Intelligent Manufacturing Systems Research Articles

Integrated scheduling of production and material delivery for the intelligent manufacturing system

This paper addresses the issue of integrated scheduling of production and material delivery in an intelligent manufacturing system. The system includes multiple flexible flowlines and a fleet of automated guided vehicles (AGVs), whose capacity is shared among the flowlines. Multiple orders from different customers are processed on the flowlines, with AGVs delivering materials in different batch sizes based on varying product quantities of orders and limited load capacity. The problem is formulated as a mixed-integer linear programming model to minimise total order delays and the number of used AGVs. To handle the complexity caused by the uneven temporal coupling relationship between production and material delivery, a bi-level neighbourhood search solution algorithm is proposed. This algorithm uses variable neighbourhood search for production scheduling and adaptive large neighbourhood search for material delivery scheduling. Extensive numerical experiments using real-case-based instances are conducted to demonstrate the satisfactory performance of our proposed algorithm. Furthermore, we emphasise the advantages of integrated scheduling compared to traditional sequential scheduling across various problem settings.

Toward sustainable process industry based on knowledge graph: a case study of papermaking process fault diagnosis

Process industry suffers from production management in terms of efficiency promotion and waste reduction in large scale manufacturing due to poor organization of the intricate relational databases. In order to enhance the suitability of intelligent manufacturing systems in process industry, this study proposed an innovative top-down structure Knowledge Graph (KG) for process fault diagnosis, and papermaking was taken as a case study. The KG consists of a normalized seven-step-built ontology, which extracted instances of papermaking knowledge via Protégé software. The exported OWL file was imported into Neo4j software for visualization of the KG. The application in papermaking drying process for fault diagnosis shows that it can depict the material and energy flows throughout the process with a clearer relationship visualization than traditional measures. They also enable rationale search for faults and identification of their potential causes. The built KG efficiently manages the vast knowledge of the process, stores unstructured data, and promotes the intelligent development of process with high reusability and dynamicity that can rapidly import new production knowledge as well as flexibly self-updating.

Sequential Optimal Trajectory Planning Scheme for Robotic Manipulators along Specified Path Based on Direct Collocation Method

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics.

Efficient Predictive Regulation Algorithms for AGV System in Industrial Internet

<p>In recent years, the industrial Internet has developed rapidly. In order to improve the reliability, real-time, and economy, Automated Guided Vehicle (AGV) in intelligent manufacturing system becomes an indispensable technology. However, the current AGV system relies too much on the fixed network bandwidth environment in information transmission and management. When the traffic demand changes frequently, this form of network configuration lacks network resource management mechanism. Further, it leads to the problems of delay, waste of network flow, and inability to dynamically allocate network resources. So it is vital to improve the AGV system. Therefore, this paper proposes three predictive control algorithms and a Network Cable Scheduling algorithm to manage the network resources. They are Markov Chain Linear Programming Regulation (MCLPR) algorithm, Prophet Linear Programming Regulation (PLPR) algorithm, and Machine Learning Linear Programming Regulation (MLLPR) algorithm. The experimental results show that PLPR and MLLPR algorithm have high efficiency in the aspect of regulation. MLLPR algorithm has the lowest cost. MLLPR algorithm has the strongest leakage limitation ability, followed by PLPR algorithm. The balance regulation efficiency of MLLPR in none “4 + 1” mode is the highest in different network cable modes.</p> <p> </p>

Inclusive manufacturing: A contribution to assembly processes with human-machine reciprocal learning

This study explores the potential synergy between neurodiversity and advanced technology within Industry 5.0, focusing on the integration of neurodiverse individuals in the workforce through Human-Machine Collaboration and Reciprocal Learning (RL). A cognitive load (CL) assessment procedure is developed using fuzzy logic inference across the dimensions of attention, memory, language, math, logic, and reading. A case study evaluates the effectiveness of RL in assisting assembly tasks. Different error-handling scenarios are compared. Experimental results show how RL can reduce the CL while improving assembly tasks efficiency, underscoring the value of intelligent systems in inclusive manufacturing, enhancing productivity and facilitating the integration of neurodiverse workers.

