Topic Editors

School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, China
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China

Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle

Abstract submission deadline
31 March 2024
Manuscript submission deadline
30 June 2024
Viewed by
11534

Topic Information

Dear Colleagues,

In recent years, research and development activities have had huge impacts on advanced manufacturing paradigms, systems and enabling technologies during the whole life-cycle of products, especially in the era of Internet for Industry 4.0/5.0. Topics concerned with keywords like digitalization, intelligentization, and servitization in the context of a networked environment often constitute the focus of both academic and industrial fields. In fact, product life cycle activities are at least dealt with product design, production, usage and maintenance aspects. So it is very important to fuse operational technologies (OTs) with information technologies (ITs) and let them become in reality, from the angle of advanced manufacturing paradigms, architectures, systems, methods, key enabling technologies, case studies, and industrial applications, etc.

On the basis of the reasons mentioned above, this special Topic collection aims to explore a wide range of issues related to the advanced systems and enabling technologies behind product design, production, usage and maintenance under the consideration of different manufacturing paradigms. Potential authors can feel free any involved group journals as the host of their manuscripts. We welcome original research articles, reviews, short communication and technical notes. Research areas include (but are not limited to) the following topics:

  • Advanced manufacturing paradigms, architectures, systems, methods, key enabling technologies, case study, and industrial applications, such as smart manufacturing, service-oriented manufacturing, social manufacturing, cloud manufacturing, networked collaborative manufacturing, digital manufacturing, mass customization, etc.
  • Advanced product design methods, key enabling technologies, case study, and industrial applications, such as design for X, product service system design, product mass customization design, product platform and modular design, intelligent computing design, generative design, crowdsourcing design, electromechanical system design, materials design, etc.
  • Advanced product production methods, key enabling technologies, case study, and industrial applications, such as intelligent factory, production lines, equipment modelling, process technologies and planning, APS/MES/DCS, production process monitoring, quality control, materials processing and logistics, manufacturing performance analysis and optimization, etc.
  • Advanced product usage and maintenance methods, key enabling technologies, case study, and industrial applications, such as product fault diagnosis and reliability, product maintenance, remote monitoring and performance prediction of product usages, MRO, product service systems, workflow modelling, etc. (here, the usage and maintenance of products includes activities for both daily life and industrial uses)
  • Artificial intelligence in manufacturing including datasets, computing power, models and algorithms such as machine learning, deep learning, generative AI, knowledge graphs, causal inference, large language models, prompt learning, multi-modal models, collective intelligence, swarm intelligence, federated learning, transfer learning, representation learning, few-shot learning, etc.
  • Digitalization, collaboration and servitization in manufacturing
  • Robotics and robots in manufacturing
  • Metaverse and VR/AR in manufacturing
  • Other new IT technologies in manufacturing
  • Next-generation industrial software and hardware models on industrial Internet

Please note that authors can submit their papers to the special topic collection at any time. Papers will be published online immediately after their acceptance and without delays caused by whether all paper collections are ready.

We look forward to hearing from you.

Prof. Dr. Pingyu Jiang
Prof. Dr. Gang Xiong
Prof. Dr. Jihong Yan
Topic Editors

Keywords

  • smart manufacturing
  • service-oriented manufacturing
  • social manufacturing
  • cloud manufacturing
  • digital manufacturing
  • networked collaborative manufacturing
  • product design
  • product production
  • product usage and maintenance

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400 Submit
Journal of Manufacturing and Materials Processing
jmmp
3.2 5.5 2017 14.2 Days CHF 1800 Submit
Machines
machines
2.6 2.1 2013 15.6 Days CHF 2400 Submit
Robotics
robotics
3.7 5.9 2012 17.3 Days CHF 1800 Submit
Systems
systems
1.9 3.3 2013 16.8 Days CHF 2400 Submit

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Published Papers (11 papers)

