Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Abstract

Abstract-Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms.A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0.

Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review

“…The second study showed how smart labels can provide identification, tracking, sensing, event detection, and interaction to human-centered Industry 4.0 applications. Emerging research topics include machine learning algorithms to identify technical defects in the manufacturing sector (such as to identify relationships that can be harnessed to preempt electrical defects at downline inspection stations) [45], genetic algorithms to predict customer needs [46], and robot actions using a multi-platform software application [47]. A very nice application was proposed by Zakhama et al [48].…”

The role of big data analytics in industrial Internet of Things

“…Clustering-based big data optimisation is another approach whereby kmeans clustering algorithms are used to cluster the attributes from customer data. The produced clusters are used to intelligently improve the design process in the product life cycle [71]. Another alternate for massive product customisation is the adoption of cloud-based manufacturing systems whereby big data integration is performed in cloud computing systems [72].…”

Business analytics in Industry 4.0: A systematic review

“…Other studies used more advanced Deep Learning Neural Networks (Choi et al, 2017; Essien & Giannetti, 2020; H. Kuo & Faricha, 2016; W. J. Lee et al, 2019; Maggipinto et al, 2018). Furthermore, some of the surveyed Preditive Analytics used optimization techniques (e.g., Genetic Algorithm, Particle Swarm Optimization) (Rosli et al, 2019; Saldivar, Goh, Li, Chen, et al, 2016; Saldivar, Goh, Li, Yu, et al, 2016), while other works focused on outliers detection and statistical analysis (Albers et al, 2017; Stein et al, 2016).…”