A novel hexagonal grid map model and regenerated heuristic factor based strategy for intelligent manufacturing system’s AGV path planning problem solving

In modern industry, flexible manufacturing systems play an indispensable role, and the precision and connectivity of map modeling are crucial for improving the efficiency and flexibility of AGV (Automated Guided Vehicle) transportation systems. Facing challenges such as spatial constraints, equipment failures, and unexpected task changes in real manufacturing environments, this paper proposes an innovative AGV path planning method based on hexagonal grid map modeling. This method significantly enhances the connectivity, sampling frequency, and safety of AGV path planning by replacing traditional square grids with hexagonal ones. Additionally, this study employs an improved ant colony algorithm combined with the hexagonal grid map for AGV path planning. The algorithm incorporates heuristic factors to effectively avoid local optima. Furthermore, by dividing the ant colony into odd and even groups and implementing a bidirectional search strategy, the comprehensive exploration capability of the algorithm is further enhanced. Experimental results show that compared to traditional square grids, the hexagonal grid reduces the optimal path length by 11.1%, and the application of heuristic factors further shortens the path length by 18.6%. The bidirectional search strategy also significantly reduces the number of iterations required by the algorithm, with a maximum reduction of 46.46%. In summary, this paper provides a new, efficient, stable, and energy-saving solution for AGV path planning systems, with thorough optimizations and improvements made in every aspect from map modeling to path planning algorithms.

A data and knowledge driven autonomous intelligent manufacturing system for intelligent factories

The non-ferrous metal industry is encountering several challenges, including production efficiency, manufacturing information fragmentation, and human health problems, which highlights the importance of implementing autonomous intelligent manufacturing systems (AIMS). Recently, the foundation model like GPT-4, has garnered attentions due to its exceptional capabilities and proficiency in diverse domains and tasks, facilitating the realization of AIMS. However, the existing foundation models can only address basic general-purpose tasks and are difficult to use for industrial applications. In this paper, we propose a data and knowledge driven AIMS with industrial-generative pretrained Transformer (Industrial-GPT) for intelligent factories. The paradigms and architecture of autonomous intelligent factories are firstly defined. Then, we explore the mechanism with knowledge graph, digital twin, and Industrial-GPT, including multi-level autonomous perception, cross layer and domain cognition, and event-driven collaborative decision-making. Finally, the detailed case study is based on the cooperation with a zinc smelting intelligent factory to achieve networked collaborative manufacturing, and explores the theory and realization mechanism of AIMS on a small scale. We explore the experimental analyses, evaluation mechanisms and platform applications of AIMS at the workshop level. We believe this will help to realize larger scale AIMS in the future.

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.

An Empirical Study of the Relationship Between Digital Transformation, Corporate Social Responsibility and Financial Performance

The article investigates the impact of corporate social responsibility (CSR) and digital transformation (DT) on corporate financial performance (CFP) indicators, as well as the moderating effect of DT on the relationship between CSR and CFP indicators. The study was conducted using Stata 17.0 software based on data (5709 observations) on 558 A-share listed manufacturing companies of the Shanghai and Shenzhen Stock for 2012-2021 (excluding financial and insurance companies, ST and ST* companies, as well as companies with incomplete data). The independent variable used in the modelling process is the CSR rating report of the Hexun website, indicators of which are reconstructed by dividing them into strategic CSR (shareholder responsibility, employee responsibility, customer responsibility) and altruistic CSR (environmental responsibility and social contribution). The dependent variable is 2 CFP indicators: return on equity (ROE) and TOBIN’s Q. The DT index is used as a moderating variable, for the calculation of which the texts of companies’ annual reports were segmented using the Python programming language and standardised data on the frequency of keywords in 4 dimensions: digital technology application, Internet business model, intelligent manufacturing and contemporary information system; the weights for each dimension index was determined using the entropy approach. The control variables are corporate assets (Size), financial leverage level (Lev), current ratio (Cur), firm growth (Gro), shareholding ratio of the largest shareholder (First) and technological innovation (RD) of companies. The study also considered the year and industry factors. To study the moderating effect of DT on the relationship between CSR and CFP indicators of companies in different situations, the grouping variables chosen were the ownership type (state-owned and non-state-owned companies), the enterprise lifecycle (cash flow portfolio method was used to identify its stages) and the degree of market competition (Herfindahl-Hirschman Index). The modelling is based on a fixed-effects regression model. The study found that both CSR and DT have a positive impact on CFP, and that DT moderates the relationship between CSR and CFP, indicating that integrating digital technologies with sustainable development strategies can enhance financial benefits. The study also highlights the importance of different dimensions of corporate social responsibility. A high level of digital transformation strengthens the promotion effect of strategic social responsibility on current and future financial success of companies. Meanwhile, the promotion effect of altruistic social responsibility on current financial performance is significant. Heterogeneity analysis shows that the positive moderating effect of digital transformation on the relationship between corporate social responsibility and financial performance mainly applies to private firms, firms in the mature stage, and firms with low market competition. The article offers practical advice for businesses seeking to use digital transformation to maximise economic and social value.

Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm

With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time.

Cloud 3D Printing Information Research

3D printing technology, as a major technological change in the global manufacturing industry, has gradually become the trend of The Times. As a service-oriented intelligent manufacturing system, cloud manufacturing has been widely studied by scholars at home and abroad in recent years. This paper takes cloud manufacturing as the carrier, studies the informatization of 3D printing service under cloud environment, elaborates the construction of 3D printing service information model in detail, and instantiates through Agent mapping model. This paper creates an implementation path for cloud 3D printing to provide on-demand precision manufacturing services.

Operating minimally intelligent agent-based manufacturing systems across the Average demand Interval – coefficient of variation (ADI-CV) demand state space

ABSTRACT Minimally Intelligent Agent-Based Manufacturing concerns the provision of agents with the minimal intelligence required to autonomously negotiate and broker work across machines and jobs. The minimal intelligence of agents can be updated in real-time and when coupled with technologies, such as Additive Manufacturing (AM), robotics, automated inventory, and Computer Numerical Control (CNC) affords highly flexible, re-configurable, resilient and responsive systems. The concept has become topical as changes to global supply chains are necessitating a shift to responsive and resilient on-demand manufacturing. However, understanding and characterising how these systems operate under various demand profiles is required to support operators operating these future systems. This paper reports a numerical study into the operating behaviour of minimally intelligent agent-based manufacturing systems operating across the Average Demand Interval and Coefficient of Variation (ADI-CV) demand state space. An established state space used in spare part supply chain research. The results show minimally intelligent manufacturing systems are stable across much of the ADI-CV demand space 85%, 63% and 76–84% for constant, triangular and lognormal manufacturing time distribution profiles respectively. The stability is largely independent of the combination of intelligence. Rather, the combination of intelligence impacts job Time-in-System and system response.

Preface

The 2023 4th International Conference on Advanced Materials and Intelligent Manufacturing (ICAMIM 2023) was held in Guangzhou, China during December 1st to 3rd, 2023. The series of ICAMIM conference was created with the aim to be an interdisciplinary incubator for the worldwide exchange of information and innovative ideas about the latest research results in the fields of advanced materials and intelligent manufacturing.At ICAMIM 2023, we wished to bring together a broad community of researchers from different branches of the rapidly developing complex science to discuss the fundamental theoretical challenges as well as experimental and applied aspects of many practical problems. The agenda was conducted mainly based on three parts, covering keynote speeches, oral & poster presentations, and academic tour.There were six keynote speeches and many regular oral talks in the morning and afternoon sessions. The topics of keynote speeches cover: a) Scientific Carbon Reduction, Green Development, Manufacturing Industry to Achieve the Goal of “Double Carbon” Efforts; b) ChatGPT with Intelligent Manufacturing System; c) Challenges Facing the Industrialization of Sodium-Ion Batteries; d) Microstructure Control and Application Properties of Ultra-High Strength and Toughness Steels; e) Double Growing Carbon Background Material Base Environmental Functional Materials Development Prospects; f) Current Status and Development of the New Materials Industry in Guangdong. These speeches allowed all the participants to interact with the speakers and people around and get some new insights from each other. An academic discussion part was also held, the topic of which was related to the recent and anticipated future progress in advanced materials and intelligent manufacturing, discussing a variety of questions and opinions important for the understanding of the technology and management methods of the industry itself.The papers submitted to this Conference were carefully reviewed by expert referees, covering topics on: Composites, New feature Materials, Integrated Manufacturing Systems, Advanced Manufacturing Technology, Intelligent Mechatronics, etc. We are very grateful to the reviewers for their efficient and thoughtful actions. Various excellent papers were included here based on the referee reports.We are thankful to the members of Technical Program Committee for their time and efforts in preparing the program of ICAMIM 2023. We also appreciate the behind-the-scenes work of the members of Organizing Committee in preparing the conference site, arrangements for excursions and accommodation, handling the financial support for participants, and so on. Finally, we acknowledge the publishing support from Journal of physics: Conference SeriesThe Committee of ICAMIM 2023List of Committee Member is available in this pdf.

