Modeling Of Manufacturing Systems Research Articles

Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment

Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.

Research and development of social manufacturing model and 3D printing testbed for industrial internet

<p indent="0mm">The deep integration of the new generation of information technology and manufacturing is driving manufacturing to socialization, service-oriented, interconnected integration, and platform-oriented development. The industrial internet-oriented social manufacturing model fits the current manufacturing industry’s vision of intelligent transformation and the creation of new business formats. Based on the analysis of the evolution of the current manufacturing model, this paper presents the system architecture and operating logic of the social manufacturing model driven by information-physical-social interconnection and explores three key implementation technologies, including Social sensor-driven resource clustering and community creation, cyber-physical-social interconnection driven social manufacturing system and software model of social manufacturing. Developed a 3D printing testbed for social manufacturing and verified the feasibility of the social manufacturing theory by introducing testbed application development and the production operation process. At the same time, it also provides a demonstration and guiding role for the application and practice of the industrial internet.

Generic Design Methodology for Smart Manufacturing Systems from a Practical Perspective, Part I—Digital Triad Concept and Its Application as a System Reference Model

Rapidly developed information technologies (IT) have continuously empowered manufacturing systems and accelerated the evolution of manufacturing system paradigms, and smart manufacturing (SM) has become one of the most promising paradigms. The study of SM has attracted a great deal of attention for researchers in academia and practitioners in industry. However, an obvious fact is that people with different backgrounds have different expectations for SM, and this has led to high diversity, ambiguity, and inconsistency in terms of definitions, reference models, performance matrices, and system design methodologies. It has been found that the state of the art SM research is limited in two aspects: (1) the highly diversified understandings of SM may lead to overlapped, missed, and non-systematic research efforts in advancing the theory and methodologies in the field of SM; (2) few works have been found that focus on the development of generic design methodologies for smart manufacturing systems from the practice perspective. The novelty of this paper consists of two main aspects which are reported in two parts respectively. In the first part, a simplified definition of SM is proposed to unify the existing diversified expectations, and a newly developed concept named digital triad (DT-II) is adopted to define a reference model for SM. The common features of smart manufacturing systems in various applications are identified as functional requirements (FRs) in systems design. To model a system that is capable of reconfiguring itself to adapt to changes, the concept of IoDTT is proposed as a reference model for smart manufacturing systems. In the second part, these two concepts are used to formulate a system design problem, and a generic methodology, based on axiomatic design theory (ADT), is proposed for the design of smart manufacturing systems.

The efficiency of modeling and simulation of manufacturing systems using Petri nets

The paper is a partial synthesis of the work that a team within the Faculty of Managerial and Technological Engineering has in the field of modeling and simulation of systems with Petri nets. The paper deals with three issues. The first part is Modeling and simulation of manufacturing systems with timed Petri nets. The first part presents the way in which the performances of some manufacturing systems are evaluated using timed Petri nets. The first example is a manufacturing system specific to the textile industry. The system has three production lines. In this case, the modeling with timed Petri nets allows the analysis of the operation of the flow lines and highlights the bottleneck. Another modeled manufacturing system is specific to a series production, more precisely it refers to the manufacture of gas cookers within a company from Oradea. The model with Petri nets also describes the supply sequences of the stocks of parts and subassemblies. The next modeled system is an automated manufacturing system for the packaging of detergents. The complexity of the Detergent Packing System and the high level of automation, require the use of efficient modeling and simulation methods to verify the validity of the solutions adopted. In this chapter is present the model of a robotic welding cell. The second part of the paper, the transition from timed Petri nets to hierarchical colored Petri nets, shows how Petri nets were developed for a robotic cell for metallographic analysis. The third part is dedicated to modeling with colored Petri nets timed with complex colors of the flexible manufacturing cell A007 within the Department of Industrial Engineering.

