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Thermal error modeling (TEM) stands as a pivotal factor in maintaining the machining accuracy of electric spindles. Deep learning (DL) techniques have shown promising potential in this area, however, they face formidable challenges including domain shift issue between training and testing datasets, as well as scarce samples due to the dynamic nature of variable working conditions. Deep transfer learning (DTL) emerges as an encouraging tool, leveraging knowledge obtained from the source domain to elevate learning outcomes within the target domain, notably addressing complicated cross-domain modeling predicaments. Nonetheless, prevailing research largely focuses on enhancing the prediction accuracy within the specific working condition, inadvertently sidestepping the problems of domain shift and scarce sample modeling. To address these issues, a novel weakly supervised adversarial network (WSAN) is proposed for cross-domain TEM with scarce samples. Firstly, the multi-scale convolutional neural network (MSCNN) is constructed to adeptly extract predictive information from multi-sensor data, effectively capturing the time-series patterns embedded within. Furthermore, the adversarial training technique is adopted to address distribution discrepancies that often manifest between distinct domains. To overcome the challenge of scarce labeled samples in the target domain, a weakly supervised learning strategy is employed to deftly control the gradient within the target domain, significantly enhancing the efficacy of positive transfer. The effectiveness and superiority of the proposed method are assessed through extensive experiments using datasets from variable working conditions. The experimental results demonstrate that the proposed method achieves satisfactory performance and outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Coordinating order acceptance decisions with production scheduling to maximize revenue is challenging for Mass Individualized Prototyping (MIP) service providers. This paper presents a dual deep reinforcement learning agents-based (DDRLA) integrated order acceptance and scheduling (IOAS) for improving revenue. Firstly, a deep reinforcement learning-based virtual production scheduling (VPS) agent together with 8 state features and 11 action rules is designed. The VPS agent quickly and virtually reschedules a dynamically-arriving accepted order to evaluate the overall impact of accepting this order, including consumed capacity and increased revenue. Then, a deep reinforcement learning-based order acceptance decision (OAD) agent is designed. Based on the information guidance resulting from an interaction with the VPS agent, the OAD agent selectively accepts orders to maximize long-term gains, as well as to improve system resilience in the presence of a high ratio of urgent orders. The experiment results show that the proposed DDRLA method has better performance, compared with other IOAS approaches. [ABSTRACT FROM AUTHOR]

The growth of personalized demands requires socialized resources to timely self-organize themselves with crowd intelligence for co-creating open architecture products. This social manufacturing paradigm drives an increased demand for makers to track the authenticity and quality of products. A new decentralized blockchain-driven model, named Makerchain, is presented to handle the cyber-credit of social manufacturing among various makers. An anti-counterfeiting method composed of chemical signature is proposed to represent unique features of personalized products. Twinning unique signature data to blockchain and other functional databases is realized and anticipated to make manufacturing service transactions among makers more trustworthy. Based on an automated execution mechanism of smart contracts among makers, a decentralized manufacturing network can be enabled for automating transactions among makers, as well as third-party verification of product lifecycle through a trail of historic events. A Makerchain Decentralized Application (DApp) is presented to demonstrate the proposed approach through which clustered makers can self-organizing themselves around personalized demands. [ABSTRACT FROM AUTHOR]

As an essential element in the overall designing of a service system, service planning plays an important role in improving final service quality and user experience. Service planning is a customer-oriented approach that facilitates the translation of customer requirements into activity process features or engineering characteristics. The trend is towards learning decision-support knowledge from massive service case data to facilitate service planning. However, when learning from imbalanced data, existing methods have poor predictive ability to identify minority patterns. To improve the quality and efficiency of the service planning, an innovative approach based on the combination of granular computing and deep learning are presented. It employs an inductive paradigm, clustering examples in a best granularity, sampling and refining into a more balanced example set, and feeding them into a deep learning model. The proposed approach can mine the planning patterns between the customer requirements and service process features, thereby facilitating knowledge transferring in service planning under the social manufacturing context. [ABSTRACT FROM AUTHOR]

