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Processes
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Modeling, Simulation and Control of Flexible Manufacturing Systems
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Modeling, Simulation and Control of Flexible Manufacturing Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 34952

Special Issue Editor

Dr. Uros Zuperl

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
Interests: control; monitoring systems; cognitive systems; cyber-physical systems; machining; optimization; modeling; applied artificial intelligence; fixtures in machining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The most innovative way to modernize flexible manufacturing systems is to introduce advanced technologies such as artificial intelligence, machine learning, cloud computing, the Internet of Things, and cognitive monitoring/control into manufacturing processes. According to paradigm 4.0, these emerging digital technologies have the potential to transform flexible manufacturing systems to a new, more efficient level. These transformed manufacturing systems must pre-process an increased amount of process data in real-time and frequently incorporate optimization abilities to improve the efficiency of sub-processes. The increasing complexity of flexible manufacturing systems causes significant difficulties in their optimization, modeling, and monitoring. Furthermore, the classical process optimization, modeling and monitoring approaches cannot respond to real-time data. Therefore, it is practically necessary to develop new advanced approaches for process optimization, modeling, and monitoring to support engineers’ decisions for process corrections.                                                   

This Special Issue of Processes will cover recent advances in the modeling, optimization, monitoring, and control of different sub-processes in flexible manufacturing with a particular interest in machining.

Dr. Uros Zuperl
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • flexible manufacturing systems
  • machining
  • modelling
  • optimization
  • monitoring and control systems
  • applied artificial intelligence
  • cloud manufacturing
  • cyber-physical systems
  • cognitive systems
  • cost reduction

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Published Papers (10 papers)