Effective metaheuristic and rescheduling strategies for the multi-AGV scheduling problem with sudden failure

This paper investigates a novel scheduling problem, the multi-AGV scheduling problem with sudden failure (MASP-SF), which is of great importance in modern intelligent manufacturing systems. The problem considers how to assign tasks to multiple AGVs with the objective of minimizing the total cost under two events of normal production and sudden failure in the workshop. In the event of normal production, a population-based variable neighborhood search (PVNS) algorithm with effective strategies such as rule-based stochastic method in the initialization stage, pancake flipping strategy in the shaking stage, improved neighborhood structures in the variable neighborhood descent stage is presented to solve the multi-AGV scheduling problem. In the event of sudden failure, a mixed-integer linear programming model and two rescheduling strategies are proposed to solve MASP-SF. The two rescheduling strategies are the rapid repair rescheduling strategy and a rescheduling strategy based on an improved NNH. To validate the above ideas, we conduct comprehensive and in-depth experiments on a practical plant and statistical analysis of the resulting data. The results show that the scheduling solutions generated by the rescheduling strategies are feasible, and the proposed PVNS algorithm has a superior performance compared to existing algorithms in multi-AGV scheduling problem.

Industry 5.0 and sustainable manufacturing: a systematic literature review

PurposeIndustry 5.0 (I5.0) is eventually set to supersede Industry 4.0 (I4.0), despite the fact that I4.0 continues to gain ground in emerging nations like India. Now India is aspiring to be a global manufacturing hub, and I5.0 offers enormous potential to position India as a forerunner in intelligent and collaborative manufacturing systems. Therefore, this research article aims to understand the relationship between I5.0 and sustainable manufacturing (SM) thoroughly; pinpoint its impact and implementation challenges; analyze its impact on Triple-Bottom-Line (TBL) sustainability; and present an inclusive framework for I5.0 implementation for Indian manufacturing enterprises.Design/methodology/approachThe coexistence of two industrial revolutions raises questions, which necessitates debates and explanations. Thus, the systematic literature review (SLR) approach is used to address this issue and this study used Web of Science, Scopus, Science Direct and Google Scholar databases. Following a critical SLR, 82 research papers have been cited in this article, and the majority of cited articles were published from 2010 to 2022, to ensure a focused analysis of pertinent and recent scholarly contributions.FindingsI4.0 is considered to be technology-driven, however, I5.0 is perceived to be value-driven. I5.0 is not a replacement or a chronological continuation of the I4.0 paradigm. The notion of I5.0 offers a distinct perspective and emphasizes the necessity of research on SM within the TBL sustainability boundaries. I5.0 introduces a new TBL: resilience in value creation, human well-being and sustainable society. Indeed, I5.0 seems to be economically, socially, and environmentally sustainable while manufacturing products with high productivity.Practical implicationsTheoretical implications pertain to restructuring business models and workforce transformation, whereas practical implications underscore the significance for manufacturing enterprises to embrace I5.0 for their sustainable development. By understanding the nuanced relationship between I5.0 and SM, enterprises can navigate implementation challenges, maximize TBL sustainability and embrace an inclusive I5.0 framework for high productivity and resilience.Originality/valueThe existing literature presents the general notion of I5.0 but lacks in-depth TBL sustainability analysis. This research used a systematic and rigorous SLR approach that evaluates the existing literature, enables an in-depth understanding, identifies research gaps and provides evidence-based recommendations for the decision-making process. Furthermore, this research aims to stand on an unbiased assessment, exploring theoretical and practical implications of I5.0 implementation for manufacturing enterprises and suggesting future research avenues.