• Digital twins-based flexible operating of open architecture production line. • The reconfigurability based on open architectural architecting of cyber system and physical system. • Integration of online system-level logistics simulation and equipment-level multi-physical field simulation. Individualized manufacturing implies high flexibility of both the hardware and software of the production lines based on a fast physical and logical system (de)commissioning. This paper proposes an open architecture production line (OAPL) design together with a digital twins-based flexible operating approach for individualized manufacturing. Firstly, an OAPL is designed and implemented with physical reconfigurability by orchestrating different open architectural platforms together with open architecture machine tools (OAMTs). Secondly, an open architectural style modeling and configuration method is presented to enable the software reconfigurability of the controls of the OAPL. Thirdly, a digital twin-based online process emulating and multi-physics simulation is integrated to aid the comprehensive characterizing of the operation status of the OAPL. Based on the system reconfigurability and digital twins system, a triple-layer Learning-Optimization-Reacting approach together with an ensemble algorithm for flexible operating of the OAPL is proposed. The digital twins are formed with the ability to flexibly operate the OAPL for catering to different individualized requirements. A demonstrative implementation of a stepping-motor assembly OAPL is presented finally. [ABSTRACT FROM AUTHOR]

There is an increasing unstructured text data produced in cross-enterprise social interaction media, forming a social interaction context that contains massive manufacturing relationships , which can be potentially used as decision support information for cross-enterprise manufacturing demand-capability matchmaking. How to enable decision-makers to capture these relationships remains a challenge. The text-based context contains high levels of noise and irrelevant information, causing both highly complexity and sparsity. Under this circumstance, instead of exploiting man-made features carefully optimized for the relationship extraction task, a deep learning model based on an improved stacked denoising auto-encoder on sentence-level features is proposed to extract manufacturing relationships among various named entities ( e.g. , enterprises, products, demands, and capabilities) underlying the text-based context. Experiment results show that the proposed approach can achieve a comparable performance with the state-of-the-art learning models, as well as a good practicality of its’ web-based implementation in social manufacturing interaction context. The ultimate goal of this study is to facilitate knowledge transferring and sharing in the context of enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprises. [ABSTRACT FROM AUTHOR]

The COVID-19 has become a global pandemic that dramatically impacted human lives and economic activities. Due to the high risk of getting affected in high-density population areas and the implementation of national emergency measures under the COVID-19 pandemic, both travel and transportation among cities become difficult for engineers and equipment. Consequently, the costly physical commissioning of a new manufacturing system is greatly hindered. As an emerging technology, digital twins can achieve semi-physical simulation to avoid the vast cost of physical commissioning of the manufacturing system. Therefore, this paper proposes a digital twins-based remote semi-physical commissioning (DT-RSPC) approach for open architecture flow-type smart manufacturing systems. A digital twin system is developed to enable the remote semi-physical commissioning. The proposed approach is validated through a case study of digital twins-based remote semi-physical commissioning of a smartphone assembly line. The results showed that combining the open architecture design paradigm with the proposed digital twins-based approach makes the commissioning of a new flow-type smart manufacturing system more sustainable. [ABSTRACT FROM AUTHOR]

Printed Circuit Board (PCB) manufacturing is a kind of energy-intensive and pollution-intensive industries. With the increment of individualized requirements, PCB manufacturers face massive customized orders with a variety of specifications. The individualized customization on orders results in large differentials of the profit, energy consumption, and environmental pollution. Making energy-efficient order acceptance decisions can reduce carbon consumption and improve material utilization during the whole manufacturing process. An order acceptance decision model is established based on a loosely-coupled integration of deep learning and reinforcement learning techniques. Firstly, different from a simple assumption of the linear cost function in a small-scale manufacturing system, the deep learning algorithm is presented for accurately predicting the production cost, makespan, and carbon consumption of incoming PCB orders in the large-scale manufacturing system. Secondly, these predicted cleaner production indicators are combined with original order features to perform a reinforcement learning-based order acceptance decision. The proposed loosely-coupled deep reinforcement learning approach is verified with a dataset built based on data collected from a PCB manufacturer in China. This research is expected to provide an environment-friendly order acceptance decision-making approach for sustainable manufacturing in the Industry 4.0 context. [ABSTRACT FROM AUTHOR]