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Research

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31 pages, 7638 KiB  
Article
A Low-Carbon Scheduling Method of Flexible Manufacturing and Crane Transportation Considering Multi-State Collaborative Configuration Based on Hybrid Differential Evolution
by Zhengchao Liu, Liuyang Xu, Chunrong Pan, Xiangdong Gao, Wenqing Xiong, Hongtao Tang and Deming Lei
Processes 2023, 11(9), 2737; https://doi.org/10.3390/pr11092737 - 13 Sep 2023
Viewed by 1009
Abstract
With increasingly stringent carbon policies, the development of traditional heavy industries with high carbon emissions has been greatly restricted. Manufacturing companies surveyed use multifunctional machining machines and variable speed cranes, as the lack of rational planning results in high energy wastage and low [...] Read more.
With increasingly stringent carbon policies, the development of traditional heavy industries with high carbon emissions has been greatly restricted. Manufacturing companies surveyed use multifunctional machining machines and variable speed cranes, as the lack of rational planning results in high energy wastage and low productivity. Reasonable scheduling optimization is an effective way to reduce carbon emissions, which motivates us to work on this research. To reduce the comprehensive energy consumption of the machining process and transportation process in an actual manufacturing environment, this paper addresses a new low-carbon scheduling problem of flexible manufacturing and crane transportation considering multi-state collaborative configuration (LSP-FM&CT-MCC). First, an integrated energy consumption model based on multi-state machining machines and cranes is established to optimize the overall energy efficiency of the production process. Then, a new hybrid differential evolution algorithm and firefly algorithm with collaborative state optimization strategy (DE-FA-CSOS) is proposed to solve the proposed MIP model. In DE-FA-CSOS, the differential evolution algorithm (DE) is used for global search, and the firefly algorithm (FA) is used for local search. The collaborative state optimization strategy (CSOS) is proposed to guide the search direction of the DE-FA algorithm, which greatly improves the performance of the hybrid algorithm. Finally, the practicality and superiority of the solution method are verified by examples. The results show that machining and transportation energy consumption is reduced by 25.17% and 34.52%, respectively. In the context of traditional optimization methods and manual scheduling modes facing failure, the method has a broad application background for manufacturing process optimization in such workshops, which is of guiding significance for promoting the low-carbon development of traditional heavy industry manufacturing. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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18 pages, 4941 KiB  
Article
A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment
by Johanna Marie Failing, José V. Abellán-Nebot, Sergio Benavent Nácher, Pedro Rosado Castellano and Fernando Romero Subirón
Processes 2023, 11(3), 668; https://doi.org/10.3390/pr11030668 - 22 Feb 2023
Cited by 8 | Viewed by 2870
Abstract
Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming [...] Read more.
Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0 is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with plug and play capabilities, minimum interruption time during setup and minimal experimental tests. In this paper, we propose a TCM system based on low-cost and non-invasive sensors that are plug and play devices, an IoT platform for fast deployment and a mobile app for receiving operator feedback. The system is based on a sensing node by Arduino Uno Wi-Fi that acts as an edge-computing node to extract a similarity index for tool wear classification; a machine learning node based on a BeagleBone Black board that builds the machine learning model using a Python script; and an IoT platform to provide the communication infrastructure and register all data for future analytics. Experimental results on a CNC lathe show that a logistic regression model applied on the machine learning node can provide a low-cost and straightforward solution with an accuracy of 88% in tool wear classification. The complete solution has a cost of EUR 170 and only a few hours are required for deployment. Practitioners in SMEs can find the proposed approach interesting since fast results can be obtained and more complex analysis could be easily incorporated while production continues using the operator’s feedback from the mobile app. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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26 pages, 9754 KiB  
Article
Optimization of Hydraulic Fine Blanking Press Control System Based on System Identification
by Yuwen Shu, Yanxiong Liu, Zhicheng Xu, Xinhao Zhao and Mingzhang Chen
Processes 2023, 11(1), 59; https://doi.org/10.3390/pr11010059 - 27 Dec 2022
Cited by 3 | Viewed by 2392
Abstract
Fine-blanking is a molding process based on the common blanking process, which obtains hydrostatic stress through blank holder reverse jacking, in order to increase material plasticity. It requires special equipment, namely a fine-blanking press, to complete the fine-blanking process. In this paper, the [...] Read more.
Fine-blanking is a molding process based on the common blanking process, which obtains hydrostatic stress through blank holder reverse jacking, in order to increase material plasticity. It requires special equipment, namely a fine-blanking press, to complete the fine-blanking process. In this paper, the problem of the speed of the slide block fluctuation found in the actual use of a 12,000 kN hydraulic fine-blanking press after multi-stage pressure source optimization is studied. Firstly, the mathematical model of the motion of the slide block in the blanking stage of the hydraulic fine blanking press is established, and the accurate mathematical model in the blanking stage of the hydraulic fine-blanking press is obtained through the least square method system identification experiment. Aiming at the complex working situation of the fine-blanking press, a phased PID control strategy is creatively proposed. The optimal PID control parameters are obtained by a genetic algorithm, and established a fuzzy PID controller for the blanking stage to accurately control the movement speed of the slide block. The results show that the new control strategy is very effective in improving the movement accuracy of the slide block, effectively improving the machining accuracy and reducing the impact vibration of the hydraulic system. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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38 pages, 7634 KiB  
