardboard box whose products are indispensable for various fields. The problem identified in NH1X36B pre-printed box, which is a shoes box, is the high defect rate that exceed company target of 2%. This study aims to reduce the defect rate of the product. The Quality-Management (QM) and Cross Industry Standard Process for Data Mining (CRISP-DM) approach was conducted by integrating Six Sigma and data mining. The Business Understanding phase was intended to define business and data mining objectives, SIPOC (Supplier-Input-Process-Output-Customer) diagram, and Critical-to-Quality (CTQ). In Data Understanding phase, it is known that the Defects Per Million Opportunities (DPMO) value is 1210.12. Data preparation phase was carried out with data cleaning, reduction, and discretization. Based on the Modeling result using Decision Tree C4.5 and FP-Growth algorithm, it is known that the dominant attributes causing high rejection are smeared ink, white spots, uneven varnish, and delamination. Decision Tree model accuracy of 90.24% indicates that the model is performing well. Analysis using FMEA yielded priority correction to the causes of smeared ink, uneven varnish, and delamination. Process improvement in Deployment phase was the application of plate cleaning and mounting form, printing process checklist, and SOP for sheet inspection. The improvement plans managed to improve the quality by rising sigma level from 4.533 to 4.648 sigma and decrease defect rate to 1.559%.

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“…A common approach to analyze monitoring data coming from different production processes is to apply association rule mining, a data mining technique which looks for patterns in big data [2][3][4][5][6][7]. Association rules aid in identifying operation modes leading to higher rates of flawed items [3,4], finding correlations between various types of defects [5], and predicting the output of the finished product [6].…”

Section: Introductionmentioning

confidence: 99%

“…A common approach to analyze monitoring data coming from different production processes is to apply association rule mining, a data mining technique which looks for patterns in big data [2][3][4][5][6][7]. Association rules aid in identifying operation modes leading to higher rates of flawed items [3,4], finding correlations between various types of defects [5], and predicting the output of the finished product [6]. Applying association rule mining in aluminum production makes it possible to reveal patterns across the values of the controlled parameters which are indicative of process disruptions and predict their occurrence in the future.…”

Section: Introductionmentioning

confidence: 99%

Application of Association Rule Mining to Detect “Spike” Disruptions in Aluminum Production

2021

Short Paper Proceedings of the 2nd Siberian Scientific Workshop on Data Analysis Technologies With Applications 2021

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The article focuses on applying association rule mining to predict the "Anode spike"-type process disruptions using daily average monitoring data from a series of reduction cells in the experimental area of the Sayanogorsk Aluminum Smelter. The data were binarized according to different criteria for grouping the values of the process parameters into ranges: statistical norms, quartiles, and ranges which are attributed to the occurrence of disruptions. Prediction models were built as a set of association rules. The quality metrics aided in defining the optimum parameters to be used in the model settings, with the results of its validation presented as well. The model selected for the implementa-tion uses binarization based on quartiles. The resulting validation values suggest that the model is effective enough for practical use.

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“…Для анализа данных мониторинга различных производственных процессов широко применяется технология ассоциативных правил (Association rule mining) -метод интеллектуального анализа данных направленный на поиск паттернов в больших объемах данных [2][3][4][5][6][7]. С помощью ассоциативных правил идентифицируют режимы работы, ведущие к повышенному проценту бракованных изделий [3,4], устанавливают взаимосвязи между типами дефектов [5], прогнозируют объемы выхода готового продукта [6].…”

unclassified

“…Для анализа данных мониторинга различных производственных процессов широко применяется технология ассоциативных правил (Association rule mining) -метод интеллектуального анализа данных направленный на поиск паттернов в больших объемах данных [2][3][4][5][6][7]. С помощью ассоциативных правил идентифицируют режимы работы, ведущие к повышенному проценту бракованных изделий [3,4], устанавливают взаимосвязи между типами дефектов [5], прогнозируют объемы выхода готового продукта [6]. Применение ассоциативных правил для анализа данных производства алюминия позволяет установить характерные сочетания значений контролируемых параметров, при которых возникают технологические нарушения и прогнозировать их возникновение в дальнейшем.…”

unclassified

Using Tools of Intellectual Analysis In Area Safety Management

2021

Short Paper Proceedings of the 2nd Siberian Scientific Workshop on Data Analysis Technologies With Applications 2021

0

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Application of association rules in woven wire mesh defects analysis

Cited by 6 publications

References 6 publications

Quality Improvement of NH1X36B Pre-Printed Box with QM-CRISP DM Approach at PT X

Quality Improvement of NH1X36B Pre-Printed Box with QM-CRISP DM Approach at PT X

Application of Association Rule Mining to Detect “Spike” Disruptions in Aluminum Production

Using Tools of Intellectual Analysis In Area Safety Management

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