Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400
Journal of Manufacturing and Materials Processing jmmp	3.3	5.1	2017	14.7 Days	CHF 1800
Machines machines	2.1	3.0	2013	15.6 Days	CHF 2400
Robotics robotics	2.9	6.7	2012	17.7 Days	CHF 1800
Systems systems	2.3	2.8	2013	17.3 Days	CHF 2400

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Published Papers (14 papers)

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16 pages, 6738 KiB  

Open AccessArticle

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

by Xuejian Zhang, Xiaobing Hu, Hang Li, Zheyuan Zhang, Haijun Chen and Hong Sun

Machines 2024, 12(5), 307; https://doi.org/10.3390/machines12050307 - 1 May 2024

Viewed by 1397

Abstract

Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector’s technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis [...] Read more.

Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector’s technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis and prediction methods. The endeavor to accurately analyze welding deformation in practical applications is compounded by the interplay of numerous variables, a pronounced coupling effect among these factors, and a reliance on expert intuition. Thus, effective deformation control in automated laser welding operations necessitates the gathering of pre-test laser welding data to develop a predictive approach that accurately reflects real-world conditions and is characterized by improved reliability and stability. To address the technological evolution in automated laser welding, a predictive model based on neural network technology is proposed to map the intricate relationship between process variables and the resulting deformation. At the heart of this approach is the formulation of a predictive model utilizing a back-propagation neural network (BP), with an emphasis on four essential welding parameters: speed, peak power, duty cycle, and defocusing amount. The model’s predictive accuracy is then honed through the application of the whale optimization algorithm (WOA) and the differential evolutionary (DE) algorithm. Finally, extensive testing in an automated laser welding experimental setup is conducted to validate the accuracy and reliability of the proposed prediction model. It is demonstrated through these experiments that the deformation prediction model, enhanced by the DEWOA-BP neural network, accurately forecasts the relationship between laser welding parameters and the induced deformation, maintaining a prediction error margin of ±0.1mm. The model is employed to fulfill the requirements for a pre-welding quality evaluation, thereby facilitating a more calculated and informed approach to welding operations. This method of intelligent prediction is not only crucial for the intelligent transformation of laser welding but also holds significant implications for traditional machining technologies such as milling, grinding, and spraying. It offers innovative ideas and methods that are pivotal for the industrial revolution and technological advancement of the traditional machining industry. Full article

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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16 pages, 6111 KiB  

Open AccessArticle

A PTM-Based Framework for Enhanced User Requirement Classification in Product Design

by Zhiwei Zhang, Yajie Dou, Xiangqian Xu and Yuejin Tan

Electronics 2024, 13(8), 1458; https://doi.org/10.3390/electronics13081458 - 12 Apr 2024

Viewed by 926

Abstract

Accurately identifying and classifying customer requirements is crucial for successful product design. However, traditional methods for requirement classification, such as Kano models based on questionnaires, can be time-consuming and may not capture all requirements accurately. Analyzing large volumes of user reviews using simple [...] Read more.

Accurately identifying and classifying customer requirements is crucial for successful product design. However, traditional methods for requirement classification, such as Kano models based on questionnaires, can be time-consuming and may not capture all requirements accurately. Analyzing large volumes of user reviews using simple natural language processing techniques can also result in accuracy issues. To address these challenges, we propose a framework that combines pre-trained models (PTMs), Kano models, and the sentiment analysis technique. Our approach integrates an LDA-K-Means model enhanced by PTM ERNIE for pinpointing product feature topics within user reviews. Then, a sentiment analysis is performed using the fine-tuned PTM SKEP to assess user satisfaction with features. Finally, the Kano model is applied to perform requirement classification. We evaluate our framework quantitatively, demonstrating its superior performance compared to the baseline models. Our sentiment analysis model also outperforms the other baseline models. Moreover, a case study on smartphones illustrates the effectiveness of our framework. This research results suggest that leveraging a suitable PTM can better address the problem of requirement classification in user review analyses, leading to improved product design. Full article

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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22 pages, 10286 KiB  

Open AccessArticle

A Method for Inspiring Radical Innovative Design Based on Cross-Domain Knowledge Mining

by Fei Yu, Xiuchuan Jia, Xiaowei Zhao and Jing Li

Systems 2024, 12(3), 102; https://doi.org/10.3390/systems12030102 - 17 Mar 2024

Viewed by 1531

Abstract

The reasonable application of cross-domain knowledge tends to promote the generation of radical innovation. However, it is difficult to accurately capture the cross-domain knowledge needed for radical innovation. To solve this problem, this paper proposes a method for inspiring radical innovative design based [...] Read more.