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17 pages, 854 KiB  
Review
Surveying Quality Management Methodologies in Wooden Furniture Production
Systems 2024, 12(2), 51; https://doi.org/10.3390/systems12020051 - 03 Feb 2024
Viewed by 533
Abstract
Furniture production is a specific industrial sector with a high human labor demand, a wide range of materials processed, and short production runs caused by high customization of end products. The difficulty of measuring the aesthetic requirements of customers is also specific to [...] Read more.
Furniture production is a specific industrial sector with a high human labor demand, a wide range of materials processed, and short production runs caused by high customization of end products. The difficulty of measuring the aesthetic requirements of customers is also specific to furniture. This review of academic papers identifies and explains effective quality management strategies in furniture production. The reviewed literature highlights a range of quality management methodologies, including concurrent engineering (CE), total quality management (TQM), lean manufacturing, lean six sigma, and kaizen. These strategies encompass a variety of pro-quality tools, such as 5S, statistical process control (SPC), quality function deployment (QFD), and failure mode and effects analysis (FMEA). The strengths of these quality management strategies lie in their ability to enhance efficiency, reduce waste, increase product diversity, and improve product quality. However, the weaknesses concern implementation challenges and the need for culture change within organizations. Successful quality management in furniture production requires tailoring strategies to the specific context of the furniture production industry. Additionally, the importance of sustainability in the furniture industry is emphasized, which entails incorporating circular economy principles and resource-efficient practices. The most important finding from the literature analysis is that early detection and correction of poor quality yields the most beneficial outcomes for the manufacturer. Therefore, it is essential to strengthen the rigor of quality testing and analysis during the early stages of product development. Consequently, a deep understanding of consumer perspectives on required furniture quality is crucial. The review identified two research gaps: (1) the impact of unnecessary product over-quality on the efficiency of furniture production and (2) the influence of replacing CAD drawings with a model-based definition (MBD) format on quality management in furniture production. Full article
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26 pages, 675 KiB  
Article
A Study of the Impact of Executive Corruption on Corporate Innovation
Systems 2024, 12(1), 25; https://doi.org/10.3390/systems12010025 - 11 Jan 2024
Viewed by 791
Abstract
Both executive corruption and corporate innovation are important factors affecting corporate development. This paper explores the impact of executive corruption on corporate innovation and examines the mechanism of their effects from the perspective of financing constraints. It is found that executive corruption significantly [...] Read more.
Both executive corruption and corporate innovation are important factors affecting corporate development. This paper explores the impact of executive corruption on corporate innovation and examines the mechanism of their effects from the perspective of financing constraints. It is found that executive corruption significantly inhibits corporate innovation in general. In addition, financing constraints act as a mediator between executive corruption and corporate innovation, i.e., executive corruption exacerbates the financing constraints faced by firms and affects the access to and allocation of corporate resources, thus leading to a decrease in corporate innovation inputs and outputs. Further, the inhibitory effect of executive corruption on firm innovation is more pronounced in firms with low quality internal controls, strong professional background of executives, low quality external audit, low shareholding of institutional investors, strong political affiliation, and state-owned enterprises. Full article
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23 pages, 4157 KiB  
Article
A Predictive Control Model of Bernoulli Production Line with Rework Loop for Real-Time WIP Optimization in Permutation Flowshop
Machines 2024, 12(1), 20; https://doi.org/10.3390/machines12010020 - 29 Dec 2023
Viewed by 666
Abstract
Permutation flowshop design and optimization are crucial in industry as they have a direct impact on production scheduling and efficiency. The ultimate goal is to model the production system (PSM) based on revealing the fundamental principles of the production process, and to schedule [...] Read more.