Article
The Impact of Additive Manufacturing on Supply Chain Management from a System Dynamics Model—Scenario: Traditional, Centralized, and Distributed Supply Chain
by Jairo Nuñez Rodriguez, Hugo Hernando Andrade Sosa, Sylvia Maria Villarreal-Archila and Angel Ortiz
Processes 2022, 10(12), 2489; https://doi.org/10.3390/pr10122489 - 23 Nov 2022
Cited by 2 | Viewed by 3009
Abstract
In order to describe the impact that the appropriation of additive manufacturing (AM) has on the supply chain (SC), a validated system dynamics model representing vectorially multiple products and multiple demands in different periods was used as a basis to apply to a [...] Read more.
In order to describe the impact that the appropriation of additive manufacturing (AM) has on the supply chain (SC), a validated system dynamics model representing vectorially multiple products and multiple demands in different periods was used as a basis to apply to a case study of medical implant manufacturing, configuring three chain scenarios: 1. traditional supply chain with subtractive manufacturing, 2. centralized supply chain with additive manufacturing, and 3. decentralized supply chain with additive manufacturing. It was possible to notice that the production time is longer in additive manufacturing compared to traditional manufacturing and the cycle time and total demand closure were lower in traditional manufacturing. In addition, it was observed that the AM performance is significantly better in conditions of lower demand, which can be attributed to the characteristics of customization and small batches that this type of production approach implies. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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18 pages, 4285 KiB  
Article
A Fault-Tolerant and a Reconfigurable Control Framework: Application to a Real Manufacturing System
by Imane Tahiri, Alexandre Philippot, Véronique Carré-Ménétrier and Abdelouahed Tajer
Processes 2022, 10(7), 1266; https://doi.org/10.3390/pr10071266 - 27 Jun 2022
Cited by 5 | Viewed by 1825
Abstract
In this paper, we propose a framework to implement a fault-tolerant and a reconfigurable distributed control approach in programmable logic controller (PLC) for manufacturing systems (MS). The reconfiguration methodology adopted in this paper is based on supervisory control theory (SCT), and it is [...] Read more.
In this paper, we propose a framework to implement a fault-tolerant and a reconfigurable distributed control approach in programmable logic controller (PLC) for manufacturing systems (MS). The reconfiguration methodology adopted in this paper is based on supervisory control theory (SCT), and it is triggered following sensor fault detection. The lost information about these sensors is replaced by timed information allowing the MS to continue its operations. The switch from a normal behavior to a degraded behavior when a sensor fault appears is ensured by reconfiguration rules. The main objective of our framework is to implement the obtained control into a PLC. To meet this objective, the distributed controllers of the two operating modes as well as the reconfiguration rules are interpreted into different Grafcet models. The implementation of these different models is verified by a checker-model technique before being tested on a digital twin and validated on a real MS. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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21 pages, 5742 KiB  
Article
A Cloud-Based System for the Optical Monitoring of Tool Conditions during Milling through the Detection of Chip Surface Size and Identification of Cutting Force Trends
by Uroš Župerl, Krzysztof Stepien, Goran Munđar and Miha Kovačič
Processes 2022, 10(4), 671; https://doi.org/10.3390/pr10040671 - 30 Mar 2022
Cited by 6 | Viewed by 2512
Abstract
This article presents a cloud-based system for the on-line monitoring of tool conditions in end milling. The novelty of this research is the developed system that connects the IoT (Internet of Things) platform for the monitoring of tool conditions in the cloud to [...] Read more.
This article presents a cloud-based system for the on-line monitoring of tool conditions in end milling. The novelty of this research is the developed system that connects the IoT (Internet of Things) platform for the monitoring of tool conditions in the cloud to the machine tool and optical system for the detection of cutting chip size. The optical system takes care of the acquisition and transfer of signals regarding chip size to the IoT application, where they are used as an indicator for the determination of tool conditions. In addition, the novelty of the presented approach is in the artificial intelligence integrated into the platform, which monitors a tool’s condition through identification of the current cutting force trend and protects the tool against excessive loading by correcting process parameters. The practical significance of the research is that it is a new system for fast tool condition monitoring, which ensures savings, reduces investment costs due to the use of a more cost-effective sensor, improves machining efficiency and allows remote process monitoring on mobile devices. A machining test was performed to verify the feasibility of the monitoring system. The results show that the developed system with an ANN (artificial neural network) for the recognition of cutting force patterns successfully detects tool damage and stops the process within 35 ms. This article reports a classification accuracy of 85.3% using an ANN with no error in the identification of tool breakage, which verifies the effectiveness and practicality of the approach. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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15 pages, 7985 KiB  
Article
Automated Stacker Cranes: A Two-Step Storage Reallocation Process for Enhanced Service Efficiency
by Bashir Salah, Mohammed Alnahhal and Rafiq Ahmad
Processes 2022, 10(1), 2; https://doi.org/10.3390/pr10010002 - 21 Dec 2021
Cited by 4 | Viewed by 5446
Abstract
Automated storage and retrieval systems (AS/RS) play a key role in improving the performance of automated manufacturing systems, warehouses, and distribution centers. In the modern manufacturing industry, the term (AS/RS) refers to various methods under computer control for storing and retrieving loads automatically [...] Read more.