Industry 4.0 Innovation: A Systematic Literature Review on the Role of Blockchain Technology in Creating Smart and Sustainable Manufacturing Facilities

Industry 4.0 has revolutionized manufacturing processes and facilities through the creation of smart and sustainable production facilities. Blockchain technology (BCT) has emerged as an invaluable asset within Industrial Revolution 4.0 (IR4.0), offering increased transparency, security, and traceability across supply chains. This systematic literature review explores the role of BCT in creating smart and sustainable manufacturing facilities, while exploring its implications for supply chain management (SCM). Through a detailed examination of 82 research articles, this review highlights three areas where BCT can have a dramatic effect on smart and sustainable manufacturing: firstly, BCT can promote green production methods by supporting efficient resource use, waste reduction strategies and eco-friendly production methods; and secondly, it allows companies to implement smart and eco-friendly manufacturing practices through BCT solutions. BCT promotes intelligent manufacturing systems by facilitating real-time data sharing, predictive maintenance, and automated decision-making. Furthermore, BCT strengthens SCM by increasing visibility, traceability, and collaboration between partners of SC operations. The review also highlights the potential limitations of BCT, such as scalability challenges and the need for standardized protocols. Future research should focus on addressing these limitations and further exploring the potential of BCT in IR4.0.

Applications and Challenges of Fieldbus Technology in Intelligent Manufacturing Systems

Abstract This study advances the field of intelligent manufacturing by integrating automation, information technology, and digitalization into a novel “five horizontal and three vertical” system architecture. We introduce an enhanced model-free adaptive control (MFAC) algorithm that utilizes a dynamic linearization method alongside pseudo-partial derivatives (PPD) for optimizing data transfer within intelligent manufacturing systems. Using CANoe software for simulation, our research demonstrates the improved MFAC algorithm’s superior bus message transmission efficiency by 0.0224 to 0.026 and a significant reduction in the network load factor by 0.2068. These advancements highlight the efficacy of fieldbus technology in reducing network congestion and enhancing real-time performance in intelligent manufacturing environments.

Internet of Production: Unleashing the Full Potential of Industry 4.0 – A Comprehensive Review of Trends, Drivers, and Challenges

The rapid advancement of Industry 4.0 technologies has revolutionized industrial processes and production systems, creating unprecedented optimization opportunities. The Internet of Production (IoP) concept has emerged as a transformative approach that capitalizes on integrating cyber-physical systems, big data analytics, artificial intelligence, and the Industrial Internet of Things (IIoT) to drive efficient, flexible, and intelligent manufacturing. This study explores the current state of expert discourse on the symbiotic relationship between IoP and Industry 4.0, focusing on production technology, automation, and digitalization. For this purpose, we performed a content-centric qualitative review in a systematic manner. The findings show that connectivity, data-driven decision-making, flexibility, intelligent optimization, digital twins, and collaborative networks promotes IoP's potential within Industry 4.0. Overall, this study explains how harnessing the potential of IoP by enhancing efficiency, productivity, and innovation in industrial processes contributes to Industry 4.0, and how understanding the strengths, weaknesses, opportunities, and threats of IoP, leveraging advanced technologies, and data-driven approaches can pave the way for the future of efficient, flexible, and intelligent manufacturing systems.

《智能自动化制造系统》课程教学改革与思考

《智能自动化制造系统》课程教学改革与思考

Design and implementation of intelligent manufacturing system based on sensor networks and cloud computing technology

Design and implementation of intelligent manufacturing system based on sensor networks and cloud computing technology