Permutation flowshop design and optimization are crucial in industry as they have a direct impact on production scheduling and efficiency. The ultimate goal is to model the production system (PSM) based on revealing the fundamental principles of the production process, and to schedule or reschedule production release plans in real time without interrupting work-in-progress (WIP). Most existing PSMs are focused on static production processes which fail to describe the dynamic relationships between machines and buffers. Therefore, this paper establishes a PSM to characterize both the static and transient behaviors of automatic and manual machines in the permutation flowshop manufacturing system. Building upon the established PSM, based on Bernoulli’s theory, discrete event model predictive control is proposed in this paper; its aim is to realize real-time optimization of production release plans without interfering with work-in-progress. According to the results of numerical examples, the discrete event model predictive control proposed in this paper is feasible and effective. The model established in this paper provides a theoretical basis for optimizing the effective operation of work-in-progress and replacement process systems. Full article
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25 pages, 1844 KiB  
Article
Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling
Machines 2024, 12(1), 8; https://doi.org/10.3390/machines12010008 - 22 Dec 2023
Cited by 1 | Viewed by 987
Abstract
An extended flexible job scheduling problem is presented with characteristics of technology and path flexibility (dual flexibility), varied transportation time, and an uncertain environment. The scheduling can greatly increase efficiency and security in complex scenarios, e.g., distributed vehicle manufacturing, and multiple aircraft maintenance. [...] Read more.
An extended flexible job scheduling problem is presented with characteristics of technology and path flexibility (dual flexibility), varied transportation time, and an uncertain environment. The scheduling can greatly increase efficiency and security in complex scenarios, e.g., distributed vehicle manufacturing, and multiple aircraft maintenance. However, optimizing the scheduling puts forward higher requirements on accuracy, real time, and generalization, while subject to the curse of dimension and usually incomplete information. Various coupling relations among operations, stations, and resources aggravate the problem. To deal with the above challenges, we propose a multi-agent reinforcement learning algorithm where the scheduling environment is modeled as a decentralized partially observable Markov decision process. Each job is regarded as an agent that decides the next triplet, i.e., operation, station, and employed resource. This paper is novel in addressing the flexible job shop scheduling problem with dual flexibility and varied transportation time in consideration and proposing a double Q-value mixing (DQMIX) optimization algorithm under a multi-agent reinforcement learning framework. The experiments of our case study show that the DQMIX algorithm outperforms existing multi-agent reinforcement learning algorithms in terms of solution accuracy, stability, and generalization. In addition, it achieves better solution quality for larger-scale cases than traditional intelligent optimization algorithms. Full article
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39 pages, 15440 KiB  
Article
Identification of Innovative Opportunities Based on Product Scenario Evolution
Systems 2023, 11(12), 572; https://doi.org/10.3390/systems11120572 - 08 Dec 2023
Viewed by 1058
Abstract
Innovation is a key factor for product development. Identifying innovative opportunities is the first step in innovative product design. Traditional methods of identifying innovative opportunities, such as market surveys and brainstorming, are limited by product users’ and designers’ experiences and lack systematic approaches [...] Read more.
Innovation is a key factor for product development. Identifying innovative opportunities is the first step in innovative product design. Traditional methods of identifying innovative opportunities, such as market surveys and brainstorming, are limited by product users’ and designers’ experiences and lack systematic approaches to generate breakthrough innovations. This paper proposes a method to identify innovative opportunities based on product scenario evolution. The method models a product scenario based on product scenario elements, states, and behaviors. A Type II hierarchical function model is constructed based on the transformation and abstraction hierarchy of the product function model to identify target elements for the scenario evolution. Based on the theory of basic element extension and needs evolution characteristics, the method of extending target scenario elements is proposed. Based on the new scenario element sets and their impact, diffusion, identification, and evaluation methods are proposed for innovation opportunities. Potential opportunities are explored for product innovation from a scenario evolutionary perspective, which updates knowledge and technology reserves and finds new market opportunities for industries. The feasibility and effectiveness of the method are verified using the innovative design of a polyethylene (PE) pipeline hot-melt welding machine. Full article
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19 pages, 7313 KiB  
Article
Implementation of Digital Twin in Actual Production: Intelligent Assembly Paradigm for Large-Scale Industrial Equipment
Machines 2023, 11(11), 1031; https://doi.org/10.3390/machines11111031 - 19 Nov 2023
Cited by 1 | Viewed by 1204
Abstract
The assembly process of large-scale and non-standard industrial equipment poses significant challenges due to its inherent scale-related complexity and proneness to errors, making it difficult to ensure process cost, production cycle, and assembly accuracy. In response to the limitations of traditional ineffective production [...] Read more.
The assembly process of large-scale and non-standard industrial equipment poses significant challenges due to its inherent scale-related complexity and proneness to errors, making it difficult to ensure process cost, production cycle, and assembly accuracy. In response to the limitations of traditional ineffective production models, this paper aims to explore and propose a digital twin (DT)-based technology paradigm for the intelligent assembly of large-scale and non-standard industrial equipment, focusing on both the equipment structure and assembly process levels. The paradigm incorporates key technologies that facilitate the integration of virtual and physical information, including the establishment and updating of DT models for assembly structures using actual data, the assessment of structural assemblability based on DT models, the planning and simulation of assembly processes, and the implementation of virtual commissioning technology tailored to the actual assembly process. The effectiveness of the proposed paradigm is demonstrated through a case study involving the actual assembly of a large-scale aerodynamic experimental equipment. The results confirm its ability to provide valuable technical support for the design, evaluation, and optimization of industrial equipment assembly processes. By leveraging the DT-based methodological system proposed in this paper, significant improvements in the transparency and intelligence of industrial equipment production processes can be achieved. Full article
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18 pages, 987 KiB  
Article
Why Do Older Adults Feel Negatively about Artificial Intelligence Products? An Empirical Study Based on the Perspectives of Mismatches
Systems 2023, 11(11), 551; https://doi.org/10.3390/systems11110551 - 15 Nov 2023
Viewed by 1586
Abstract
Artificial intelligence products (AIPs) for older adults enhance the functions of traditional products and improve the quality of their lives. However, AIPs are not popular among this population, and limited attempts have been made to investigate these users’ negative tendencies regarding AIPs. This [...] Read more.
Artificial intelligence products (AIPs) for older adults enhance the functions of traditional products and improve the quality of their lives. However, AIPs are not popular among this population, and limited attempts have been made to investigate these users’ negative tendencies regarding AIPs. This study explores the causes of avoidance and exit behaviors toward AIPs among older people from both a functional and socio-emotional mismatch perspective. Data were collected from 1102 older AIP users to verify the research model and hypotheses. The results indicate that perceived control and expectation disconfirmation affect the functional mismatch, while public stigma has the greatest impact on the socio-emotional mismatch. Furthermore, the results highlight a mixed influence of the functional and socio-emotional mismatches on negative behaviors. This study explores older people’s negative tendencies toward AIPs, comprehensively considering the functions of AIPs and the socio-emotions they evoke. Thus, it provides new empirical evidence for the systematic relationship between the functional mismatch and the socio-emotional mismatch and fills the research gap on the influence on the subsequent behaviors of older adults. Additionally, this study sheds light on the specific methods of designing, developing, and promoting AIPs. Full article
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16 pages, 9115 KiB  
Article
Overlay Optimization Algorithm for Directed Sensor Networks with Virtual Force and Particle Swarm Optimization Synergy
Electronics 2023, 12(20), 4332; https://doi.org/10.3390/electronics12204332 - 19 Oct 2023
Viewed by 581
Abstract
In this study, a novel algorithm for optimizing the coverage of directed sensor networks is proposed. The deployment of sensor networks is typically random, leading to the potential issues of extensive coverage overlaps and blind areas. To address this challenge and enhance the [...] Read more.
In this study, a novel algorithm for optimizing the coverage of directed sensor networks is proposed. The deployment of sensor networks is typically random, leading to the potential issues of extensive coverage overlaps and blind areas. To address this challenge and enhance the effectiveness of network coverage, a directional sensor network coverage optimization algorithm is developed based on the principles of virtual force and particle swarm optimization. Firstly, the article introduces the concept of a segmented virtual negative centroid model. This model revolutionizes the configuration of the virtual negative centroid, thereby enabling a more efficient adjustment of the gravitational forces exerted by the coverage blind areas on the sensor nodes. Therefore, the influence of these blind areas on the improvement of