Modeling framework to support decision making and control of manufacturing systems considering the relationship between productivity, reliability, quality, and energy consumption

Different formalisms and modeling frameworks have been developed to capture the discrete behavior of manufacturing systems. However a purely discrete model does not capture the continuous variables of machine-level operation. Previous research in modeling manufacturing systems has studied the quantity-quality and quantity-reliability coupling of production environments but their relationship to energy consumption is often not considered. This work presents a framework that extends the state-of-the-art in modeling manufacturing systems to support decision making for control at both machine- and system-level variables using hybrid models. The modeling strategy considers the coupling between productivity, quality, reliability and energy consumption and leverages the current plant floor data extraction capabilities to develop data-driven models. A novel control strategy is presented that considers the effect of various machine- and system-level variables over different performance metrics and balances them using multi-objective optimization. This framework was validated using a combination of real and simulated data of a production representative environment. The optimal set of control variables is obtained using simulation-based optimization to support plant floor decision making by studying process variables, maintenance actions, or system reconfiguration. Results show the effect of different control variables and the ability to reduce energy consumption while improving productivity. The implementation of the modeling and control framework presented here has the potential to impact the operations of manufacturing system by reducing cost and improving productivity.

Intelligent Development of Safety Production System under 5G+ Industrial Internet

As an opportunity for countries around the world to revitalize the manufacturing industry and the transformation and upgrading of our country’s manufacturing industry, the production safety system is an opportunity for the development of advanced information technology, manufacturing technology and artificial intelligence. At this stage, intelligent manufacturing has set off a global research boom. This article mainly introduces the research on the intelligent development of the safety production system under 5G+ Industrial Internet. Through the intelligent research on the safety production system, the development of the early warning system model of the safety production system is carried out. This paper uses 5G + Industrial Internet combined with intelligent manufacturing technology to establish an early warning model. Increase the efficiency of the production system by 34%, greatly reducing the possibility of safety accidents. Based on the traditional safe production system, facing the development needs of intelligent manufacturing technology, and taking the training goal of advanced skilled talents as the direction, this paper deeply analyzes the requirements and characteristics of the training model for the intelligent manufacturing system, and constructs the corresponding Intelligent manufacturing safety production system.

Demonstration and evaluation of a digital twin-based virtual factory.

Smart manufacturing, tailored by the 4th industrial revolution and forces like innovation, competition, and changing demands, lies behind the concurrent evolution (also known as co-evolution) of products, processes and production systems. Manufacturing companies need to adapt to ever-changing environments by simultaneously reforming and regenerating their product, process, and system models as well as goals and strategies to stay competitive. However, the ever-increasing complexity and ever-shortening lifecycles of product, process and system domains challenge manufacturing organization’s conventional approaches to analysing and formalizing models and processes as well as management, maintenance and simulation of product and system life cycles. The digital twin-based virtual factory (VF) concept, as an integrated simulation model of a factory including its subsystems, is promising for supporting manufacturing organizations in adapting to dynamic and complex environments. In this paper, we present the demonstration and evaluation of previously introduced digital twin-based VF concept to support modelling, simulation and evaluation of complex manufacturing systems while employing multi-user collaborative virtual reality (VR) learning/training scenarios. The concept is demonstrated and evaluated using two different wind turbine manufacturing cases, including a wind blade manufacturing plant and a nacelle assembly line. Thirteen industry experts who have diverse backgrounds and expertise were interviewed after their participation in a demonstration. We present the experts’ discussions and arguments to evaluate the DT-based VF concept based on four dimensions, namely, dynamic, open, cognitive, and holistic systems. The semi-structured conversational interview results show that the DT-based VF stands out by having the potential to support concurrent engineering by virtual collaboration. Moreover, DT-based VF is promising for decreasing physical builds and saving time by virtual prototyping (VP).

