Along with the number and the functional complexity of machines increase in the intelligent manufacturing system, the probability of faults will increase, which may lead to huge economic losses. ...Traditional passive or regular maintenance methods of solving the faults have the problems of low efficiency and huge resource consumption. Besides, traditional maintenance methods mostly contain single model, so all the prognostics and maintenance tasks of the intelligent manufacturing system can hardly be addressed at the same time. Therefore, this paper proposes a novel predictive maintenance (PDM) method based on the improved deep adversarial learning (LSTM-GAN). The long-short-term memory (LSTM) network can solve the disadvantage of vanishing gradients and the mode collapse from the generative adversarial network (GAN). The method can not only avoid the mode collapse of GAN but also realize the self-detection of abnormal data. Meanwhile, the predictive maintenance model includes two prediction models and a maintenance decision model. The prediction models can predict the state of the machine and the fault of the machine in advance. Then the maintenance decision model will arrange maintenance personnel and offer a plan of maintenance. Finally, a case study about predictive maintenance using LSTM-GAN in the intelligent manufacturing system is presented. The fault prediction accuracy of LTSM-GAN is as high as 99.68%. With the comparison between LSTM-GAN and other traditional methods, LSTM-GAN shows priority both in accuracy and efficiency. Moreover, the proposed PDM can reduce maintenance costs and downtime so that the life of machines in the intelligent manufacturing system will extend.
The flexibility claimed by the next generation production systems induces a deep modification of the behaviour and the core itself of the control systems. Over-connectivity and data management ...abilities targeted by Industry 4.0 paradigm enable the emergence of more flexible and reactive control systems, based on the cooperation of autonomous and connected entities in the decision-making process. From most relevant articles extracted from existing literature, a list of 10 key enablers for Industry 4.0 is first presented. During the last 20 years, the holonic paradigm has become a major paradigm of Intelligent Manufacturing Systems. After the presentation of the holonic paradigm and holon properties, this article highlights how historical and current holonic control architectures can partly fulfil Industry 4.0 key enablers. The remaining unfulfilled key enablers are then the subject of an extensive discussion on the remaining research perspectives on holonic architectures needed to achieve a complete support of Industry 4.0.
The multi-agent control strategy has been previously shown to improve the flexibility of complex, dynamic manufacturing systems. One key component of this strategy is the product agent. The product ...agent is responsible for autonomously guiding a physical part in the manufacturing system based on its production goals. Though the product agent has been described in previous works, a fully developed software architecture for the product agent that uses a model-based optimization approach has not been proposed. In this work, a product agent architecture with the capabilities to explore the local environment, plan and schedule events based on its knowledge, and request desired actions from the resources in the system is presented and tested.
In the context of Industry 4.0, the manufacturing sector is moving from automation towards intelligence. The application of new generation information and communication technologies (ICTs) improves ...the interconnection and transparency of intelligent manufacturing (IM) systems, which will change how information interacts and work is done, thus changing how work should be managed. These changes require the following characteristics for IM production and operations management (POM): integration, flexibility and networking, autonomous and collaborative decision-making, learning-based operations management, self-optimisation and adaptability, and proactive decision-making. This paper presents the state of the art, current challenges, and future directions of IM-related POM research from the perspectives of these characteristics through a systematic literature review. Descriptive and thematic analyses of 208 research articles published between 2005 and 2020 are provided. The review and discussions focus on five research themes, i.e. value creation mechanisms, resource configuration and capacity planning, production planning, scheduling, and logistics.
This volume of Journal of Physics: Conference Series presents the proceedings of the 1st International Conference on Mechanics, Electronics, Automation and Automatic Control (MEAAC2023). The ...conference was held in Wuhan, China during May 13-15, welcoming around 50 attendants physical and online from home and abroad. MEAAC2023 organized discussions on hot topics including intelligent manufacturing, robot system control, and smart cities and focused on the challenges during research as well as application. The conference consisted of one physical conference in the morning and one virtual conference in the afternoon, showcasing various sessions such as keynote speeches, oral reports, poster presentations, Q&A, etc. We had the utmost pleasure of having with us experts and scholars from around the globe sharing their latest findings and insights. The MEAAC conference is conceived in the belief of promoting academic exchange within and across disciplines, addressing theoretical and practical challenges and advancing current understanding and application, during the process of which amity is spread, connections established and future collaborations enabled. The MEAAC organizing committee extend their sincerest gratitude to all who have supported the conference in their ways, to the authors who have chosen this platform to publish their works and communicate with peers, to the participants who took an interest and attended the conference in person and/or online, to the chairs and committee members who have been indispensable in lending their professional expertise and judgment, to the keynote speakers who generously shared their vision and passion, and to the reviewers who held up the faith of being a scholar and contributed their experience and honest opinions. It has been a pleasure and honor working alongside them, and we look forward to future cooperation with them at future MEAAC conferences to come. List of Conference Chair, Conference Co-chair, TPC Chair, Technical Program Committee are available in this Pdf.
