Condition Based Maintenance Optimization of Multi-Equipment Manufacturing Systems by Combining Discrete Event Simulation and Multiobjective Evolutionary Algorithms

Modern industrial engineers are continually faced with the challenge of meeting increasing demands for high quality products while using a reduced amount of resources. Since systems used in the production of goods and deliveries of services constitute the vast portion of capital in most industries, maintenance of such systems is crucial (Oyarbide-Zubillaga, Goti, & Sánchez 2008). Several studies compiled by Mjema (2002) show that maintenance costs represent from 3 to 40 % out of the total product cost (with an average value of a 28%). Within maintenance, the Condition-Based Maintenance (CBM) techniques are very important. Nevertheless, and comparing it to the Preventive Maintenance (PM) optimization problem, relatively few papers related to CBM have been developed: According to Aven (1996), one of the reasons to justify this fact is that CBM models are usually by its nature rather sophisticated compared to the more traditional replacement models. Within this maintenance strategy, Das & Sarkar (1999) distinguish two CBM subtypes, On-Condition Maintenance (OCM) and Condition Monitoring (CMT). OCM is based on periodic inspections, while CMT performs a continuous monitoring on the hardware through instrumentation. Considering the described context, this paper focuses on the problem of CMT optimisation in a manufacturing environment, with the objective of determining the optimal CMT deterioration levels beyond which PM activities should be applied under cost and profit criteria in a multi-equipment system. The initiative considers the interaction of production, work in process material, quality and maintenance aspects. In this work the suitability of discrete event simulation to model or modify complex system models is combined with the aptitude that multiobjective evolutionary algorithms have shown to deal with multiobjective problems to develop a maintenance management and optimisation approach. An application case where the activities applied on a system that produces hubcaps for the car maker industry is performed, showing the quantitative benefits of adopting the detailed approach

Manufacturing System Lean Improvement Design Using Discrete Event Simulation

Lean manufacturing (LM) has been used widely in the past for the continuous improvement of existing production systems. A Lean Assessment Tool (LAT) is used for assessing the overall performance of lean practices within a system, while a Discrete Event Simulation (DES) can be used for the optimization of such systems operations. Lean improvements are typically suggested after a LAT has been deployed, but validation of such improvements is rarely carried out. In the present article a methodology is presented that uses DES to model lean practices within a manufacturing system. Lean improvement scenarios are then be simulated and investigated prior to implementation, thereby enabling a systematic design of lean improvements

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 Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

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

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Evaluating the cooling rate of hot mix asphalt in tropical climate

This paper aims to investigate the environmental effect on cooling rate and to determine the appropriate time available for compaction (TAC) using laboratory tests. This includes the study parameters, namely solar flux, base and ambient temperatures (daytime and night-time paving) and wind velocity, focusing on hot mix asphalt (HMA) asphalt concrete wearing with 14 mm nominal maximum aggregate size (ACW14) mix type for the wearing course and ACB28 mix type for the binder course. Samples were prepared in slab moulds 30.5 cm × 30.5 cm × 5 cm and compacted using a manually operated steel-roller. Readings were taken by averaging the temperature measurements at the middle and surface of the slabs and a temperature of 160 ºC was used as the mixing temperature. A control sample was prepared for each mix type and tested in the laboratory without the influence of wind velocity and solar flux. It was found that the cooling rate of HMA is significantly affected by environmental factors, thus influencing the TAC. The TAC tends to decrease by 15-50% during windy and night conditions but increases by up to 100% during daytime conditions compared to the control samples

State of the Industry 4.0 in the Andalusian food sector

The food industry is a key issue in the economic structure of Andalusia, due to both the weight and position of this industry in the economy and its advantages and potentials. The term Industry 4.0 carries many meanings. It seeks to describe the intelligent factory, with all the processes interconnected by Internet of things (IOT). Early advances in this field have involved the incorporation of greater flexibility and individualization of the manufacturing processes. The implementation of the framework proposed by Industry 4.0. is a need for the industry in general, and for Andalusian food industry in particular, and should be seen as a great opportunity of progress for the sector. It is expected that, along with others, the food and beverage industry will be pioneer in the adoption of flexible and individualized manufacturing processes. This work constitutes the state of the art, through bibliographic review, of the application of the proposed paradigm by the Industry 4.0. to the food industry.Telefónica, through the “Cátedra de Telefónica Inteligencia en la Red”Paloma Luna Garrid

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems