Analysis Of Manufacturing Systems Research Articles

On formal modeling, analysis and optimization of reconfigurable manufacturing systems

ABSTRACT This paper deals with the formal specification and multi-objective optimization of reconfigurable manufacturing systems (RMSs). RMSs are characterized by their dynamic structure, crucial for responding to functional variations, adjusting production capacity and overcoming operational dysfuntions promptly and cos-effectively. The effective utilization of this dynamic capability depends on thorough study and optimization of RMS configurations. To tackle this challenge, a novel formalism called genetic reconfigurable object nets (Gen-RONs) is introduced. Gen-RONs formalism integrates the modeling, verification and reconfiguration capabilities of reconfigurable object nets with advanced multi-objective techniques from genetic algorithms. Within Gen-RONs, Petri nets serve to: (i) encode RMS configurations, (ii) manage RMS scheduling and (iii) employ graph transformation theory rules as genetic algorithm operators for real-time system reconfiguration. Thus, Gen-RONs formalism provides a robust framework for optimizing RMSs using genetic algorithms. To ensure deadlock-free RMS configurations, Gen-RONs incorporate a binary tree-based technique. Furthermore, Taguchi’s method is employed to fine-tune genetic algorithm parameters. The contribution of this paper is demonstrated through a case study involving an RMS and the widely used genetic algorithm NSGA-II. Significant gains in terms of the number of solutions (i.e. configurations) and the criteria values are achieved.

Dynamic modeling and real-time performance analysis of multi-product batch manufacturing systems with perishable products

Production lines handling perishable goods in industries such as petrochemical and food processing demand meticulous precision in timing. These systems are governed by stringent timeframes, where products must strictly adhere to defined residence times. Exceeding these time constraints leads to product scrapping, posing a significant threat to overall productivity. Modeling such intricate systems presents a formidable challenge, and success hinges upon a deep understanding of the complex interplay between production and batch scrapping dynamics. In response to this challenge, this paper presents a novel model for a multiproduct batch production line with perishable products. Furthermore, the real-time performance metric of Permanent Production Loss (PPL) is extended to this system using a method of PPL back-adjustment in time. To validate the fidelity of the proposed mathematical model, numerical case studies are conducted, comparing the developed model to a discrete event simulation. The results demonstrate that the model accurately represents the system at hand. The expressions for PPL evaluation and attribution are also validated using case studies.

Stochastic model analysis of a noodles manufacturing system

Stochastic model analysis of a noodles manufacturing system

Sensitivity and performance analysis of a three-unit soft biscuit manufacturing system with two types of repairers

Sensitivity and performance analysis of a three-unit soft biscuit manufacturing system with two types of repairers

Development of flexible manufacturing system

The focus of this project is the development and analysis of a Flexible Manufacturing System (FMS) for the production of a range of tables. Tables are typically used in homes and offices, where different variants are selected for different environments and uses. By implementing a flexible manufacturing system, different versions of tables can be produced in the same factory to fulfil different customer needs and preferences. This project designed and evaluated four different variants in the FMS, demonstrating aesthetic and functional differences while maintaining core similarities; this demonstrates FMSs flexibility and adaptability and provides insights into the trade-offs between design choices and manufacturing considerations. The article uses Mejabi and Solberg analysis for detailed FMS design and analysis. This analytical approach allows for thoroughly examining the FMS design, including factors such as productivity, efficiency and cost-effectiveness. The analysis further helps understand and optimise the implementation of the FMS to ensure that it effectively meets the production goals. This paper paves the way for enhancing productivity, increasing product range and improving customer satisfaction in the furniture manufacturing industry.

Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

RAMD analysis of mixed standby serial manufacturing system

RAMD analysis of mixed standby serial manufacturing system

Performance analysis of matrix-structured manufacturing systems compared to dedicated flow and reconfigurable manufacturing lines

Performance analysis of matrix-structured manufacturing systems compared to dedicated flow and reconfigurable manufacturing lines

Identification of influence of digital twin technologies on production systems: a return on investment-based approach