A Review on Reliability Modeling of Manufacturing Systems Considering the Production Quality and Equipment Reliability

Background: Nowadays, the manufacturing industry is much more fiercely competitive and global than ever before. For sustainable increases in market share and profitability, manufacturers are aiming to improve the quality of products and maintain the reliability of manufacturing systems. Objective: Therefore, to simultaneously optimize the quality and reliability for manufacturing systems has become an important research topic in recent years. Methods: This study presents an overview of the methods for modeling manufacturing systems, especially considering the system reliability and the product quality. And then, we review the optimization methods for manufacturing system maintenance and production strategy. Conclusion: Finally, optimization methods and some challenges are summarized for future studies.

Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study

Process manufacturing industries constantly strive to make their processes increasingly sustainable from an environmental and economic perspective. A manufacturing system model is a powerful tool to holistically evaluate various manufacturing configurations to determine the most sustainable one. Previously models of process manufacturing systems are typically single target models, trained to fit and/or predict data for a single output variable. However, process manufacturing systems produce a variety of outputs with multiple, sometimes contradictory, sustainability implications. These systems require multiple target models to find the most sustainable manufacturing configuration which considers all outputs. A novel bioprocess that treats process wastewaters to reduce pollutant load for reuse, while simultaneously generating energy in the form of biogas was studied. Multiple target models were developed to predict the percentage removal of chemical oxygen demand and total suspended solids, in addition to the biogas (as volume of methane) produced. Predictions from the models were able to reduce wastewater treatment costs by 17.0%. Eight models were developed and statistically evaluated by the coefficient of determination (R2), normalised root mean square error (nRMSE) and mean absolute percentage error (MAPE). An artificial neural network model built following the ensemble of regressor chains demonstrated the best multi target model performance, averaged across all the bioprocess’s outputs (R2 of 0.99, nRMSE of 0.02, MAPE of 1.74). The model is able to react to new regulations and legislation and/or variations in company, sector, world circumstances to provide the most up to date sustainable manufacturing configuration.

Due-date quotation model for manufacturing system scheduling under uncertainty

This paper studies the scheduling problem for the manufacturing systems with uncertain job duration, and the possibility of planning due-date quotations for critical manufacturing tasks given a fixed contingency budget. We propose a due-date quotation model to measure the risk of delay in the manufacturing process in terms of the allocated contingency budget. The risk of delay is measured in the same unit as its corresponding milestone factor such that the decision makers could directly visualize and quantify the level of risks in units of hours or days. In addition, the proposed model possesses various great properties required by a convex risk measure and it represents a minimized certainty equivalent of the overall expected risk in achieving the manufacturing due-dates. Extensive computational experiments are conducted to evaluate the model performance. The results show that our proposed model, compared to various existing methods, provides a much more balanced performance in terms of success rate of due-date achievement, due-date quotation shortfall, as well as, robustness against uncertainties. The practical applicability of the proposed models are also tested with the job scheduling problem in a real stamping industry application.

Knowledge-Based Automation for Smart Manufacturing Systems

Smart manufacturing is targeted as the next generation of manufacturing by many national and international strategic development. The increasingly rich production data, the integration and extensive application of information technology, and the intelligent data processing and system modeling methods have collectively enabled smart manufacturing. Building upon them, manufacturing system modeling, knowledge acquisition, design, and real-time control are the key components [item 1) in the Appendix], [item 2) in the Appendix]. It is still one of the really huge challenges to gather data, transform them into information, and derive knowledge out of this information, especially given the requirement of knowledge that can be trusted as manufacturing systems may harm humans and the environment if they come to the wrong conclusion. Despite the learning and derivation of knowledge, it could be modeled beforehand and taken, for example, as an environmental model for online decisions like in deliberative agent-based systems. Nevertheless, for decisions during operation, real-time requirements, dependability, and security issues are to be guaranteed. Finally, for acceptance and trust, humans need to “understand” the reasons behind automated decisions; therefore, explainability or at least a white box description is an issue of such knowledge-based systems.