Automated storage and retrieval systems (AS/RS) play a key role in improving the performance of automated manufacturing systems, warehouses, and distribution centers. In the modern manufacturing industry, the term (AS/RS) refers to various methods under computer control for storing and retrieving loads automatically from defined storage locations. Using an (AS/RS) is not considered a value-added activity. Therefore, the longer (AS/RS) travels, the more expensive the warehousing process becomes. This paper presents an algorithm for minimizing total travel distance/time between input/output (I/O) stations. The proposed algorithm is used to manage the storage and retrieval orders on warehouse shelves in class-based storage on the storage racks. It contains two steps: the first step is to evacuate some storage compartments (locations) near the I/O station; in the second step, some tote bins are reallocated to compartments closer to the I/O station. Among the features of this algorithm are mechanisms that determine the number of reallocated tote bins, which tote bins to reallocate, and in which direction (toward the I/O station or away from it). A simulation model using R software developed specifically for this purpose was used to validate the suggested method. Based on the results, the new method can reduce the service time per order by about 10% to 20%, depending on parameters like the number of orders and the height of the storage rack. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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16 pages, 2021 KiB  
Article
Improving Retail Warehouse Activity by Using Product Delivery Data
by Aurelija Burinskienė and Tone Lerher
Processes 2021, 9(6), 1061; https://doi.org/10.3390/pr9061061 - 17 Jun 2021
Cited by 2 | Viewed by 3833
Abstract
This paper presents a research study which is dedicated to the improvement in retail warehouse activity. This study aims to improve activity by identifying an efficient order picking strategy. (1) Background: The literature review shows the application of order picking strategies, but research [...] Read more.
This paper presents a research study which is dedicated to the improvement in retail warehouse activity. This study aims to improve activity by identifying an efficient order picking strategy. (1) Background: The literature review shows the application of order picking strategies, but research related to their selection lacks an integrated approach. (2) Methods: The authors use the discrete event simulation method for the analysis of order picking strategies. The application of the discrete event simulation method enables various scenario tests in retail warehouses, allowing one to benchmark order picking strategies. By using the simulation model, experiments were designed to evaluate order picking strategies that are dependent on the delivery of the product distance variable. This research uses analysis of cost components and helps to identify the best possible order picking strategy to improve the overall warehouse performance. The authors benchmarked order picking strategies and presented constraints following product delivery data concerning their applications. (3) Results: The results presented show that the application of the order sorting strategy delivers 46.6% and the order batching strategy 6.7% lower costs compared to the single picking strategy. The results of the order batching strategy could be improved by 8.34% when the product clustering action is used. (4) Conclusions: The authors provide a theoretical framework which follows the application of order picking strategies using the product delivery data approach, which is the main scientific novelty of this paper. Recommendations are provided regarding the application of the proposed framework for the future improvement in retail warehouse activity. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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18 pages, 1375 KiB  
Article
Manufacturing Control System Development for an In Vitro Diagnostic Product Platform
by Brian Boylan, Olivia McDermott and Niall T. Kinahan
Processes 2021, 9(6), 975; https://doi.org/10.3390/pr9060975 - 31 May 2021
Cited by 8 | Viewed by 3769
Abstract
The current in vitro diagnostic design process is a combination of methods from engineering disciplines and from government regulatory agencies. The goal of design processes that have been developed is to ensure that a new product meets the user’s expectations and is safe [...] Read more.
The current in vitro diagnostic design process is a combination of methods from engineering disciplines and from government regulatory agencies. The goal of design processes that have been developed is to ensure that a new product meets the user’s expectations and is safe and effective in providing its claimed benefits and proper functioning, otherwise known as the essential design outputs. In order to improve the ability of designers and auditors to ascertain the safety and efficacy of a product, the use of design controls has been adopted that specify a method of evaluating the design process at several key stages. The main objective of this research was to examine the resolution and architectural details necessary to build an adequate manufacturing control system to assure the EDO outputs in large IVD instruments in the company under study. The control system is the defined inspections and test processes to delineate between acceptable and unacceptable product before release for sale. The authors reviewed current design control regulatory requirements within the IVD industry, as well as design controls in other regulated industries. This research was completed to determine what opportunities could be transferred to large in-vitro IVD instruments using an IVD manufacturer as a case study. In conclusion, the research identified three areas where a properly configured EDO can add value within IVD instrument design and manufacture, namely: (1) development of a control system which is fit for purpose; (2) a mechanism to manage and proliferate key design knowledge within the organisation and thereby manage outsourced services; and (3) implementing a scaled engineering change process because changes impacting EDO naturally require extra regulatory and engineering oversight. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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Review

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26 pages, 5293 KiB  
Review
A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems
by Prita Meilanitasari and Seung-Jun Shin
Processes 2021, 9(8), 1391; https://doi.org/10.3390/pr9081391 - 11 Aug 2021
Cited by 11 | Viewed by 5161
Abstract
This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, [...] Read more.
This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Flexible Manufacturing Systems)
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