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

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

Analyzing the Impact of Genetic Parameters on Gene Grouping Genetic Algorithm and Clustering Genetic Algorithm

Genetic Algorithms are stochastic randomized procedures used to solve search and optimization problems. Many multi-population and multi-objective Genetic Algorithms are introduced by researchers to achieve improved performance. Gene Grouping Genetic Algorithm (GGGA) and Clustering Genetic Algorithm (CGA) are of such kinds which are proved to perform better than Standard Genetic Algorithm (SGA). This paper compares the performance of both these algorithms by varying the genetic parameters. The results show that GGGA provides good solutions, even to large-sized problems in reasonable computation time compared to CGA and SGA. Keywords: Stochastic, randomized, multi-population, Gene Grouping Genetic Algorithm, Clustering Genetic Algorithm

GACN: Self-clustering genetic algorithm for constrained networks

Extending the lifespan of a wireless sensor network is a complex problem that involves several factors, ranging from device hardware capacity (batteries, processing capabilities, and radio efficiency) to the chosen software stack, which is often unaccounted for by the previous approaches. This letter proposes a genetic algorithm-based clustering optimization method for constrained networks that significantly improves the previous state-of-the-art results, while accounting for the specificities of the Internet engineering task force, Constrained RESTful Environment (CoRE), standards for data transmission and specifically relying on CoRE interfaces, which fit this purpose very well.info:eu-repo/semantics/publishedVersio

Optimized assembly design for resource efficient production in a multiproduct manufacturing system

Resource efficiency is one of the greatest challenges for sustainable manufacturing. Material flow in manufacturing systems directly influences resource efficiency, financial cost and environmental impact. A framework for material flow assessment in manufacturing systems (MFAM) was applied to a complex multi-product manufacturing case study. This supported the identification of options to alter material flow through changes to the product assembly design, to improve overall resource efficiency through eliminating resource intensive changeovers. Alternative assembly designs were examined using a combination of intelligent computation techniques: k-means clustering, genetic algorithm and ant colony algorithm. This provided recommendations balancing improvement potential with extent of process modification impact

Uncovering the physics of flapping flat plates with artificial evolution

We consider an experiment in which a rectangular flat plate is flapped with two degrees of freedom, and a genetic algorithm tunes its trajectory parameters so as to achieve maximum average lift force, thus evolving a population of trajectories all yielding optimal lift forces. We cluster the converged population by defining a dynamical formation number for a flapping flat plate, thus showing that optimal unsteady force generation is linked to the formation of a leading-edge vortex with maximum circulation. Force and digital particle image velocimetry measurements confirm this result

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Research on Genetic Algorithm and Data Information based on Combined Framework for Nonlinear Functions Optimization

AbstractIn recent years, piecewise linear change has become an attractive tools, used for all kinds of complicated nonlinear system. Piecewise linear individual function to provide the platform segmental affine nonlinear system contains a large amount of counter approximate nonlinear function value. Even if section of linearization method widely used the best approximation of the nonlinear function of continuous time a minimum number of piecewise functions did not mention liveried with appropriate literature. This paper presents a method of optimization based on clustering evolution get optimal piecewise linear approximation of a class of nonlinear function. The technology is based on the balance between the approximate precision and simplified, and improves the approximate Linear A minimum number of department. The technology has been successfully applied in some common nonlinear function