Modeling Of Manufacturing Systems Research Articles

An assessment method of machine components for usage-based value creation in complex manufacturing systems

Rising demands on manufacturing technologies lead to an increase in the complexity of corresponding machines and systems. This complexity acts as an obstacle to the development and realization of digital, usage-based business models for complex manufacturing systems. Particularly in the case of pay-per-use business models, it is necessary to monitor the actual machine load in operation to minimize the business risk on the operator and supplier side. This complicates the transition towards data-oriented value creation in manufacturing system environment. To overcome this challenge, a novel method to assess machine components regarding their potential to monitor the actual load and usage behavior of the whole manufacturing system is proposed. On this basis, the transparency and therefore confidence in digital, usage-based value creation like pay-per-use business models can be increased. The resulting approach is demonstrated by taking the example of a sheet metal forming press.

Layout planning and analysis of a Flexible Manufacturing System based on 3D Simulation and Virtual Reality

The high competition in the global market where the agile product launch and variable demand of goods by customers persuaded manufacturing companies to adopt Flexible Manufacturing System (FMS) and many companies have already implemented FMS solutions. On the other hand, manufacturing digitalization such as digital twin development, Industrial Virtual Reality (IVR), virtual modeling and 3D simulation of manufacturing systems offer new possibilities for effective facility layout planning, quick and easy modification and validation of production processes, analysis, and optimize the workflow and activities conducted on a factory floor. These new technological developments in digitalization have changed the thinking of manufacturing companies and they are eager to use digitalization solutions in their factory operations. However, there is a lack of harmonized methods and procedures to implement virtual modeling, 3D simulation, and IVR for layout planning and analysis of FMS. This paper proposed an approach for performance analysis of a FMS that is based on the 3D layout creation and simulation in a virtual environment, monitoring of key performance indicators via a digital dashboard, and immersive visualization through virtual reality. The relevance and feasibility of the proposed performance analysis approach are demonstrated by a case study.

A Simulation – Optimization Model of Network Failure Prone Manufacturing Systems with a Reliability-Based Maintenance and Revenue Sharing Approach

با توجه به اثر عوامل تصادفی مانند خرابی ماشین‌ها بر قدرت رقابتی سازمان‌های تولیدی، اهمیت برنامه‌ریزی تولید دوچندان شده است. لذا سیستم‌های تولیدی مستعد شکست برای مقابله با این عدم قطعیت، پدید آمده‌اند. جهت حفظ سهم در بازار رقابت و افزایش بهره‌وری، سیستم‌های صنعتی به اسـتراتژی نگهداری و تعمیرات به‌منظور کاهش نرخ خرابی و افزایش قابلیت اطمینان روی آورده‌اند. به‌منظور افزایش ظرفیت تولید، فراهم­آوردن انعطاف‌پذیری بیشتر و اطمینان از رضایت‌مندی مشتری از نظر کمیّت، کیفیت و زمان‌بندی، استفاده از پیمانکاری فرعی با رویکرد اشتراک درآمدها، گزینه‌ای مناسب در این پژوهش است. این پژوهش، سیستمی متشکل از شبکه‌ای از ماشین‌آلات با محدودیت رابطه و زمان خرابی و تعمیر تصادفی می باشد. به‌منظور جلوگیری از کمبود از بافرهای میانی و یک بافر نهایی استفاده می‌شود. پارامتر مؤثر دیگر تعیین دفعات بهینه‌ نگهداری پیشگیرانه است که منتج به حداقل رساندن هزینه نگهداری و تعمیرات پیشگیرانه و اصلاحی می‌شود. هدف تعیین نرخ بهینه تولید و متغیر‌های نگهداری پیشگیرانه و متغیر‌های مربوط به پیمانکاران فرعی است. برای این منظور از شبیه‌سازی گسسته ـ پیشامد استفاده می‌شود. بعد از مدل‌سازی در نرم‌افزار ارنا بهترین مقادیر متغیر‌های تصمیم در بستر Opt Quest به­دست می‌آید که به کاهش 5/22 درصدی هزینه‌های کل سیستم منجر می‌شود.

Switched Server Systems Whose Parameters are Normal Numbers in Base 4

Switched server systems are mathematical models of manufacturing, traffic and queueing systems. Recently, it was proved in (Eur J Appl Math 31(4), 682–708, 2020) that there exist switched server systems with 3 buffers (tanks), a server, filling rates \(\rho _1=\rho _2=\rho _3=\frac{1}{3}\) and parameters \(d_1, d_2, d_3>0\) whose \(\omega \)-limit set is a fractal set. In this article, we give an explicit large subset of parameters for which the corresponding switched server systems have no fractal \(\omega \)-limit set. More precisely, the Poincaré map of each system has a finite \(\omega \)-limit set. The approach we use is to study the topological dynamics of a family of piecewise \(\lambda \)-affine contractions that includes the Poincaré maps of the switched server systems as a particular case.