SimEvents-based discrete-event simulation modelling and performance analysis for dynamic job-shop manufacturing system

The present paper deals with the application of discrete-event simulation (DES) to develop simulation model for dynamic job-shop manufacturing system (DJMS). In the present time, DES software tool ...

Knowledge models in computer-aided manufacturing systems

Despite of vast amounts of information and considerable opportunities to process it with computer-aided manufacturing systems, personal experience and lore of experts become a role-defining category in the issues of creating and making technical decisions. This should be factored into the process of creating instruments for decision making, which are based on computer-aided manufacturing systems. Efficient decision-making models, as well as models of factors (determinants) evaluation, should include expert knowledge data. In this connection, it is of burning importance to find ways to formalize expert knowledge into a code readable by computer-aided manufacturing systems. The research paper presents three determinant (criteria) evaluation models which help make technical decisions. The models include expert knowledge data formalized by means of linguistic variables. The research paper also presents recommendations on how to opt for a proper model, including requirements, and peculiarities of expert information collection matters.

SimEvents-based discrete-event simulation modelling and performance analysis for dynamic job-shop manufacturing system

The present paper deals with the application of discrete-event simulation (DES) to develop simulation model for dynamic job-shop manufacturing system (DJMS). In the present time, DES software tool is very known to engineers in designing manufacturing system layout and production lines, it facilitates to get a good solution for the analysis and validation of any problem. In this research work, DES software tool 'SimEvents' has been used for developing simulation model. Dynamic processing plus waiting (DPPW) time due date assignment method and four dispatching rules are investigated in this article. The performance of the DJMS is evaluated through different measures such as mean flow time, mean tardiness, standard deviation of flow time, standard deviation of tardiness, production output and work-in-process. A combination of dispatching rules and different job types is taken for simulation experiments. This paper analyses the simulation results statistically and graphically.

Multi-scale modelling of manufacturing systems using ontologies and delta-lenses

The adoption of digital technologies in manufacturing enables intelligent dynamic control approaches, at the cost of increased design complexity. In this paper, ontologies and delta-lenses are exploited to enable multi-scale models of a manufacturing system to map digital models at different scales and let data flow according to the level of fidelity. A workflow is designed to assess the capability of models with a lower level of details to approximate the behaviour of the original system, through the application of a hybrid delta-lens. The approach is illustrated with a user case and applied to an industrial case, aiming at deciding the positions of sensors in an assembly line.

Considerations on Present Production Science Theory and Didactics: The Evident Lack of a Rational Manufacturing Theory

Today’s universities’ manufacturing courses on production system design and analysis of performance dynamics are heuristics than rational-based, far away from a physics or mathematics theorem constructed science. This is also due to increasing computational power, which allowed the proliferation of simulation-based modeling for complex manufacturing systems to analyze their behavior and optimize their performance. Indeed, manufacturing theory remained stuck to generic practice-based production concepts. The lack of a well-structured scientific production specific theory is increasingly problematic. Engineering students lacking the foundation of a solid theory will base manufacturing systems design rather on believing that the optimization software will solve the scheduling issue instead of comparing alternative modern design principles to conceive high performance manufacturing systems. This attitude is far away from a scientific rational approach to design demand characteristic and product appropriate manufacturing systems by knowing “the theory”. A scientifically formulated manufacturing theory should cover two aspects: Firstly, a proper Cartesian-based understanding and law-based modeling of manufacturing systems to describe rationally their behavior; and secondly, a library with a comprehensive set of elementary production systems’ design principles. The design principles define the functioning of the manufacturing system and the theory models the resulting dynamics of the manufacturing system. This paper exemplarily shows the increased didactic and professional benefit of such an appropriate, solid, theorem and law based manufacturing science. In addition, it also discusses the impact of presently applied manufacturing simulation in today’s context of emerging Industry 4.0 type cyber-physical production systems (CPPS).

