Uses and applications of artificial intelligence in manufacturing

The purpose of the THESIS is to provide engineers and personnels with a overview of the concepts that underline Artificial Intelligence and Expert Systems. Artificial Intelligence is concerned with the developments of theories and techniques required to provide a computational engine with the abilities to perceive, think and act, in an intelligent manner in a complex environment. Expert system is branch of Artificial Intelligence where the methods of reasoning emulate those of human experts. Artificial Intelligence derives it\u27s power from its ability to represent complex forms of knowledge, some of it common sense, heuristic and symbolic, and the ability to apply the knowledge in searching for solutions. The Thesis will review : The components of an intelligent system, The basics of knowledge representation, Search based problem solving methods, Expert system technologies, Uses and applications of AI in various manufacturing areas like Design, Process Planning, Production Management, Energy Management, Quality Assurance, Manufacturing Simulation, Robotics, Machine Vision etc. Prime objectives of the Thesis are to understand the basic concepts underlying Artificial Intelligence and be able to identify where the technology may be applied in the field of Manufacturing Engineering

A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision-making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision-making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%

Integration of human behavioral aspects in a dynamic model for a manufacturing system

International audienceThe computational simulation of human intelligent behavior has been one of the main research topics in (AI) artificial intelligence domain. Therefore, a great number of behavioral models were proposed considering emotional, cognitive and psychological factors to simulate the human behavior in different domain such as military or manufacturing systems. In addition to psychological factors, the social state of a group of workers plays a critical role in rational decision-making, perception, human interaction and human intelligence. Thus, it is judicious to analyze the workers' behavior at work and to integrate their needs and requirements in manufacturing systems models in order to improve the simulation accuracy. In this context, this paper suggests a graphical and a mathematical representation model of workers' behaviors as well as the ties that can exist among them. The model is also extended to consider inter-worker social relations that can influence the individual performance

Job Shop Scheduling Problem: an Overview

The Job-shop scheduling is one of the most important industrial activities, especially in manufacturing planning. The problem complexity has increased along with the increase in the complexity of operations and product-mix. To solve this problem, numerous approaches have been developed incorporating discrete event simulation methodology. The scope and the purpose of this paper is to present a survey which covers most of the solving techniques of Job Shop Scheduling (JSS) problem. A classification of these techniques has been proposed: Traditional Techniques and Advanced Techniques. The traditional techniques to solve JSS could not fully satisfy the global competition and rapidly changing in customer requirements. Simulation and Artificial Intelligence (AI) have proven to be excellent strategic tool for scheduling problems in general and JSS in particular. The paper defined some AI techniques used by manufacturing systems. Finally, the future trends are proposed briefly

Manufacturing planning and operations optimisation for mass customisation manufacturing using computational intelligence.

Doctorate of Philosophy in Engineering (Mechanical). University of KwaZulu-Natal, Durban 2015.This study determined whether an Advanced Manufacturing System could be optimised, more effectively than by traditional methods, using new and novel computational intelligence techniques. An Advanced Manufacturing System can be described as highly automated and highly complex systems that strive for global competitiveness. In the context of this study, these systems aim to compete in a Mass Customisation Manufacturing market. Traditional optimisation methods refer to methods based on mathematical models, experience, or industry best practice. Computational Intelligence refers to computational methods inspired by natural systems and processes. This includes, but is not limited to, evolutionary intelligence, Artificial Neural Networks, swarm intelligence, and fuzzy systems. This study investigated the optimisation of the manufacturing system from both a planning and an operations perspective. Research was carried out to identify Computational Intelligence paradigms and algorithms for Advanced Manufacturing System planning and operations optimisation. Static and dynamic simulation models of an Advanced Manufacturing System, for the respective perspectives, have been developed in order to simulate a manufacturing system designed to produce a hypothetical range of customisable men’s wristwatches on a mass scale at a competitive cost. A new Biogeography-Based Optimisation algorithm was developed to optimise an aggregate production plan using static simulation models. This algorithm was implemented to find the lowest production cost for the wristwatch production system case study. This algorithm produced a lower cost plan than a Simulated Annealing algorithm with a lower impact on workforce. A new Distributed Dynamic Selection Rule Strategy was developed for optimising production scheduling using dynamic simulation models. This new strategy was inspired by the Harmony Search principle and was based on traditional selection rules for scheduling. This strategy was able to produce statistically significantly lower average order lead times than three out of four traditional selection rules tested

Towards smart layout design for a reconfigurable manufacturing system

Global competition and increased variety in products have created challenges for manufacturing companies. One solution to handle the variety in production is to use reconfigurable manufacturing systems (RMS). These are modular systems where machines can be rearranged depending on what is being manufactured. However, implementing a rearrangeable system drastically increases complexity, among which one challenge with RMS is how to design a new layout for a customized product in a highly autonomous and responsive fashion, known as the layout design problem. In this paper, we combine several Industry 4.0 technologies, i.e., IIoT, digital twin, simulation, advanced robotics, and artificial intelligence (AI), together with optimization to create a smart layout design system for RMS. The system automates the layout design process of RMS and removes the need for humans to design a new layout of the system

Using Semantic Web Services for AI-Based Research in Industry 4.0

The transition to Industry 4.0 requires smart manufacturing systems that are easily configurable and provide a high level of flexibility during manufacturing in order to achieve mass customization or to support cloud manufacturing. To realize this, Cyber-Physical Systems (CPSs) combined with Artificial Intelligence (AI) methods find their way into manufacturing shop floors. For using AI methods in the context of Industry 4.0, semantic web services are indispensable to provide a reasonable abstraction of the underlying manufacturing capabilities. In this paper, we present semantic web services for AI-based research in Industry 4.0. Therefore, we developed more than 300 semantic web services for a physical simulation factory based on Web Ontology Language for Web Services (OWL-S) and Web Service Modeling Ontology (WSMO) and linked them to an already existing domain ontology for intelligent manufacturing control. Suitable for the requirements of CPS environments, our pre- and postconditions are verified in near real-time by invoking other semantic web services in contrast to complex reasoning within the knowledge base. Finally, we evaluate our implementation by executing a cyber-physical workflow composed of semantic web services using a workflow management system.Comment: Submitted to ISWC 202