Association rules mining in R for product performance management in industry 4.0

Lin, Hsiao-Kang; Hsieh, Cheng-Huan; Wei, Nai-Chieh; Peng, Yi-Chun

doi:10.1016/j.procir.2019.04.099

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…Apriori algorithm is one of the association rule mining algorithms that use the accuracy to determine the appropriate number of indices, it's used to discover the frequent itemset, this algorithm is easy and suitable to find the association rules and relations among the given dataset items [26]. The techniques of association rules are used to extract the hidden relations of the data and discover the rules among those items [18]. So, in each transaction data with multiple items, association rules try to find the rules that govern how or why such items often appear together.…”

Section: A) Association Rules Techniquesmentioning

confidence: 99%

“…Khakifirooz et al [17] developed a model that explore the complex semi-conductor manufacturing data for fault discovery to enable intelligent manufacturing, they developed a framework focused on Gibb's sampling and Bayesian inference and the using of the kappa coefficient algorithm of Cohen to eliminate the effect of foreign variables. Lin et al [18] used association rules, "Arules" package in R and Apriori algorithm improves the measure of preventing the failure from happening again and provide (predictive analyses) to improve product quality, the predictive enhancing the performance of the product and able to maximizes the value that has been captured by the product service system (PSS) offer, they applied these algorithms on "WBGA" product. Munirathinam and Ramadoss [19] conducted research in data analytics, the method of this analysis is modeled based on the CRISP-DM model, they used the Weka platform and R languages to introduce the proposed method and five other techniques of exploration of machine learning, and they built a decision model to help identify any faults in equipment to enhance the production process in manufacturing.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance

Shaaban

Khafagy

Elmasry

et al. 2022

IJEECS

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Recently due <span>to the explosion in the data field, there is a great interest in the data science areas such as big data, artificial intelligence, data mining, and machine learning. Knowledge gives control and power in numerous manufacturing areas. Companies, factories, and all organizations owners aim to benefit from their huge; recorded data that increases and expands very quickly to improve their business and improve the quality of their products. In this research paper, the knowledge discovery in databases (KDD) technique has been followed, “association rules” algorithms “Apriori algorithm”, and “chi-square automatic interaction detection (CHAID) analysis tree” have been applied on real datasets belonging to (Emisal factory). This factory annually loses tons of production due to the breakdowns that occur daily inside the factory, which leads to a loss of profit. After analyzing and understanding the factory product processes, we found some breakdowns occur a lot of days during the product lifecycle, these breakdowns affect badly on the production lifecycle which led to a decrease in sales. So, we have mined the data and used the mentioned methods above to build a predictive model that will predict the breakdown types and help the factory owner to manage the breakdowns risks by taking accurate actions before the breakdowns happen.</span>

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Section: A) Association Rules Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance

Shaaban

Khafagy

Elmasry

et al. 2022

IJEECS

View full text Add to dashboard Cite

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“…For rule A⇒B (antecedent →consequent), the Support, Confidence and Lift equations are the same presented in [52]:…”

Section: Generating Association Rules By Applying Apriori Algorithmmentioning

confidence: 99%

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

Mensouri

Azmani

2022

Applied Computer Systems

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In order to increase sales, companies try their best to develop relevant offers that anticipate customer needs. One way to achieve this is by leveraging artificial intelligence algorithms that process data collected based on customer transactions, extract insights and patterns from them, and then present them in a user-friendly way to human or artificial intelligence decision makers. This study is based on a hybrid approach, it starts with an online marketplace dataset that contains many customers’ purchases and ends up with global personalized offers based on three different datasets. The first one, generated by a recommendation system, identifies for each customer a list of products they are most likely to buy. The second is generated with an Apriori algorithm. Apriori is used as an associate rule mining technique to identify and map frequent patterns based on support, confidence, and lift factors, and also to pull important rules between products. The third and last one describes, for each customer, their purchase probability in the next few weeks, based on the BG/NBD model and the average of transactions using the Gamma-Gamma model, as well as the satisfaction based on the CLV and RFMTS models. By combining all three datasets, specific and targeted promotion strategies can be developed. Thus, the company is able to anticipate customer needs and generate the most appropriate offers for them while respecting their budget, with minimum operational costs and a high probability of purchase transformation.

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“…With modern industry and the presence of the Internet of Things (IoT), there is a huge number of sensors and, consequently, data [29][30][31]. The use of artificial intelligence methods to process large amounts of data efficiently is often invoked [32]. Predictive analytics is also used in mechanical manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Form of a Hardened Metal Workpiece during the Straightening Process

et al. 2023

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In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do.

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Association rules mining in R for product performance management in industry 4.0

Cited by 8 publications

References 8 publications

Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance

Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

Prediction of the Form of a Hardened Metal Workpiece during the Straightening Process

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