Data-Enabled Real-Time Modeling for Production Systems with Variable Cycle Time

Cycle time is one of the essential parameters that could lead to a significant improvement on the productivity of a manufacturing system. In the traditional manufacturing systems, cycle time for each station is recommended by the vendor and determined based on production line design and through repeated experiments and simulations. Once determined, the cycle time is typically unchangeable during the production operation. Nowadays, the development of smart manufacturing enables a flexible production operation, and the cycle time of each machine can be made changeable to quickly respond to market demand fluctuation and to further improve the system efficiency. To evaluate the performance of a serial production line with variable cycle time, a novel data-enabled manufacturing system model is developed, in which sensor data inputs are allowed and cycle times are modeled as input variables. In addition, an opportunity window concept, which has been studied in the previous study and has been treated as a real-time system status measurement, is extended to the variable cycle time scenario. Numerical case studies are presented to validate the fidelity of the data-enabled model and demonstrate the influence of variable cycle time of individual machines on the production line throughput and the opportunity windows.

Research on Modeling and Scheduling Methods of an Intelligent Manufacturing System Based on Deep Learning

Under the background of intelligent manufacturing, the modeling and scheduling of an intelligent manufacturing system driven by big data have attracted increasing attention from all walks of life. Deep learning can find more hidden knowledge in the process of feature extraction of the hierarchical structure and has good data adaptability in domain adaptation. From the perspective of the manufacturing system, intelligent scheduling is irreplaceable in intelligent production when the manufacturing quantity of workpieces is small or products are constantly changing. This paper expounds the outstanding advantages of deep learning in intelligent manufacturing system modeling, which provides an effective way and powerful tool for intelligent manufacturing system design, performance analysis, and running status monitoring and provides a clear direction for selecting, designing, or implementing the deep learning architecture in the field of intelligent manufacturing system modeling and scheduling. The scheduling of the intelligent manufacturing system should integrate intelligent scheduling of part processing and intelligent planning of product assembly, which is suitable for intelligent scheduling of any kind and quantity of products and resources.

MOSAIK: A Formal Model for Self-Organizing Manufacturing Systems

In this article, we review past and current system architectures displaying self-organization in the domain of manufacturing. Based on a corpus of 84 reference papers, we find that multiagent systems (MAS) play a significant role in self-organization, especially MAS featuring bio-inspired algorithms for agent coordination. The emergence of new classes of cyber-physical systems further strengthens the prevalence of MAS on the subject. As outcome of our review, we devise the MOSAIK model, a generic model synthesizing all system architectures found in our corpus. The MOSAIK model can be used as a reference for formally comparing distinct architectures. We also use it to identify gaps for future research on self-organizing manufacturing systems. The model includes the central concepts of Agent and Artifact, which suggest that the Web is an adequate communication infrastructure for modern manufacturing systems: Agents become (autonomous) Web Agents and Artifacts become resources exposed by Web servers.

The Sensor Network for Multi-agent System Approach in Smart Factory of Industry 4.0

This research was developed to plan, monitor, and control the production in a modern manufacturing system model with heterogeneous production facilities, consisting of several automatic machine tools and conventional machine tools. Therefore, it proposed, a smart factory concept that utilises computer technology, internet networks and sensors so that the production process can be monitored. The sensor network monitors the condition of the machine tools and the status of the job. The temperature sensors, the vibration sensors, the electrical energy sensors are used to check tool conditions in machine tools. Meanwhile, the radio frequency identification (RFiD) system is used to check the status of the workpiece whether it has been completed, work in progress, or is waiting in a buffer or a pallet stocker. The information relating to the performance of the machine tools is sent using the IoT application so that through the web. The machine performance data are collected, and their status can be monitored. Likewise, job status is visible on the shop-floor control system. The sensor network model at the prototype scale had been built and tested on a laboratory scale. The test results showed that the performance of machine tools and job status were monitored properly.