Sustainability is a pressing need, as well as an engineering challenge, in the modern world. Developing smart technologies is a critical way to ensure that future manufacturing systems are sustainable. Blockchain is a next-generation development of information technology for realizing sustainability in businesses and industries. Much research on blockchain-empowered sustainable manufacturing in Industry 4.0 has been conducted from technical, commercial, organizational, and operational perspectives. This paper surveys how blockchain can overcome potential barriers to achieving sustainability from two perspectives, namely, the manufacturing system perspective and the product lifecycle management perspective. The survey first examines literature on these two perspectives, following which the state of research in blockchain-empowered sustainable manufacturing is presented, which sheds new light on urgent issues as part of the UN's Sustainable Development Goals. We found that blockchain-empowered transformation of a sustainable manufacturing paradigm is still in an early stage of the hype phase, proceeding toward full adoption. The survey ends with a discussion of challenges regarding techniques, social barriers, standards, and regulations with respect to blockchain-empowered manufacturing applications. The paper concludes with a discussion of challenges and social barriers that blockchain technology must overcome to demonstrate its sustainability in industrial and business spheres. • The transparency characteristics enabled by blockchain shows promising for enhancing the sustainability of manufacturing networks. • Twelve metrics of adopting blockchain in the manufacturing sector have been concluded corresponding to nine blocks of the Business Model Canvas. • An overview of social barriers and the challenges of achieving sustainability goal in blockchain-empowered manufacturing applications have been presented. [ABSTRACT FROM AUTHOR]

Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. With large-scale sensor systems in modern factories, much data will be captured during monitoring and maintenance of complex industrial equipment. Accumulated data facilitates maintenance planning becomes more thorough and timely. Recently, a knowledge graph (KG) was offered to handle large-scale, unorganized maintenance data semantically, resulting in better data usage. Some prior studies have utilized KG for maintenance planning with semantic searching or graph structure-based algorithms, nevertheless neglecting the prediction of potential linkage. To fill this gap, a maintenance-oriented KG is established firstly based on a well-defined domain-specific ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to predict potential solutions and explain fault in maintenance tasks. Lastly, a maintenance case of oil drilling equipment is carried out, where the proposed model is compared with other cutting-edge models to demonstrate its effectiveness in link prediction. This research is anticipated to shed light on future adoption of KG in maintenance planning recommendations. • A Manufacturing Knowledge Graph's schema is proposed in light of the low efficiency of knowledge use and the absence of uniform standards in maintenance scenarios. • A link prediction model, ACRGCN, is proposed, which is tailored for the sparsity and imbalance node types characteristics of Manufacturing Knowledge Graph. • A question-and-answer system is provided to reduce the operational difficulty of knowledge graph search reasoning, satisfying convenient and user-friendly purposes. [ABSTRACT FROM AUTHOR]

With the increasing demand for automotive glass, improving the efficiency of automotive glass manufacturing systems can make full use of production resources and reduce the waste of natural and social resources. Therefore, this work aims to provide an efficient method for a real-world two-stage hybrid flow shop scheduling problem with small batches in an automotive glass manufacturing system. For the investigated problem, there is a significant setup time at the first stage, the second stage is served by machines that should not be interrupted, and each batch has a due date. Such constraints make this scheduling problem challenging. To solve this problem, a mixed integer linear programming model is established. Also, two properties and three theorems are given to enhance the problem-solving process. Subsequently, an efficient genetic algorithm is carefully designed to solve large-scale problems by considering the properties of the system. Meanwhile, an improvement scheme is proposed to decrease the running time of the algorithm, and experimental results show that this scheme can reduce the running time by 280 s at most from the average results of different scale problems. Finally, extensive experiments are carried out and a real-world case is solved to demonstrate the efficiency and effectiveness of the designed genetic algorithm. Also, the Taguchi method is adopted to tune the parameters for the designed algorithm. [ABSTRACT FROM AUTHOR]

