Recent years have seen the emergence of several new manufacturing paradigms due to consumer demand for more customized, intelligent, and sustainable products and the explosive rise of the industrial ...Internet and cyber-physical technology leasing to the development of smart manufacturing systems. However, the quantification of several capabilities for smart manufacturing systems, such as decentralization, customization, interoperability, etc., is a matter of concern. The current research makes an attempt in this direction by questioning the need for this "multiplicity" of definitions and employs a methodical methodology to quantify the decentralization components to produce a coherent "blueprint" that justifies and integrates all of these many points of view. Further, a conceptual analysis of the model is made to discuss the alterations in the intelligence offerings with the enhancing implementation level of smart manufacturing systems. Based on the graphical analysis, the various level of organizational maturity with the enhancing smart manufacturing implementation is deliberated. The research provides significant directions to the managers to effectively plan, design, and execute the decentralization aspects with the increasing deployment level of smart manufacturing systems at a limited investment.
Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of ...products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of 'prediction and manufacturing'. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality-reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality control and mission reliability analysis, is obtained by minimising the total cost. Finally, a case study on decision-making with the integrated PdM strategy for a cylinder head manufacturing system is presented to validate the effectiveness of the proposed method. The final results shows that proposed method achieves approximately 26.02 and 20.54% cost improvement over periodic preventive maintenance and conventional condition-based maintenance respectively.
As the industrial requirements change at a rapid pace due to the drastic evolution of technology, the necessity of quickly investigating potential system alternatives towards a more efficient ...manufacturing system design arises more intensely than ever. Manufacturing systems simulation has proven to be a powerful tool for designing and evaluating a manufacturing system due to its low cost, quick analysis, low risk and meaningful insight that it may provide, improving thus the understanding of the influence of each component. Simulation comprises an indispensable set of IT tools and methods for the successful implementation of digital manufacturing. It allows experimentation and validation of product, process, and system design and configuration. This paper investigates the major historical milestones in the evolution of manufacturing systems simulation technologies and examines recent industrial and research approaches in key fields of manufacturing. It describes how the urge towards digitalisation of manufacturing in the context of the 4th Industrial revolution has shaped simulation in the design and operation of manufacturing systems and reviews the new approaches that have arisen in the literature. Particular focus is given to technologies in the digitalised factories of the future that are gaining ground in industrial applications simulation, offering multiple advantages.
Deadlock resolution has been an important research topic in the field of automated manufacturing systems (AMSs). Researchers generally assume that AMS resources never break down whereas only a few ...resolve the issues of resource failures in the discrete-event supervision of AMSs. In fact, an AMS consists of a number of numerically controlled machines interacting with each other. The failure of resources happens unexpectedly. In this article, we allow parallel routes to use unreliable resources. Because of their powerful modeling capabilities, Petri nets are used to model the considered AMSs. By using a look-ahead control strategy, a robust supervisory control policy is developed for AMSs with assembly operations allowing resource failures. Our objective is to advance parts requiring failed resources in their remaining routes into a special position so as to release shared resources in case some unreliable resources fail. Consequently, those parts not necessarily requiring any failed resource can keep progressing all the time. The conventional methods are on the basis of monolithic and structure-oriented control specifications with centralized supervisors. Our policy can be implemented in a distributed, online, and local way. Several examples are given to elucidate our control policy clearly. Note to Practitioners -In automated manufacturing systems (AMSs), resources such as machines and tools with higher reliablity are always expensive. Sometimes, when it is not cost-effective to use resources with higher reliability, manufacturers may choose some resources with possible failures. These resources are thus considered as unreliable ones in our article. Normally, unreliable resources may fail unexpectedly. Their occurrences can lead a system to stagnation, causing unnecessary downtime, and bringing economic loss to enterprises. To resolve such stagnation issues, we develop a robust supervisory control policy to synthesize a robust liveness-enforcing supervisor for AMSs with assembly operations and unreliable resources. The supervisor can guarantee that a controlled system continues to progress without deadlock and blocking states even if some unreliable resources fail to work.