The reasonable application of cross-domain knowledge tends to promote the generation of radical innovation. However, it is difficult to accurately capture the cross-domain knowledge needed for radical innovation. To solve this problem, this paper proposes a method for inspiring radical innovative design based on FOS and technological distance measurement. First, the functional analysis of the problem product is carried out to determine the target function. Second, the patent sets of problem domain and target domains are constructed based on FOS. Then, this study optimizes the method of technological distance measurement and uses it to determine the optimal target domain. After further categorizing and screening the patents contained in the optimal target domain, specific cross-domain knowledge is pushed to designers. This method can help firms select the most appropriate cross-domain knowledge to design solutions for different problems, thus increasing the possibility of generating radical innovation. In the end, the method is validated in the design of a stovetop cleaning device. Full article

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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17 pages, 854 KiB  

Open AccessReview

Surveying Quality Management Methodologies in Wooden Furniture Production

by Ewa Skorupińska, Miloš Hitka and Maciej Sydor

Systems 2024, 12(2), 51; https://doi.org/10.3390/systems12020051 - 3 Feb 2024

Cited by 6 | Viewed by 4763

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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26 pages, 675 KiB  

Open AccessArticle

A Study of the Impact of Executive Corruption on Corporate Innovation

by Ming Bai, Yanru Chen, Ye Hong and Zhongqi Yang

Systems 2024, 12(1), 25; https://doi.org/10.3390/systems12010025 - 11 Jan 2024

Viewed by 2411

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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23 pages, 4157 KiB  

Open AccessArticle

A Predictive Control Model of Bernoulli Production Line with Rework Loop for Real-Time WIP Optimization in Permutation Flowshop

by Wenbin Gu, Zhenyang Guo, Xianliang Wang, Yiran Yang and Minghai Yuan

Machines 2024, 12(1), 20; https://doi.org/10.3390/machines12010020 - 29 Dec 2023

Viewed by 1373

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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25 pages, 1844 KiB  

Open AccessArticle

Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling

by Shaoming Peng, Gang Xiong, Jing Yang, Zhen Shen, Tariku Sinshaw Tamir, Zhikun Tao, Yunjun Han and Fei-Yue Wang

Machines 2024, 12(1), 8; https://doi.org/10.3390/machines12010008 - 22 Dec 2023

Cited by 5 | Viewed by 2634

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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39 pages, 15440 KiB  

Open AccessArticle

Identification of Innovative Opportunities Based on Product Scenario Evolution

by Fanfan Wang, Runhua Tan, Qingjin Peng, Kang Wang and Yafan Dong

Systems 2023, 11(12), 572; https://doi.org/10.3390/systems11120572 - 8 Dec 2023

Viewed by 2283

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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19 pages, 7313 KiB  

Open AccessArticle

Implementation of Digital Twin in Actual Production: Intelligent Assembly Paradigm for Large-Scale Industrial Equipment

by Huaqiu Ding, Lizhong Zhao, Jihong Yan and Hsi-Yung Feng

Machines 2023, 11(11), 1031; https://doi.org/10.3390/machines11111031 - 19 Nov 2023

Cited by 4 | Viewed by 2696

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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18 pages, 987 KiB  

Open AccessArticle

Why Do Older Adults Feel Negatively about Artificial Intelligence Products? An Empirical Study Based on the Perspectives of Mismatches

by Wenjia Hong, Changyong Liang, Yiming Ma and Junhong Zhu

Systems 2023, 11(11), 551; https://doi.org/10.3390/systems11110551 - 15 Nov 2023

Viewed by 4319

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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16 pages, 9115 KiB  

Open AccessArticle

Overlay Optimization Algorithm for Directed Sensor Networks with Virtual Force and Particle Swarm Optimization Synergy

by Lingjian Zhu, Li Lin, Qi Liang, Yaling Lu, Haonan Tan, Xuan Ma and Dongya Zhang

Electronics 2023, 12(20), 4332; https://doi.org/10.3390/electronics12204332 - 19 Oct 2023

Cited by 1 | Viewed by 1048

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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16 pages, 6472 KiB  

Open AccessArticle

Intelligent Online Inspection of the Paste Quality of Prebaked Carbon Anodes Using an Anomaly Detection Algorithm

by Laiyi Li, Qingzong Li, Wentao Yong, Shuwei Zhang, Maolin Yang and Pingyu Jiang

Systems 2023, 11(9), 484; https://doi.org/10.3390/systems11090484 - 21 Sep 2023

Viewed by 1761

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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23 pages, 8362 KiB  

Open AccessArticle

Design of Backstepping Sliding Mode Control for a Polishing Robot Pneumatic System Based on the Extended State Observer

by Qinsheng Li and Birong Ding

Machines 2023, 11(9), 904; https://doi.org/10.3390/machines11090904 - 11 Sep 2023

Cited by 3 | Viewed by 1297

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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14 pages, 4573 KiB  

Open AccessArticle

Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII

by Ruizhi Li, Zipeng Wang and Jihong Yan

Machines 2023, 11(9), 882; https://doi.org/10.3390/machines11090882 - 1 Sep 2023

Cited by 2 | Viewed by 1601

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

(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)

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