Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

A brief network analysis of Artificial Intelligence publication

In this paper, we present an illustration to the history of Artificial Intelligence(AI) with a statistical analysis of publish since 1940. We collected and mined through the IEEE publish data base to analysis the geological and chronological variance of the activeness of research in AI. The connections between different institutes are showed. The result shows that the leading community of AI research are mainly in the USA, China, the Europe and Japan. The key institutes, authors and the research hotspots are revealed. It is found that the research institutes in the fields like Data Mining, Computer Vision, Pattern Recognition and some other fields of Machine Learning are quite consistent, implying a strong interaction between the community of each field. It is also showed that the research of Electronic Engineering and Industrial or Commercial applications are very active in California. Japan is also publishing a lot of papers in robotics. Due to the limitation of data source, the result might be overly influenced by the number of published articles, which is to our best improved by applying network keynode analysis on the research community instead of merely count the number of publish.Comment: 18 pages, 7 figure

Adaptive Heterogeneous Multi-Population Cultural Algorithm

Optimization problems is a class of problems where the goal is to make a system as effective as possible. The goal of this research area is to design an algorithm to solve optimization problems effectively and efficiently. Being effective means that the algorithm should be able to find the optimal solution (or near optimal solutions), while efficiency refers to the computational effort required by the algorithm to find an optimal solution. In other words, an optimization algorithm should be able to find the optimal solution in an acceptable time. Therefore, the aim of this dissertation is to come up with a new algorithm which presents an effective as well as efficient performance. There are various kinds of algorithms proposed to deal with optimization problems. Evolutionary Algorithms (EAs) is a subset of population-based methods which are successfully applied to solve optimization problems. In this dissertation the area of evolutionary methods and specially Cultural Algorithms (CAs) are investigated. The results of this investigation reveal that there are some room for improving the existing EAs. Consequently, a number of EAs are proposed to deal with different optimization problems. The proposed EAs offer better performance compared to the state-of-the-art methods. The main contribution of this dissertation is to introduce a new architecture for optimization algorithms which is called Heterogeneous Multi-Population Cultural Algorithm (HMP-CA). The new architecture first incorporates a decomposition technique to divide the given problem into a number of sub-problems, and then it assigns the sub-problems to different local CAs to be optimized separately in parallel. In order to evaluate the proposed architecture, it is applied on numerical optimization problems. The evaluation results reveal that HMP-CA is fully effective such that it can find the optimal solution for every single run. Furthermore, HMP-CA outperforms the state-of-the-art methods by offering a more efficient performance. The proposed HMP-CA is further improved by incorporating an adaptive decomposition technique. The improved version which is called Adaptive HMP-CA (A-HMP-CA) is evaluated over large scale global optimization problems. The results of this evaluation show that HMP-CA significantly outperforms the state-of-the-art methods in terms of both effectiveness and efficiency

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

Application of Soft Computing to Serial Manipulator Kinematic Problems

Inspired by the techniques in artificial intelligence, this research applies the constituent of artificial intelligence to perform manipulator positioning tasks. This work combines three methods in artificial intelligence: fuzzy rules, neural networks, and genetic algorithm to form the control block specifically designed to solve kinematic problems of a six degree-of-freedom serial manipulator. The direct kinematic of a manipulator is taken as the feed forward control and the artificial intelligence control block replaces the inverse kinematics when the reverse solving is done. Fine manipulator positioning is achieved from the learning stages executed by the artificial intelligence control block. It is shown experimentally that the technique proposed was capable of producing results with very low errors

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

A critical analysis of job shop scheduling in context of industry 4.0

Scheduling plays a pivotal role in the competitiveness of a job shop facility. The traditional job shop scheduling problem (JSSP) is centralized or semi-distributed. With the advent of Industry 4.0, there has been a paradigm shift in the manufacturing industry from traditional scheduling to smart distributed scheduling (SDS). The implementation of Industry 4.0 results in increased flexibility, high product quality, short lead times, and customized production. Smart/intelligent manufacturing is an integral part of Industry 4.0. The intelligent manufacturing approach converts renewable and nonrenewable resources into intelligent objects capable of sensing, working, and acting in a smart environment to achieve effective scheduling. This paper aims to provide a comprehensive review of centralized and decentralized/distributed JSSP techniques in the context of the Industry 4.0 environment. Firstly, centralized JSSP models and problem-solving methods along with their advantages and limitations are discussed. Secondly, an overview of associated techniques used in the Industry 4.0 environment is presented. The third phase of this paper discusses the transition from traditional job shop scheduling to decentralized JSSP with the aid of the latest research trends in this domain. Finally, this paper highlights futuristic approaches in the JSSP research and application in light of the robustness of JSSP and the current pandemic situation