Manufacturing System Model Seamlessly Connecting Welding Process and Cyber System

Manufacturing System Model Seamlessly Connecting Welding Process and Cyber System

Structural decomposition-based energy consumption modeling of robot laser processing systems and energy-efficient analysis

• Generalized motion EC models of main functional units of the RLPS were established, while the computational complexity of EC was analysed. • A structural decomposition and modeling method of EC was proposed for the IR, to achieve rapid EC calculation of IRs. • The energy-saving planning problems were analyzed for a laser processing IR welding complex parts. Energy efficiency has been one of the main issues in manufacturing. Rapid energy consumption (EC) calculation is the key to improving the efficiency of EC optimization. In this paper, rapid EC calculation of robot laser processing systems (RLPSs) is studied based on structural decomposition. Firstly, the generalized motion EC models of main functional units of RLPSs are established for manufacturing processes, while the power consumption characteristics and computational complexity of EC are analyzed. Then, a modeling method of EC is proposed for industrial robots (IRs), based on structural decomposition, considering the generating mechanisms and computational complexity of EC. The models of all EC components are established, which are all parameterized functions of time or key factors (joint angle paths of the robot and the average acceleration of the end-effector). Moreover, EC models of other functional units are also well established according to the corresponding EC characteristics. A KUKA robot case is described to verify the proposed method. The results show that the established EC model of the IR has reliable prediction accuracy within the feasible speed range, which can well reflect the characteristic relationships between EC and time. The proposed study solution is also suitable for EC modeling of other robot manufacturing systems. Furthermore, the energy-saving planning of processing complex parts are analyzed for a laser processing IR. The RLPS has considerable energy-saving potentials under situations concerned.

Help students learn interpreted Petri nets with Minecraft

Background: Petri nets are a formal specification technique for modelling of control processes and modern flexible manufacturing systems. Interpreted Petri nets take into account input and output signals, allowing to apply them in any control system or even in control part of a cyber-physical system. Due to the fact that Petri nets are not used in the industrial practice, the students sometimes lack motivation to learn them. Contributions: In the paper we propose how to help students learn interpreted Petri nets with Minecraft (as a game-based learning). We show how interpreted Petri nets can be modelled in Minecraft and how they communicate with the surrounding environment via input and output signals to visualize control processes. The proposed approach has been validated experimentally among university students. Hypotheses: (1) Creating interpreted Petri net models with Minecraft helps to understand the basic principles; (2) Minecraft makes the course more attractive. Methodology: Students were divided into an experimental group (with game-based learning) and a control group (with traditional learning). The experimental group filled in a knowledge test twice (on the entry and on the exit) and a questionnaire. The control group filled in the same knowledge test at the end of the course. Findings: The observations confirm that the Minecraft-based teaching of interpreted Petri nets allows to gain better results in final tests, making at the same time the course more attractive and enjoyable.

Mission reliability–centered maintenance approach based on quality stochastic flow network for multistate manufacturing systems

Previous studies of reliability centered maintenance (RCM) rarely consider the maintenance quality for the operation condition monitoring of manufacturing system. Therefore, a quality-oriented maintenance approach for the multistate manufacturing system with the aid of mission reliability is proposed. First, connotations of the mission reliability and maintenance quality of the multistate manufacturing system are expounded on the basis of the operational mechanism. Second, a quality stochastic flow network (QSFN) model of the multistate manufacturing system is established, and a novel mission reliability model is presented. Third, a quality-oriented mission reliability–centered maintenance framework for multistate manufacturing systems is proposed, and the optimal integrated maintenance strategy is obtained by minimizing the total cost. Finally, an industrial example of subway flow receiver is presented to verify the proposed method. Results show that the proposed method can simultaneously balance the maintenance cost and maintenance quality of the multistate manufacturing system.

Agent-based Modelling of Manufacturing Systems in the Context of “Industry 4.0”

The requirements of the modern market are manifested mainly in the need to provide a diversified assortment and personalized products. The ability to diversify the product range and adjust the production volume to the market requirements is a desirable feature of modern production systems. The Fourth Industrial Revolution has introduced many changes in the process planning domain that increased the possibility of better allocation of resources by applying artificial intelligence methods in order to achieve the effect of self-organization of the production process. The Smart Factory concept, based on cyber-physical systems, has allowed the customers to have greater influence on the process of manufacturing the ordered goods and even to change the order details during the process. The implementation of Industry 4.0 assumptions, and in particular the emergence of autonomous units, requires a new approach to modelling of manufacturing systems. This paper considers the problem of applying the agent-based modelling in modelling of such systems, focusing especially on the problem of adjusting the current formalism of agents or holons to the principles of Industry 4.0. An important part is the analysis of the agent definition in the context of manufacturing systems, in accordance with the available literature. The next step involves the discussion on the possibility of representing the Industry 4.0-compliant systems in terms of multi-agent paradigm.