Industry 4.0 (I4.0) encompasses a plethora of digital technologies effecting on manufacturing enterprises. Most research on this topic examines the effects in the smart factory domain, focusing on ...production scheduling. However, there is still a lack of comprehensive research on the applications of I4.0 enabling technologies in manufacturing life-cycle processes. This paper is thus intended to provide a systematic literature review answering the following research question: What are the applications of I4.0 enabling technologies in the business processes of manufacturing companies? The study analyses 186 articles and the results show that production scheduling and control is the process most often investigated, while there is also an increasing trend in servitization and circular supply chain management. Moreover, there is extensive combined use of IoT, Big Data Analytics and Cloud, whose applications cover a wide range of processes. On the contrary, other technology like Blockchain is not as widely discussed in the domain of I4.0. This picture calls for a future research agenda extending the scope of investigation into I4.0 in manufacturing. Furthermore, the results of this research can prove extremely useful for practitioners who wish to implement one or more technologies, providing them with solutions for applications in manufacturing.
This paper proposes an adaptive supervisory control policy for an automated manufacturing system (AMS) with multiple types of unreliable resources. The considered AMS is a generalized system of ...simple sequential process with resources (GS<inline-formula> <tex-math notation="LaTeX">^{3}</tex-math> </inline-formula>PR). In order to model resource failures and recoveries, modified recovery subnets are developed, which model more complex resource failure scenarios compared with traditional recovery subnets. For the purpose of guaranteeing the execution of a system's production successfully, from the perspective of the structural analysis of the system, a siphon control-based technique is introduced and a type of controller is developed. It is verified that the proposed adaptive control policy assures the deadlock-freeness of the controlled system no matter if there exist resource failures, and retains all behavior of the original system. Finally, examples are presented to demonstrate the proposed method. Note to Practitioners -Resources failures are ubiquitous in an automated manufacturing system (AMS). Their occurrences can lead partial or global system stagnation, which causes unnecessary downtime and even catastrophic results. Most of the existing robust deadlock control strategies for AMSs with unreliable resources usually restrict system evolution in an unnecessarily conservative way such that part of permissive behavior is excluded if no resource failure occurs, which affects the utilization of resources and reduces the productivity of the system. In order to address the issue, this work considers a type of AMSs where each processing stage may require an arbitrary number of units, but all from a single resource type. For such an AMS, we propose an adaptive deadlock control policy that makes the controlled system deadlock-free, keeping all the system behavior as in the case that unreliable resources do not break down. The proposed method is realized by dynamically adjusting the working modes of a system to adapt to resource failures and recoveries, which is readily accessible to practitioners from a technical viewpoint.
•This study is the first systematic review concerning maintenance in Industry 4.0.•The impact of each Industry 4.0 technology in maintenance tasks has been identified.•Remote Maintenance had an ...increase due to Augmented Reality and smart devices.•The results show Self-maintenance is an attractive possibility for smart factories.•Smart factories require changes in manufacturing and operators management.
Industry 4.0 is revolutionizing manufacturing, increasing flexibility, mass customization, quality and productivity. In today's competitive manufacturing scenario, maintenance is one of the most critical issues and companies are approaching its digital transformation from technological and management perspectives.
This article carries out a systematic literature review aimed to investigate how maintenance tasks and maintenance management strategies are changing in Industry 4.0 context, analyzing the state-of-the-art of Industry 4.0 technologies currently employed in maintenance and the resulting potential innovations in maintenance policies and manufacturing management. In addition, the most relevant trends in current maintenance policies have been investigated, such as “remote maintenance” and the attractive possibility of a “self-maintenance”. Also, the importance of human factor has been considered. The results are summarized in a comprehensive database, to provide, through concepts and empirical evidence present in literature, examples and strategies for the implementation of maintenance in Industry 4.0.
The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart ...manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of “semantic similarity” to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.
To meet the challenges of global competitiveness, manufacturing organizations are now facing the problems of selecting appropriate manufacturing strategies, product and process designs, manufacturing ...processes and technologies, and machinery and equipment. The selection decisions become more complex as the decision makers in the manufacturing environment have to assess a wide range of alternatives based on a set of conflicting criteria. To aid these selection processes, various multi-objective decision-making (MODM) methods are now available. This paper explores the application of an almost new MODM method, i.e., the multi-objective optimization on the basis of ratio analysis (MOORA) method to solve different decision-making problems as frequently encountered in the real-time manufacturing environment. Six decision-making problems which include selection of (a) an industrial robot, (b) a flexible manufacturing system, (c) a computerized numerical control machine, (d) the most suitable non-traditional machining process for a given work material and shape feature combination, (e) a rapid prototyping process, and (f) an automated inspection system are considered in this paper. In all these cases, the results obtained using the MOORA method almost corroborate with those derived by the past researchers which prove the applicability, potentiality, and flexibility of this method while solving various complex decision-making problems in present day manufacturing environment.
This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be ...moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.
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