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

An investigation of constraint-based risk management for collaborative design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the context of internationally challenging economic, design has been regarded as a key factor in assisting design and manufacturing companies to survive. By using up-to-date computer-supported technology, the global design collaboration based on multidisciplinary and distributed environment is becoming a mainstream to new product development (NPD). However, during the process of collaborative design, risk is rarely mentioned. In particular, due to the complexity of design process and lack of efficient design decision-making, there have been some design collaboration failures across multiple companies. Some design projects cannot deliver the benefits as companies have expected through the collaboration. Moreover, a number of stakeholders, managers and designers expressed their disappointment at not seeing the projected savings in cost and time, which critically discredited the value of design collaboration. Many studies in academia and commercial cases have suggested that risk assessment can be applied as an effective means in the realm of design. Nevertheless, few of them conducted risk management research associated with design constraints under a collaborative environment from both theoretical and practical perspectives. In current risk practice, many risk practitioners simply report key risks to their management teams and no further analysis, which might subsequently result in confusion with excessive discussions. Consequently, to prevent the failure of design collaboration and perform a satisfactory risk assessment, it is important to perform risk management with an upstream perspective and at an operational level. An approach, called constraint-based design risk management (DRM) where a conceptual framework has been proposed on the basis of collaborative design features, risk management process and Theory of Constraints (TOC). Moreover, a DRM matrix has been developed to map, measure and mitigate collaborative design risk through evaluating the critical design constraints, and then specified design risk variables in the light of risk criteria. Design constraints are quantitative parameters that frequently affect main design processes and decisions. The combination of design constraints and risk criteria can be accessible and applicable by designers and design mangers. In addition, a Bayesian weighting method based on Bayesian theorem has been developed to measure collaborative design risk in a more efficient manner. Ultimately, a DRM tool has been created as a simulated scenario prototype, which incorporated with three case-study evaluations, to demonstrate the importance and effectiveness of using TOC and risk theory in the realm of design collaboration

A framework for waste heat energy recovery within manufacturing

One third of energy consumption is attributed to the industrial sector, with as much as half ultimately wasted as heat. Consequently, research has focused on providing a framework for waste heat energy recovery within manufacturing. A decision support tool is hence developed based on the framework to deliver a streamlined and optimised heat recovery strategy. Substantial improvement in plant energy efficiency together with financial and environmental benefit from heat recovery has been demonstrated