Modelling and simulation of robotic production systems

Modern manufacturing systems consist of a sequence of robotic systems, such as robotic cells, which are widely used in the industry. As they become commonplace and play important roles in modern industrial production, efficient modelling and use of robotic cells become a necessity. Robotization brings many measurable benefits. It enables effective utilization of both human resources and the possessed machine park and thus contributing to the improvement of product quality and efficiency. Industrial robots not only increase the growth of productivity, by replacing man in monotonous works, requiring enormous, not achievable for human precision but above all ensure the accuracy and repeatability of the product quality. Appropriate design and modelling of robotic manufacturing systems are crucial in the efficient integration of work cell components and proper maintenance of robotic production systems. Therefore, new solutions for programming environments in industrial applications are sought which not only allow reprogramming of industrial robots but also enable the virtual design of new production lines, which in turn allows the faster introduction of higher quality products. This article presents the modelling and programming of robotic work cells in the Siemens Process Simulate environment.

A Lean Manufacturing Progress Model and Implementation for SMEs in the Metal Products Industry

The manufacturing industry faces the challenge of small and diversified customer orders. To meet this challenge, strong internal production capabilities are required. A lean manufacturing process that uses fewer resources and offers greater process improvement will help SMEs to continue to contribute to the global economy. Though SMEs provide most employment opportunities, previous studies have focused on large companies in auto-manufacturing-related industries. With the commitment and support of the management, and the application of a value stream map (VSM) and related improvement tools, we produced a practical process improvement model for a lean manufacturing system in an SME. With the commitment and support of the management and the joint efforts of the project improvement staff, the 10 improvement projects over a six-month period all achieved their goals: reduction in lead time from 26 days to 19.5 days, improvement of welding per people per hour (PPH) efficiency by 28.3%, improvement of packaging PPH efficiency by 64.1%, improvement of working in process (WIP) efficiency at the production site by 83.84%, and improvement of raw material storage by 83.84%. The efficiency of the raw material warehouse inventory was improved by 58.63%, and the efficiency of the shipment completion rate was improved by 14.5%.

Evaluation model and algorithm of intelligent manufacturing system based on pattern recognition and big data

Evaluation model and algorithm of intelligent manufacturing system based on pattern recognition and big data

A learning workflow based on an integrated digital toolkit to support education in manufacturing system engineering

Digital modelling of manufacturing systems is experiencing a fast development, but it still shows significant limitations when considering integration and interoperability of enabling technologies. Indeed, there is still a lack of reference integrated workflows to perform the wide span of tasks, ranging from layout configuration and performance evaluations to 3D representations. Commercial software tools are either too complex or expensive to be approached by non-specialists, therefore it is hard to design effective learning activities in manufacturing system engineering. This paper proposes a structured learning workflow based on an open toolkit that takes advantage of a common ontology-based data model to smoothly integrate digital tools for manufacturing system modelling, performance evaluation, and virtual reality representation. After detailing methodologies and digital tools, the proposed workflow is applied to a pilot case in higher education.

Digital twin framework for reconfigurable manufacturing systems (RMSs): design and simulation.

Faced with the global crisis of COVID-19 and the strong increase in customer demands, competition is becoming more intense between companies, on the one hand, and supply chains on the other. This competition has led to the development of new strategies to manage demand and increase market share. Among these strategies are the growing interest in sustainable manufacturing and the need for customizable products that create an increasingly complex manufacturing environment. Sustainable manufacturing and the need for customizable products create an environment of increased competition and constant change. Indeed, companies are trying to establish more flexible and agile manufacturing systems through several systems of reconfiguration. Reconfiguration contributes to an extension of the manufacturing system’s life cycle by modifying its physical, organizational and IT characteristics according to the changing market conditions. Due to the rapid development of new information technology (such as IoT, Big Data analytics, cyber-physical systems, cloud computing and artificial intelligence), digital twins have become intensively used in smart manufacturing. This paper proposes a digital twin design and simulation model for reconfigurable manufacturing systems (RMSs).