Under the promotion of sustainable development goals, cleaner production (CP) has become an important strategy for energy-intensive manufacturing industries to maintain their competitiveness. Studies have shown that the implementation of Industry 4.0 (I4.0) can effectively promote the CP process in manufacturing. However, existing studies often focus on specific scenarios, limiting a comprehensive assessment of the industry's overall status. This paper aims to provide a comprehensive overview of the main research areas of I4.0 and its key impacts on CP by employing systematic mapping studies. By reviewing 121 studies retrieved from the Web of Science, a hierarchical analysis framework centered on the product life cycle (PLC) has been introduced. This framework provides a holistic analysis of sustainable intelligent manufacturing, summarizing the application of I4.0 and its impact on CP across the PLC stages. The main findings reveal a growing focus on I4.0 and CP, with the manufacturing and maintenance stages of the PLC being primary research scenarios. Moreover, attention should be directed towards integrating clean technologies, bolstering industrial data security, fostering circular economy practices, and exploring emerging fields like Industry 5.0. Moreover, to better explain the impact of this study, management implications are also provided from the three dimensions of theory, practice, and policy. These viewpoints and conclusions are of great significance for guiding future research and practical applications. • A comprehensive review of Industry 4.0 and cleaner production is conducted. • Systematic mapping and bibliometric methods are employed. • A hierarchical framework based on product life cycle is proposed. • The current challenges and future prospects are discussed. • Management implications from theory, practice, and policy are provided. [ABSTRACT FROM AUTHOR]

This paper addresses the two-dimensional strip-packing (2DSP) problem of placing a set of rectangular pieces onto a fixed-width rectangular sheet to minimize the total length used. We propose a Block-Based Heuristic Search Algorithm (BBHSA) to solve 2DSP problems with guillotine cut constraints. Initially, it converts the 2DSP problem into a series of 2D rectangular packing problems (2DRP), where the size of the sheet is fixed. In the BBHSA, rectangular pieces are aggregated into blocks, which are partial solutions without residual space. These blocks provide the ingredients for a good layout and are basic components in a tree-based constructive search process. Two basic operations, called placing & splitting and approximate binary search are used in the search process. Furthermore, several block-based placement rules are explored to speed up the search process and improve solution quality. To verify the performance of our proposed algorithm, we conducted extensive experiments using the zero-waste benchmark and non-zero-waste benchmark instances. The results show that BBHSA demonstrates computational effectiveness, particularly in zero-waste cases, achieving optimal solutions for almost all zero-waste benchmark instances reported in the existing literature. [ABSTRACT FROM AUTHOR]

The energy-saving potential of original equipment manufacturers (OEMs) for electronic products is substantial owing to the huge market size of assembling industry in China. A semi-automatic electronic assembly line (SAEAL) for smart-phones is introduced, and its digital twin (DT) enabled energy-saving platform is developed as the hardware foundation. We propose a stochastic dynamics model via max-plus algebra to characterize the spatio-temporal nature of state transition, which provides dynamical opportunity windows for further energy-saving control strategy. Accordingly, model linearization is conducted to obtain linear state and output equations. Then, a model predictive control (MPC) approach for energy saving optimization is provided to determine the optimal start-stop control signal. The dynamics modeling and MPC approach have been applied and verified in the case of assembly line. The results show that the optimization approach could reach a proportion of 49% for the equipment sleep time of total running time and save a considerable amount of energy for normal production of OEMs. [Display omitted] • A digital twin prototype system of smart-phone assembly line is developed as the testing platform for energy-saving control. • The assembling behaviors of assembly line are depicted by linear state equations via max-plus semiring algebra. • A spatio-temporal dynamics model is provided to evaluate the system performance based on max-plus semiring algebra. • An energy-saving MPC approach is proposed to determine the start-stop control sequences of automatic machines. • The equipment's sleeping time can reach a proportion of 49% of total running time without production efficiency loss. [ABSTRACT FROM AUTHOR]

Reducing the number of lower-level function evaluations (LFEs) is crucial to designing a bilevel evolutionary algorithm (BLEA) for bilevel optimization. Direct neighbor solution transfer (DST) is regularly used in BLEA because it can improve the lower-level search efficiency and reduce the number of LFEs. But this method still faces the challenge of misleading searches to make the algorithm converge to the wrong optimum. This paper proposes a bilevel improved multi-operator differential evolution algorithm (BL-IMODE) for bilevel optimization, which uses the knowledge transfer and adaptive coordinate systems approach. Specifically, this paper proposes a lower-level search knowledge transfer (LLS-KT) and a lower-level (LL) optimal solution distribution information transfer (LLO-DIT) mechanism to implement knowledge transfer. In LLS-KT, transferring LL solutions to the LL optimizer is similar to DST, but the LLS-KT transfers multiple solutions and thus has less possibility of misleading searches than DST. Moreover, the LLO-DIT transfers the LL optimal solution distribution information to the LL optimizer for generating promising offspring solutions. Since this method does not directly transfer LL solutions, it avoids misleading searches. Additionally, the adaptive coordinate systems (ACS) approach is used in two-level optimizers to suit different fitness landscapes and further reduce the number of LFEs. In ACS, three differential evolution mutation operators are produced in two coordinate systems. An adaptive strategy is used to select the suitable differential evolution mutation operator to generate promising offspring solutions in the Eigen coordinate or original coordinate system. Experimental studies on three test suites benchmark problems for comparison with seven state-of-the-art algorithms are conducted. The results show BL-IMODE has a significant advantage over the comparative algorithms. Finally, BL-IMODE is applied to tackle the inverse optimal control problem. • We propose a bilevel improved multi-operator differential evolution algorithm. • We propose a knowledge transfer approach to avoid misleading search. • We propose an adaptive coordinate systems approach to improve search efficiency. • We outperform all existing approaches in most test instances. [ABSTRACT FROM AUTHOR]