network coverage is significantly amplified. Secondly, taking into account the characteristics of global optimization and the inherent randomness of particle swarm optimization, the algorithm synergistically combines the principles of virtual force and particle swarm optimization. This integration effectively fine-tunes the sensing direction of the sensor nodes, thereby optimizing their overall performance. The algorithm in this study incorporates an adjusted inertia weight strategy and introduces Gaussian disturbance in the local optimization enhancement phase to prevent local optimization, accelerate particle convergence, and facilitate the sensor network’s attainment of an optimal distribution for coverage optimization. Simulation experiments were conducted to verify the algorithm’s effectiveness. The initial sensor network coverage was 31.04%. After applying the algorithm, the average coverage increased to 80.16%, with a maximum coverage of 84.2%. These results verify the effectiveness of the algorithm. Full article
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16 pages, 6472 KiB  
Article
Intelligent Online Inspection of the Paste Quality of Prebaked Carbon Anodes Using an Anomaly Detection Algorithm
Systems 2023, 11(9), 484; https://doi.org/10.3390/systems11090484 - 21 Sep 2023
Viewed by 1091
Abstract
Prebaked carbon anodes are a critical consumable in the aluminum electrolysis industry. Prebaked carbon anode paste is the intermediate product of the prebaked carbon anode, and its quality significantly impacts the prebaked carbon anode. Therefore, inspecting the quality of the prebaked carbon anode [...] Read more.
Prebaked carbon anodes are a critical consumable in the aluminum electrolysis industry. Prebaked carbon anode paste is the intermediate product of the prebaked carbon anode, and its quality significantly impacts the prebaked carbon anode. Therefore, inspecting the quality of the prebaked carbon anode paste is essential. Currently, the quality inspection of the paste still relies on laboratory analysis or manual experience. A laboratory inspection cannot obtain results in real time, while manual inspection poses potential risks. To address these issues, an online intelligent inspection method for prebaked carbon anode paste based on an anomaly detection algorithm was proposed. Firstly, we acquired the temperature of the paste and the power of the kneading motor. Secondly, we transformed these time-series data into images using the Gramian Angular Field (GAF) technique and joined them to create the paste anomaly detection dataset. Thirdly, we trained a matched anomaly detection model based on the PatchCore algorithm. Finally, we compared two advanced models: HaloAE and TSRD. PatchCore performs best on our dataset with an AUC-ROC score of 0.9943, followed by HaloAE (0.9906) and TSRD (0.9811). Our proposed method enables on-time intelligent inspection of prebaked carbon anode paste quality. This eliminates the need for manual inspection, reduces labor requirements, and ensures worker safety. Full article
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23 pages, 8362 KiB  
Article
Design of Backstepping Sliding Mode Control for a Polishing Robot Pneumatic System Based on the Extended State Observer
Machines 2023, 11(9), 904; https://doi.org/10.3390/machines11090904 - 11 Sep 2023
Viewed by 713
Abstract
Due to advantages such as a high power-to-weight ratio, a simple structure, and low cost, pneumatic systems are widely applied in automation. However, precise position control of pneumatic actuators is challenging because of factors such as friction, compressibility, and external disturbances. This paper [...] Read more.
Due to advantages such as a high power-to-weight ratio, a simple structure, and low cost, pneumatic systems are widely applied in automation. However, precise position control of pneumatic actuators is challenging because of factors such as friction, compressibility, and external disturbances. This paper presents a backstepping sliding mode control (BSMC) strategy based on the extended state observer (ESO) for pneumatic cylinder position tracking. A nonlinear model of the pneumatic system is first established, then system states and disturbances are estimated by an ESO, next the BSMC approach is developed using backstepping method and sliding mode control theory, and the stability of the ESO and controller is analyzed using Lyapunov theory. Finally, simulations and experiments on a pneumatic testbed are performed to compare the effectiveness of the proposed approach with PID control. The results show that the proposed strategy improves tracking accuracy and robustness against disturbances, with a 77.04% reduction in root mean square error (RMSE). This research provides a promising control solution for automated pneumatic polishing robots. Full article
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14 pages, 4573 KiB  
Article
Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII
Machines 2023, 11(9), 882; https://doi.org/10.3390/machines11090882 - 01 Sep 2023
Viewed by 782
Abstract
Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is [...] Read more.
Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications. Full article
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