The object of this study is the impact of different digital twin solutions on the performance of job-shop manufacturing systems, while economic aspects are also taken into consideration. This paper proposes an approach to analyze the impact of different identification systems on the efficiency and ROI of digital twin deployment in production systems. In order to achieve this aim, let’s analyze the investment and operation cost of different Internet of Things technologies. The next phase of the research work was the definition of performance parameters, which makes it possible to analyze the impact of different digital twin solutions on the productivity of the job-shop manufacturing system. It is possible to choose four financial indicators to analyze the economic impact of digital twin solution on job-shop manufacturing: return on investment, compound annual growth rate, internal rate of return and net present value. Our approach is based on a novel agent-based simulation model using AnyLogic simulation tool. From the results of this productivity analyses, the model computes the financial indicators, which describe the expected financial impact of the investment and operation cost. It is compared the impact of barcodes and radiofrequency identification technologies on the financial and technological impact of the job-shop manufacturing environment. The numerical analysis of a job-shop manufacturing system shows, that the radiofrequency identification-based digital twin solution has 9.2 % higher return on investment, 53 % higher net present value and 1.6 % higher compound annual growth rate. The model can be easily converted to analyze other types of manufacturing systems, which can lead to increased efficiency of digital twin solutions

Immediate Transitions in Timed Continuous Petri Nets: Performance Evaluation and Control

Timed continuous Petri nets (TCPNs) are continuous-state dynamical systems that were originally defined to approximate the behavior of timed discrete event systems. In particular, it has been shown in the literature that TCPNs approximate the average marking and throughput of a class of generalized stochastic Petri nets (GSPNs), a well-known model used for the performance evaluation analysis of manufacturing, communication, logistic and traffic systems, among others. In this work, the TCPN model is enriched with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">immediate transitions</i> , which represent very fast events, resulting in a new model denoted as TCPN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> I. Then, a fast simulation algorithm for TCPN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> Is is introduced. Nevertheless, the introduction of continuous immediate transitions leads to ill-conditioned problems when analyzing the TCPN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> I model. For such reason, a couple of procedures are, here, introduced to transform a TCPN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> I model into a set of dynamically equivalent TCPNs, by removing the immediate transitions, allowing, thus, the application of analysis techniques and methods already proposed in the literature for TCPN systems. The application of the results introduced in this work for performance evaluation and model predictive control is illustrated through a manufacturing example.

Cellular manufacturing system: a descriptive bibliometric analysis, framework, implementation steps and future research direction

PurposeThe purpose of this article is to conduct a bibliometric analysis of the cellular manufacturing system (CMS) literature published from 1982–2021 to identify key issues and trends for the future.Design/methodology/approachA six-stage methodology is used to conduct a literature review, which includes: (1) article collection; (2) inclusion/exclusion criteria; (3) reviewing the articles; (4) analyzing the articles; (5) framework development; and (6) future research directions. A total of 936 CMS-specific articles are reviewed. This paper made use of three software tools: the R package, VOSviewer and SciMAT.FindingsAccording to the findings, the majority of CM researchers focused on cell formation and design. The USA, Iran and India are the top three leading publishers. Additionally, the gap and future direction of CM are discussed.Originality/valueTo the best of the authors' knowledge, the current study is the first attempt to investigate CMS evaluation through bibliometric and thematic analysis and provides a decisional framework as well as steps for CMS adoption. For individuals who are interested in understanding more about CMS and its evolution, this paper offers a starting point.

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.

Modeling and simulation of the furniture manufacturing and assembly process in the arena simulation software

The article describes issues related to creating discrete simulation models for the implementation of the furniture manufacturing and assembly process in a furniture company. The methodology of the manufacturing system analysis was presented, which is aimed to support the appropriate approach to the construction of simulation models. The scope of the work includes the technological identification of the furniture manufacturing and assembly process in real production conditions, on the basis of which the appropriate simulation model was built. The course of the process was analyzed in the Arena software on the basis of a computer simulation based on reports. As a result of the simulation of the manufacturing process with the use of information from report of usage, an area for improvement was located. The re-analysis of the material flow made it possible to propose a change in the input parameters for the simulation model in the indicated area. The results of the second simulation show significant changes in the effective use of workstations and increase in the efficiency of the production line. In practice, it can be the basis for introducing simulated changes in the production system.

Analysis of interactive manufacturing systems: Towards a performance‐based assessment methodology

Analysis of interactive manufacturing systems: Towards a performance‐based assessment methodology

Reduced order dynamical models for complex dynamics in manufacturing and natural systems using machine learning