First-article inspection of flexographic printing labels (FPLs) is significant before the mass production of FPLs, which is manually implemented by quality check (QC) workers in real industries. In this paper, an adaptive coarse-to-fine framework is proposed for automatic first-article inspection of FPLs based on design drawings, consisting of coarse matching, fine matching, and defect detection. Specifically, a deep learning method integrating SuperPoint and SuperGlue is used for coarse matching between the design drawings and the corresponding FPL image to reveal their inherent characteristics. To deal with local deviations of the characters in the FPL image, a character-wise normalized cross-correlation (NCC) method is proposed to finely align each character in the design drawings with that in the FPL image, which utilizes different searching windows for different characters. Next, a constrained clustering method is proposed to accurately extract the contents of interest (COIs) in the FPL image. Finally, an edge distance measure with an adaptive threshold scheme is proposed to evaluate the COIs for inspection. Comparison experiments show that the proposed framework achieves better inspection performance for first-article inspection of FPLs than other fabric inspection methods. Specifically, it achieves the inspection performance of 1.3% missing rate, 2.0% false rate, and 0.951 Dice at an acceptable inspection speed of 2.735 s/pcs, which demonstrates that it is applicable to facilitate the QC task in real industries. [ABSTRACT FROM AUTHOR]

Shared manufacturing (SM) is an advanced manufacturing mode to solve the problems of information asymmetry and resource imbalance in the complex supply and demand relationship under the background of mass personalization. Blockchain can provide technical solutions for trust issues between manufacturing service providers and demanders, but less research has focused on the stable maintenance of a blockchained shared manufacturing network (BSMN). Industrial knowledge graph and cognitive intelligence play an important role in designing incentive mechanisms for BSMN. Based on the evolutionary game theory, this paper designs various incentive models and develops the corresponding smart contracts to encourage different enterprises to participate in the "accounting" operations, to enhance the trust and maintain the operational stability of BSMN. Firstly, the evolution mechanism of BSMN was analyzed. Then, an evolutionary game model between shared manufacturing enterprise nodes was established to deduce the stable state of BSMN. Based on the analysis of the evolutionarily stable state, an incentive model with three strategies to encourage different enterprise nodes to participate in "accounting" operations was determined, and smart contracts of the incentive models were developed. Finally, the effectiveness of the proposed incentive models was verified by simulation experiments. [ABSTRACT FROM AUTHOR]

To satisfy the varied requirements of customers and improve their sense of participation, manufacturers collaborate with equipment providers and customers to produce customer-centered individualized products. This can be achieved with the aid of new information technologies such as Cyber-Physical System (CPS) and advanced business models such as Product-Service System (PSS). However, how to incorporate CPS and PSS into the shop floor-level production system to support service-oriented collaborative manufacturing operations such as production monitoring is a vital issue. This work proposes a Cyber-Physical Production Monitoring Service System (CPPMSS) for the collaborative production monitoring of individualized product orders. First, the business model, framework, and operating logic of CPPMSS are researched. Second, the energy-aware production monitoring service for customers, manufacturers, and equipment providers in the CPPMSS is clarified. Third, a demonstrative case is studied to verify the feasibility of CPPMSS. The results show that CPPMSS can satisfy the collaborative manufacturing operations among different stakeholders in the mass individualization environment. [ABSTRACT FROM AUTHOR]