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

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Numerical modelling and simulation in sheet metal forming

The application of numerical modelling and simulation in manufacturing technologies is looking back over about a 20–30 years history. In recent years, the role of modelling and simulation in engineering and in manufacturing industry has been continuously increasing. It is well known that during manufacturing processes simultaneous the effect of many different parameters can be observed. This is the reason why in former years, detailed analysis of manufacturing processes could have been done only by time-consuming and expensive trial-and-error methods. Due to the recent developments in the methods of modelling and simulation, as well as in computational facilities, modelling and simulation has become an everyday tool in engineering practice. Besides the aforementioned facts, the emerging role of modelling and simulation can also be explained by the growing globalisation and competition of the world market requiring shorter lead times and more cost effective solutions. In spite the enormous development of hardware and software facilities, the exclusive use of numerical modelling still seems to be very time- and cost consuming, and there is still often a high scepticism about the results among industrialists. Therefore, the purpose of this paper is to overview the present situation of numerical modelling and simulation in sheet metal forming, mainly from the viewpoint of scientific research and industrial applications

VisIVO - Integrated Tools and Services for Large-Scale Astrophysical Visualization

VisIVO is an integrated suite of tools and services specifically designed for the Virtual Observatory. This suite constitutes a software framework for effective visual discovery in currently available (and next-generation) very large-scale astrophysical datasets. VisIVO consists of VisiVO Desktop - a stand alone application for interactive visualization on standard PCs, VisIVO Server - a grid-enabled platform for high performance visualization and VisIVO Web - a custom designed web portal supporting services based on the VisIVO Server functionality. The main characteristic of VisIVO is support for high-performance, multidimensional visualization of very large-scale astrophysical datasets. Users can obtain meaningful visualizations rapidly while preserving full and intuitive control of the relevant visualization parameters. This paper focuses on newly developed integrated tools in VisIVO Server allowing intuitive visual discovery with 3D views being created from data tables. VisIVO Server can be installed easily on any web server with a database repository. We discuss briefly aspects of our implementation of VisiVO Server on a computational grid and also outline the functionality of the services offered by VisIVO Web. Finally we conclude with a summary of our work and pointers to future developments

Simulation-based optimisation of maintenance systems: Industrial case studies

Investigating the optimum blend of maintenance strategies for a given manufacturing system is a continuing concern amongst maintenance academics and professionals. Recent evidence suggests that little research is conducted on the simulation optimisation of maintenance in industrial systems. This study was designed to make an important contribution to the field of simulation-based optimisation of maintenance by presenting two empirical case studies: a tyre re-treading factory and a petro-chemical plant. It is one of the first to optimise various maintenance strategies simultaneously with their parameters in industrial manufacturing systems while considering production dynamics. Stochastic Discrete Event Simulation models were developed and connected to a Multi-Objective Optimisation engine. Various maintenance strategies were investigated including Corrective Maintenance, Preventive Maintenance, Opportunistic Maintenance and Condition-Based Maintenance. The results of this research suggest that over-looking the optimisation of maintenance on the strategic level may lead to sub-optimal solutions. In addition, it appears that traditional trade-offs between maintenance cost and production throughput are not present in some maintenance systems. This is an interesting observation that requires further investigation and experimentation

Inverse determination of constitutive equations and cutting force modelling for complex tools using oxley's predictive machining theory

In analysis of machining processes, finite element analysis is widely used to predict forces, stress distributions, temperatures and chip formation. However, constitutive models are not always available and simulation of cutting processes with complex tool geometries can lead to extensive computation time. This article presents an approach to determine constitutive parameters of the Johnson-Cook's flow stress model by inverse modelling as well as a methodology to predict process forces and temperatures for complex three-dimensional tools using Oxley's machining theory. In the first part of this study, an analytically based computer code combined with a particle swarm optimization (PSO) algorithm is used to identify constitutive models for 70MnVS4 and an aluminium-alloyed ultra-high-carbon steel (UHC-steel) from orthogonal milling experiments. In the second part, Oxley's predictive machining theory is coupled with a multi-dexel based material removal model. Contact zone information (width of cut, undeformed chip thickness, rake angle and cutting speed) are calculated for incremental segments on the cutting edge and used as input parameters for force and temperature calculations. Subsequently, process forces are predicted for machining using the inverse determined constitutive models and compared to actual force measurements. The suggested methodology has advantages regarding the computation time compared to finite element analyses.BMBF/02PN205

Simulation-based optimisation of complex maintenance systems

There is a potential as well as a growing interest amongst researchers to utilise simulation in optimising maintenance systems. The state of the art in simulation-based optimisation of maintenance was established by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. In general, approaches to optimise maintenance varied significantly in the literature. Overall, these studies highlight the need for a framework that unifies the approach to optimising maintenance systems. Framework requirements were established through two main sources of published research. Surveys on maintenance simulation optimisation were examined to document comments on the approaches authors follow while optimising maintenance systems. In addition, advanced and future maintenance strategies were documented to ensure it can be accommodated in the proposed framework. The proposed framework was developed using a standard flowchart tool due to its familiarity and ability to depict decision structures clearly. It provides a systematic methodology that details the steps required to connect the simulation model to an optimisation engine. Not only it provides guidance in terms of formulating the optimal problem for the maintenance system at hand but it also provides support and assistance in defining the optimisation scope and investigating applicable maintenance strategies. Additionally, it considers current issues relating to maintenance systems both in research and in practice such as uncertainty, complexity and multi-objective optimisation. The proposed framework cannot be applied using existing approaches for modelling maintenance. Existing modelling approaches using simulation have a number of limitations: The maintenance system is modelled separately from other inter-related systems such as production and spare parts logistics. In addition, these approaches are used to model one maintenance strategy only. A novel approach for modelling maintenance using Discrete Event Simulation is proposed. The proposed approach enables the modelling of interactions amongst various maintenance strategies and their effects on the assets in non-identical multi-unit systems. Using the proposed framework and modelling approach, simulation-based optimisation was conducted on an academic case and two industrial cases that are varied in terms of sector, size, number of manufacturing processes and level of maintenance documentation. Following the structured framework enabled discussing and selecting the suitable optimisation scope and applicable maintenance strategies as well as formulating a customised optimal problem for each case. The results of the study suggest that over-looking the optimisation of maintenance strategies may lead to sub-optimal solutions. In addition, this research provides insights for non-conflicting objectives in maintenance systems

Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

© 2019 by SAGE Publications Ltd.Parametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubrication–assisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication–assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.Peer reviewedFinal Published versio