Semi‐Markov reliability model of manufacturing systems considering machine degradation, human errors, and their mutual facilitation effects

AbstractReliability plays a significant role in the overall performance of manufacturing systems, and accurate reliability assessments can guarantee their functionalities and efficiencies. This paper develops a reliability model of a manufacturing system considering machine degradation, human errors, and their mutual facilitation effects (MFE): machine degradation may increase fatigue rate and weaken learning process, thereby facilitating the occurrence of human errors, while human errors may, in turn, facilitate the machine degradation process. Although MFE may reduce system reliability, current reliability models ignore this effect. In this paper, to describe the MFE, a stochastic human error model under the effects of learning–forgetting and fatigue–recovery is proposed, where the human error rate depends on the current machine degradation state; next, based on their facilitation effects on machine degradation, human errors are divided into safety, damage, and fatal zones; finally, we incorporate the zoned human error model into the machine degradation model and develop a Semi‐Markov reliability model. A Monte Carlo method is implemented to calculate system reliability. The turret of a lathe operated by a worker is utilized to illustrate the effectiveness of the reliability model.

Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment

Electricity demand response is proved to be a viable approach to address the peak demand, improve the power grid reliability, and facilitate the grid integration of renewable resources. The implementation of demand response management in the manufacturing sector, which is one of the main energy consumers, has received significant attention. The existing studies in this field have traditionally focused on maximizing the benefits of demand response through machine control and scheduling. However, due to the widespread adoption of fast-charging infrastructure for material handling equipment (MHE), the high-power demand of fast charging and the close coordination between the machines and MHE pose much-increased complexity in the energy management, which necessitates a more comprehensive demand response scheduling method for the integrated manufacturing and material handling system. In this study, an analytical manufacturing and material handling system model is proposed to obtain cost-effective production schedules under demand response. The interactions between machines and MHE, production throughput requirement, battery charging characteristic, and time-varying electricity pricing are jointly considered in the integrated model to help manufacturers reap substantial benefits of demand response. The effectiveness of the proposed method is validated through numerical case studies, where the results indicate a 15.1% energy cost reduction compared to the benchmark scheduling scheme.

Partial-Order Reduction for Supervisory Controller Synthesis

A key challenge in the synthesis and subsequent analysis of supervisory controllers is the impact of state-space explosion caused by concurrency. The main bottleneck is often the memory needed to store the composition of plant and requirement automata and the resulting supervisor. Partial-order reduction (POR) is a well-established technique that alleviates this issue in the field of model checking. It does so by exploiting redundancy in the model with respect to the properties of interest. For controller synthesis, the functional properties of interest are nonblockingness, controllability, and least-restrictiveness, but also performance properties, such as throughput and latency are of interest. We propose an on-the-fly POR on the input model that preserves both functional and performance properties in the synthesized supervisory controller. This improves the scalability of the synthesis (and any subsequent performance analysis). Synthesis experiments show the effectiveness of the POR on a set of realistic manufacturing system models.

Hybrid digital modelling of large manufacturing systems to support continuous evolution

Automation 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.

Multi-method simulation modelling of circular manufacturing systems for enhanced decision-making

Circular manufacturing systems (CMS) constitute complex value networks comprising a large and diverse set of stakeholders that collaborate to close the loop of products through multiple lifecycles. Complex systems modelling and simulation play a crucial role in providing quantitative and qualitative insights into the behaviour of such systems. In particular, multi-method simulation modelling that combines agent-based, discrete-event, and system dynamics simulation methods is considered more suitable to model and simulate CMS as it allows to capture their complex and dynamic nature. This paper provides a step-by-step approach on how to build a CMS multi-method simulation model in order to assess their economic, environmental, and technical performance for enhanced decision-making. To model and simulate CMS three main elements need to be considered:•A multi-method model architecture where the CMS stakeholders with heterogeneous characteristics are modelled individually as autonomous agents using agent-based, discrete-event, and system dynamics.•An agent environment defined by a Geographic Information System (GIS) to establish connections based on agents’ geographic location.•The product journey resulting from the product's interaction with various CMS stakeholders in the circular value network is traced throughout its multiple lifecycles.

Required parameters for modelling heterogeneous geographically dispersed manufacturing systems

COVID-19 and global crises/events are driving governments to rethink their national manufacturing strategies. The drastic change of societal conditions has exposed our reliance on a constrained set of production practices. Furthermore, the future manufacturing landscape indicates - supply chain crises, trade agreements and natural disasters - a high level of volatility which requires a response that is far from being achieved.While these emergent challenges have called the efficacy of established practices into question, new manufacturing technologies, such as Additive Manufacturing (AM), present the capability to provide a solution. One proposal is agent-based brokering of AM which could be a method for tackling local, regional, national, and international production needs. However, to achieve the reality of brokered AM, it is imperative that the diversity of AM capability is considered. Diversity that existing homogeneous modelling of AM and manufacturing systems rarely consider or capture. This paper conceptualizes the reality of AM systems and elucidates parameters that are necessary for successful modelling and subsequent co-ordination. Having presented the required parameters the paper continues to discuss requisite levels of abstraction, suitable performance metrics and the role of humans in agent-based manufacturing systems.