Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Semiconductor manufacturing simulation design and analysis with limited data

This paper discusses simulation design and analysis for Silicon Carbide (SiC) manufacturing operations management at New York Power Electronics Manufacturing Consortium (PEMC) facility. Prior work has addressed the development of manufacturing system simulation as the decision support to solve the strategic equipment portfolio selection problem for the SiC fab design [1]. As we move into the phase of collecting data from the equipment purchased for the PEMC facility, we discuss how to redesign our manufacturing simulations and analyze their outputs to overcome the challenges that naturally arise in the presence of limited fab data. We conclude with insights on how an approach aimed to reflect learning from data can enable our discrete-event stochastic simulation to accurately estimate the performance measures for SiC manufacturing at the PEMC facility

Semantic web service architecture for simulation model reuse

COTS simulation packages (CSPs) have proved popular in an industrial setting with a number of software vendors. In contrast, options for re-using existing models seem more limited. Re-use of simulation component models by collaborating organizations is restricted by the same semantic issues however that restrict the inter-organization use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontology to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development of ontology, connector software and web service discovery architecture in order to understand how such ontology are created, maintained and subsequently used for simulation model reuse. The ontology is extracted from health service simulation - comprising hospitals and the National Blood Service. The ontology engineering framework and discovery architecture provide a novel approach to inter- organization simulation, uncovering domain semantics and adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community

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

A Web Services Component Discovery and Deployment Architecture for Simulation Model Reuse

CSPs are widely used in industry, although have yet to operate across organizational boundaries. Reuse across organizations is restricted by the same semantic issues that restrict the inter-organization use of web services. The current representations of web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging semantic web. Semantic models, in the form of ontology, utilized by web service discovery and deployment architecture provide one approach to support simulation model reuse. Semantic interoperation is achieved through the use of simulation component ontology to identify required components at varying levels of granularity (including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The paper presents the development carried out within CSPI-PDG and Fluidity Group at Brunel University, of an ontology, connector software and web service discovery architecture. The ontology is extracted from simulation scenarios involving airport, restaurant and kitchen service suppliers. The ontology engineering framework and discovery architecture provide a novel approach to inter-organization simulation, adopting a less intrusive interface between participants. Although specific to CSPs the work has wider implications for the simulation community