1,720,981 research outputs found
Switching- and hedging- point policy for preventive maintenance with degrading machines: application to a two-machine line
Full text linkMaintenance and production are frequently managed as separate activities although they do interact. Disruptive events such as machine failures may find the company unready to repair the machine immediately leading to time waste. Preventive Maintenance may be carried out and maintenance time reduced to the effective task duration, in order to prevent time waste. Companies and researchers have been focusing on policies able to mitigate the impact of Preventive Maintenance on system availability, by exploiting the knowledge about degradation profiles in machines and the joint information from the machine state and the buffer level. In this work, the mathematical proof of the optimal threshold-based control policy for Preventive Maintenance with inventory cost, maintenance cost, backlog cost is provided. The control policy is defined in terms of buffer thresholds and dependency of the thresholds on the degradation condition. The optimal control policy is proved to include a combination of switching points and hedging points, where the first ones activate the Preventive Maintenance for a given condition and the latter ones control the production rate in order to minimize the surplus. An extensive experimental campaign analyzes the impact of system parameters such as the Maintenance duration on the cost function. The results show that there exists cases in which the optimal policy is dominated by the effect of the hedging points or the switching points, alternatively. Therefore, the proposed method is used to provide suggestions to the management for operative decisions, in order to choose the policy fitting best the system
Editorial: Special issue on advances in zero defect manufacturing
No full textThis introduction to the special issue discusses contemporary advances in zero defect manufacturing. As a technology-intensive concept, zero defect manufacturing has gained greater traction in recent years, given widespread interest in and adoption of Industry 4.0. As such, zero defect manufacturing has the potential to disrupt and reshape our entire manufacturing ideology. In this editorial, we present an overview of the main findings of the papers that were selected for publication in this special issue and provide our reflections for the future of zero defect manufacturing research
Performance evaluation of asynchronous two-stage manufacturing lines fabricating discrete parts
No full textAsynchronous systems with deterministic processing times are critical for fabricating discrete parts, especially in countries offering high salaries. Despite their widespread application, it is difficult to analyze these systems using conventional analytical modeling tools as commonly used hypotheses for these models consider different conditions compared to real behaviors. This paper presents a new analytical model for the performance evaluation of asynchronous two-stage manufacturing lines fabricating discrete parts with finite buffer capacity. A continuous-time Markov chain flow model that mimics the behavior of discrete asynchronous manufacturing systems by employing threshold-based control mechanisms is presented. The results obtained with the proposed model are compared with those using discrete event simulation, and they show good agreement. The model proposed in this paper can be used as a building block for the evaluation of longer asynchronous lines
A model-based Digital Twin to support responsive manufacturing systems
Full text linkManufacturing systems are subject to continuous changing conditions, which are due both to external reasons (e.g. changing demand) and to the natural system evolution, (e.g. machine degradation, operators’ upskilling). At tactical level, production engineers are challenged to continuously improve the system performance. At strategical level, the manufacturing company must monitor the system status and proactively identify reconfiguration actions to ensure system fitness to the evolving competitive scenario. A novel Digital Twin based on an analytical model for performance evaluation of manufacturing system embedding evaluation of joint parameter variations is introduced. In particular this work concentrates on how tactical decision makers can benefit from an integrated system model. The method is proved in a real industrial case in the railway sector
Information process-system modelling for Circular Economy of manufacturing systems
Full text linkRecent push towards sustainable development have lead machine tool builders to begin adopting novel business models which are usually peculiar in product-service-systems. In particular, business models as 'pay-per-use' introduced the issue of rental of production capacity integrated with the management of the life cycle of assets in a Circular Economy perspective. However, when it comes to machine tool builders, the high degree of customization of capital goods and in particular of multi-stage automated assembly systems that are strongly linked to the characteristics of the final product make this solution more difficult. Hence, the implementation of such business models represents a clear challenge for machine tool builders and manufacturing system suppliers from technological perspective. This work discusses preliminary results in the technological implications of such strategy. A real industrial case related to cross-sector multi-stage automated assembly systems is presented to highlight the technological challenges and potential research directions
A Markovian model of asynchronous multi-stage manufacturing lines fabricating discrete parts
Full text linkAsynchronous serial manufacturing lines that fabricate discrete parts are traditionally used in mass production, which represents a key sector in the global economy. Recent technological solutions for the modularization and standardization of manufacturing stations have led to this type of manufacturing system being reconfigured more often than in the past. Therefore, synthetic but accurate performance-evaluation models have become relevant as kernels in decision supporting tools for the continuous improvement of manufacturing systems. This paper presents a novel analytical model for the performance evaluation of asynchronous unreliable manufacturing lines fabricating discrete parts with finite buffers and deterministic processing times. This approach is based on continuous-time continuous-flow Markov chains. The general concept of operational cycles in discrete production is integrated into the modeling. The proposed model was validated using a discrete event simulation. The results demonstrate the accuracy and robustness of this model in evaluating a wide set of performance measures. The advantages of using this approach with respect to a purely continuous model were demonstrated. The applicability of the model to actual industrial scenarios was also demonstrated in a use case involving a high-volume assembly line. © 2023 The Society of Manufacturing Engineer
Hybrid digital modelling of large manufacturing systems to support continuous evolution
Full text linkAutomation and technological innovations pushed manufacturing companies to integrated plant configurations, where sub-systems are highly intertwined, though easily reconfigurable thanks to modularization. Frequent reconfigurations change the way sub-systems interact amongst each other more often than in the past. However, in large manufacturing systems digital sub-system models may be still ran independently, limiting their support in decision-making. This work proposes a methodology for the hybrid digital modelling of large manufacturing systems, where hybrid stands for multi-technique modelling, to achieve: (i) reduction of modelling complexity, (ii) portability, (iii) optimal modelling choice, (iv) hybrid modelling integration. An industrial case study in the electrical equipment sector shows the validity of the proposed approach
Decision making for fast productivity ramp-up of manufacturing systems
Full text linkFrequent changes in demand and production context call for frequent modifications in manufacturing systems, which can be realized by reconfigurations. After a modification, a manufacturing system usually fails short in delivering the expected production performance, due to an increased production of defective items and unexpected machine failures caused by incomplete or inadequate changes/reconfigurations implemented at physical or control levels. The time between the production of the first part and the stable production of good parts at the target effective throughput level is called the ramp-up time. Labor-intensive, time-consuming and expensive interventions are needed to understand and address issues that affect ramp-up. This essay provides a comprehensive overview on the topic, by addressing the following aspects: (i) definition of productivity ramp-up and brief explanation of the problem, (ii) research presented in the literature as solution approaches, (iii) selection of methodologies and their implementation, (iv) examples of applications, (v) research directions
Performance evaluation of multi-stage manufacturing systems operating under feedback and feedforward quality control loops
Full text linkIn manufacturing, the essential product characteristics are often created through multiple stages. Coupling product data obtained through inspection and controllers based on decision models with prediction capabilities enables quality control loops, enhancing both feedback and feedforward mechanisms. This paper proposes a methodology to merge the formulation of feedback and feedforward quality control loops into a performance evaluation model for multi-stage manufacturing systems. This approach evaluates quality control loop impacts system-wide, aiding in configuring and reconfiguring quality gates. A case study illustrates how allocating inspection technologies and efficient decision models improves overall system performance through effective feedback and feedforward control loops
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