Dynamical analysis of manufacturing and natural systems provides critical information about production of manufactured and natural resources, respectively. Current dynamic models for full industrial process plants exist as highly accurate first-principle relationships. However, their integration is computationally intensive and provides no simplified understanding of the underlying mechanisms driving the overall dynamics. Similarly, for natural systems, most dynamical models are first principle based, with high data requirements and low state accuracy. Consequently, lower-order models that may sacrifice accuracy for simplicity and ease of training can prove useful. Yet, there have been few attempts at finding low-order models of chemical manufacturing processes and natural systems, with work focusing on modeling individual mechanisms. We seek to fill this research gap by using a machine learning (ML) approach, SINDy, validated on a soybean-diesel process plant and watershed system. This ML method combines sparse, grey-box modeling with additional nonlinear optimization to identify governing dynamics as ODEs. We find a linear ODE model for the process plant that gives an accurate relation between input and output and selected internal molar flow rates reflective of underlying linear stoichiometric mechanisms and an internal mass balance. For the natural system, we modify the SINDy approach to include the effect of past dynamics on training the model, which gives a nonlinear model for streamflow dynamics. This improves dynamical transitions, but falls short of accurate state estimation. We conclude that the proposed ML approach works well for non-chaotic systems with minimal hysteresis, but is limited when this condition is not met.

Missingness analysis of manufacturing systems: A case study

Increasingly manufacturing companies are looking to use sensors to collect data from production lines to help analyse their performance. More rigorous approaches are needed to process and analyse the resulting data, particularly when considering missingness. In this paper, we present the results from a major study into missingness in Seagate’s disc head manufacturing process in Londonderry UK. Working in collaboration with company staff, a detailed approach for analysing missingness has been developed. The work shows how missing data analytics can be employed to analyse the quality of the data, identify relationships and diagnose the presence of any patterns.

Modeling, analysis and optimization of carousel-based flexible manufacturing system

ABSTRACT This manuscript presents an approach to model, evaluate, and optimize a mixed-model carousel-based flexible manufacturing system (FMS). In the proposed FMS, a carousel-based layout is enclosed by different manufacturing cells and assembly cells, which are equipped with multiple multipurpose machines (undergoing planned breakdowns), robots and a conveyor for the production and assembly tasks. The FMS exhibits intra and inter-cellular routing flexibility allowing the parts to adopt different routes within cells and in between cells to ensure continuous production under planned resources failure conditions. For system modeling, a comprehensive hierarchical Colored Petri net (CPN) approach is used, and the result of several input variables has been revealed on the output indicators in particular cycle time, throughput, and work-in-process (WIP). Further, the design of experiment (DoE) and response surface methods (RSM) have been employed for system optimization. The cycle time and WIP responses have been minimized and throughput has been maximized.

Ranking Features to Promote Diversity: an Approach Based on Sparse Distance Correlation

The improvement of sensing technology enables features of process variables to be collected during the fabrication of products. This article develops an automatic tool for process feature rankings based on these data. Based on the sensing data characteristics and the need of manufacturing system analysis, we propose two rules of the feature ranking scheme: assessing general dependency between each individual process feature and the quality variable, and satisfying a diversity rule. Specifically, we propose a feature ranking scheme based on the sparse distance correlation (SpaDC) that satisfies these two rules. Theoretical properties of the proposed algorithm are investigated. Simulation studies and two real-case studies from semiconductor manufacturing applications demonstrate that the SpaDC method ranks the features effectively given these two ranking rules. The supplementary materials for this article are available online.

Reconfigurable manufacturing system: a systematic bibliometric analysis and future research agenda

PurposeThe purpose of this paper is to conduct a systematic bibliometric analysis of reconfigurable manufacturing system (RMS) articles using VOSviewer to identify their research themes and future research trends and investigate their interconnectivity. This paper also aims to identify prominent authors, publishers, organizations, countries and their collaborations in the RMS domain.Design/methodology/approachIn this study, the Scopus database is used to retrieve 454 RMS articles published between 1988 and 2020. These articles are then investigated using VOSviewer to determine their interconnectedness, clusters and citations, as well as to generate a map based on text data. The network visualization diagrams and clusters obtained for documents, authors, sources, organizations and countries are explored to determine the current state and future trends in RMS research.FindingsA bibliometric analysis of selected articles is performed, and current research hotspots in this domain are identified. This work also investigates the current status and future research trends in this domain. The work presented also identifies top researchers, journals, countries and documents in RMS.Practical implicationsThis paper can provide academics, researchers and practitioners with additional research insights. At the same time, the research trends identified here can help to direct research and benefit researchers.Originality/valueThe study is the first attempt to review selected documents in the RMS domain using bibliometric analysis tools, and it presents a method for collecting articles, organizing them and analyzing the data.

Reliability modelling and performance analysis of bio-coal manufacturing system with deviation in demand

Reliability modelling and performance analysis of bio-coal manufacturing system with deviation in demand