Materials and technologies are significant elements for all kinds of high-tech industries that pave the road for advancements in the manufacturing area. With the rapid development of computer ...technology, communications technology, and network technology, the traditional manufacturing process has evolved to intelligent manufacturing that is more technologically flexible and efficient. The Fifth International Conference on Materials Science and Manufacturing Technology 2023 (ICMSMT 2023) was a premier interdisciplinary platform for researchers, academicians, and practitioners worldwide to present recent developments in materials sciences and manufacturing technology. The fifth edition of the ICMSMT conference series (ICMSMT 2023) has been organized with partnered by Akshaya College of Engineering and Technology, Coimbatore, and Diligentec Solutions, Coimbatore, India. The conference received submissions from 15 countries, and the editorial board of ICMSMT has accepted the papers after the stringent screening and review process. ICMSMT is a scientific event and aimed to be conducted in the second week of April every year. ICMSMT 2023 was organized in an hybrid model with both online and physical participations. Each presenter was given ten minutes for the presentation and three minutes for queries and discussion. The online meetings have been conducted using the Google Meet platform. Dr. Ramya Muthusamy Editor / Chair – TPC ICMSMT 2023 List of Committees are available in this pdf.
Achieving the goal of sustainable development of the fruit and vegetables (F&Veg) value chain is heavily dependent on processing at both the global and local levels. The future contribution of F&Veg ...to human health is widely recognized, but the scientific needs that underpin their production, processing and distribution still need elucidation.
A comprehensive exploration of the challenges, future trends and solutions for F&Veg post-harvest and processing to counter F&Veg losses and waste, and to promote F&Veg consumption and sustainable development. These encompass many transformative aspects, often facilitated by integration of numerous tools such as human-machine collaboration or intelligent manufacturing. Different scales need to be addressed, such as i) processing operations themselves, with small-scale local innovative processing, design of alternatives to animal products and precision processing, ii) relations with the consumer with traceability systems, personalization, and food sharing, and iii) insertion in the larger scale of bioeconomy.
In the future, the cohesion between processing type, products and consumers should need to be further strengthened to ensure that it meets the more recent demands of the consumer and citizen, such as environmentally friendly and personalized, while the more classical quality traits such as (low) cost, convenience, and taste are preserved and the prerequisites of safety and nutrition are not compromised. This demands a high level of innovation for the entire processing in a short term and it will mean a new balance in F&Veg value chains. The future tasks involve interdisciplinary and cross-border collaboration, and the F&Veg production and processing needs are global, but their application will require different approaches in different regions.
•The COVID-19 pandemic highlights the importance of local F&Veg processing solutions.•The work of F&Veg processing requires breaking down barriers across disciplines.•The beyond processing trend may be to integrate the human mind, i.e., human-machine collaboration.•There is no one-size-fits-all approach of the trade-offs between crisis and opportunity.•New perspectives for the future work on F&Veg processing are suggested.
As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a ...pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert' judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.
Deep neural networks have been widely studied in the field of mechanical fault diagnosis with the rapidity of intelligent manufacturing and industrial big data, however, attractive performance gains ...usually come from a premise that source training data and target test data have the same distribution. Unfortunately, this assumption is generally untenable in practice due to changeable working conditions and complex industrial environment. To address this issue, a double-level adversarial domain adaptation network (DL-ADAN) is presented for cross-domain fault diagnosis, which is able to bridge the divergences between the source and target domains. Specifically, the proposed diagnostic framework is composed of a feature extractor based on deep convolutional network, a domain discriminator and two label classifiers, which conducts two minimax adversarial games. In the first adversarial stream, the feature extractor and domain discriminator game with each other to achieve domain-level alignment from a global perspective. On the other line, the extractor and two classifiers are against each other to conduct class-level alignment, in which Wasserstein discrepancy is used to detect outlier target samples. As a result, the extractor can learn transferable discriminative features for accurate fault diagnosis. Extensive diagnostic experiments are constructed for performance analysis and several state of the art diagnostic methods are selected for comparative study. The comprehensive results demonstrate the effectiveness and superiority of the proposed method.
•A double-level adversarial domain adaptation network is proposed to bridge the domain distribution differences for intelligent fault diagnosis.•Domain-level and class-level alignments are jointly conducted by two minimax games.•Wasserstein metric is adopted to construct a reliable discrepancy measure in class-level alignment.•Extensive experiments on two mechanical equipment are constructed to verify the efficacy and superiority of the proposed method.
Smart factory in Industry 4.0 Shi, Zhan; Xie, Yongping; Xue, Wei ...
Systems research and behavioral science,
July/August 2020, Letnik:
37, Številka:
4
Journal Article
Recenzirano
The transformation from traditional manufacturing to intelligent manufacturing intrigues the profound and lasting effect on the future manufacturing worldwide. Industry 4.0 was proposed for advancing ...manufacturing to realize short product life cycles and extreme mass customization in a cost‐efficient way. As the heart of Industry 4.0, smart factory integrates physical technologies and cyber technologies and makes the involved technologies more complex and precise in order to improve performance, quality, controllability, management, and transparency of manufacturing processes. So far, leading manufacturers have begun the journey toward implementing smart factory. However, most firms still lack insight into the challenges and resources for implementing smart factory. As such, this paper identifies the requirements and key challenges, investigates available new technologies, reviews existing studies that have been done for smart factory, and further provides guidance for manufacturers to implementing smart factory in the context of Industry 4.0.
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