Manufacturing Mode Research Articles

Cross-Domain Access Control Model in Industrial IoT Environment

The Industrial Internet of Things (IIoT) accelerates smart manufacturing and boosts production efficiency through heterogeneous industrial equipment, intelligent sensors, and actuators. The Industrial Internet of Things is transforming from a traditional factory model to a new manufacturing mode, which allows cross-domain data-sharing among multiple system departments to enable smart manufacturing. A complete industrial product comes from the combined efforts of many different departments. Therefore, secure and reliable cross-domain access control has become the key to ensuring the security of cross-domain communication and resource-sharing. Traditional centralized access control schemes are prone to single-point failure problems. Recently, many researchers have integrated blockchain technology into access control models. However, most blockchain-based approaches use a single-chain structure, which has weak data management capability and scalability, while ensuring system security, and low access control efficiency, making it difficult to meet the needs of multi-domain cooperation in IIoT scenarios. Therefore, this paper proposes a decentralized cross-domain access model based on a master–slave chain with high scalability. Moreover, the model ensures the security and reliability of the master chain through a reputation-based node selection mechanism. Access control efficiency is improved by a grouping strategy retrieval method in the access control process. The experimental benchmarks of the proposed scheme use various performance metrics to highlight its applicability in the IIoT environment. The results show an 82% improvement in the throughput for the master–slave chain structure over the single-chain structure. There is also an improvement in the throughput and latency compared to the results of other studies.

Individualized requirement-driven multi-task scheduling in cloud manufacturing using an extended multifactorial evolutionary algorithm

Cloud manufacturing is an emerging manufacturing paradigm, which enables the simultaneous processing of multiple manufacturing tasks based on customer requirements through centralized management and planning of manufacturing services provided by distributed enterprises. How to optimally schedule the multiple manufacturing tasks is an important problem in cloud manufacturing. As cloud manufacturing is a demand-driven manufacturing mode and the requirement of each customer is highly individualized, a new individualized requirement-driven cloud manufacturing multi-task scheduling (IRCMMS) model is proposed in this study. It aims to benefit not only individual customers but also the whole system. To solve the proposed model, an extended multifactorial evolutionary algorithm is designed to obtain the approximate optimal Pareto solution set, which offers more alternatives for the cloud manufacturing system. Experimental results based on different simulation instances confirm the feasibility and effectiveness of the IRCMMS model as well as the efficiency of the algorithm in solving the IRCMMS model.

An effective adaptive adjustment model of task scheduling and resource allocation based on multi-stakeholder interests in cloud manufacturing

Cloud manufacturing (CMfg) is a new manufacturing mode emerging in the global manufacturing industry. One of the key issues in CMfg is task scheduling and resource allocation (TSRA), which is to allocate suitable resources for multi-tasks while satisfying the interests of multi-stakeholders. Among them, dynamic TSRA is a challenging but crucial problem to ensure the smooth operation of CMfg system since it involves several exceptions. Under these contexts, this study first analyzes the optimized objectives and conflict situations that may damage the interests of multi-stakeholders in dynamic TSRA process. And then, four adaptive adjustment strategies are designed to deal with these conflict situations and ensure the smooth operation. After that, an adaptive adjustment TSRA model based on multi-stakeholder interests (TSRA-MSI) is proposed. To solve the problem, a multi-objective algorithm called HHO-NSGA2 is proposed by combining the advantages of standard Harris Hawks Optimizer and Non-dominated Sorting Genetic Algorithm-II, which contains several problem-specific optimization strategies. In the numerical experiments, the superiority of HHO-NSGA2 is demonstrated by comparing with other five algorithms in terms of convergence, diversity, and comprehensive performance. Finally, a case study is conducted under the actual auto parts production environment, and the results also demonstrate the effectiveness of the proposed TSRA-MSI model and HHO-NSGA2 algorithm.

Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

With the adoption of service-oriented manufacturing modes, more and more manufacturing services are released over manufacturing service platforms. As it is known, the problem of the QoS (Quality of Service)-aware manufacturing service composition is NP-hard. Thus, the optimization remains a challenging research issue, especially in the situation of large-scale manufacturing service data which arouse a scalability problem as well. To improve both the optimization performance and scalability of the QoS-aware manufacturing service composition, this paper proposes a scalable and optimal QoS-aware manufacturing service composition approach via business process decomposition. Specifically, the service composition process is decomposed by using a refined process structure tree (RPST). Moreover, an optimized service composition is achieved layer by layer based on the refined process structure tree in a bottom-up manner. For the atomic tasks or the compound tasks in the same layer of RPST, the corresponding QoS-aware service selection is optimized by calculating Skyline services, which can be carried out in parallel if necessary. When the optimization arrives at the root node, the complete service composition plans are derived. In our approach, the optimal manufacturing service candidates are picked out stage by stage. In this way, both the optimality and scalability of the whole approach can be guaranteed. Extensive experiments are conducted to verify the optimality and scalability of our approach.

A Formal Analytical Framework for IoT-Based Plug-And Play Manufacturing System Considering Product Life-Cycle Design Cost

Technological advances in the Internet of Things (IoTs) and the evolution of manufacturing models applied in industrial environments increase the use of concepts, such as plug-and-play in intelligent manufacturing systems for customized products. However, plug-and-play manufacturing modes are subjected to dynamic changes in both products and resources during the runtime phase. Hence, traditional model-based formal verification methods cannot guarantee system dependability before and after the reconfiguration. In addition, customized manufacturing is a product-driven process, which requires product- and equipment-based modeling and analysis to be considered during the product life-cycle management. In this article, a model-data-driven (DD) framework for the formal analysis of production resources is proposed. An IoT-based DD technology is combined with a model-driven approach to ensure dependability verification of formal models in the operational production line phase through real-time resource status feedback. The proposed method is verified experimentally, using a smart factory experimental platform. The results show that the proposed method reduces the time-cost of the formal modeling phase by at least 75%.

Cloud–edge collaboration task scheduling in cloud manufacturing: An attention-based deep reinforcement learning approach

Cloud Manufacturing (CMfg), as a service-oriented manufacturing mode, aims to provide consumers on-demand manufacturing services. The CMfg platform requires task scheduling technology to schedule manufacturing tasks efficiently, and improve resource utilization and customer satisfaction. Existing scheduling models for manufacturing tasks mainly consider maximizing the quality of service for customers but ignore the actual production execution, which will lead to low-quality execution or delayed delivery. To maximize customer satisfaction and balance production, this article studies a cloud–edge collaboration manufacturing task scheduling in CMfg (CETS). CETS refines manufacturing services deployed in the cloud to the factory process level, and schedules tasks according to the real-time production information on the edge side and manufacturing service information on the cloud side. Considering the dynamics of CETS and the complexity of state information in CETS, an attention-based deep reinforcement learning (DRL) algorithm is proposed to solve CETS. First, the CETS is mathematically represented and built as a partially observable Markov decision process. Second, on-policy maximum a posteriori policy optimization (V-MPO) with gated transformer-XL (GTrXL) named AV-MPO is developed. The effectiveness, training stability, generalizability, scalability, and robustness of AV-MPO are investigated. Rule-based algorithms and some state of art DRL algorithms, such as proximal policy optimization (PPO), soft actor-critic (SAC), and dueling deep q network (Dueling DQN), are compared with AV-MPO. The experimental results validate that AV-MPO can deal with the CETS problem more effectively.

Making, Hacking, Coding: Fablabs as Intermediary Platforms for Modes of Social Manufacturing

The article suggests that the emergence of FabLabs and other collaborative spaces of innovation imply new modes of manufacturing, based on a more social, inclusive, and open approach than current industrial mass-production models. We argue that FabLabs are platforms of social manufacturing, allowing different combinations of interactions between industries and individuals, through their activities around making (producing goods from raw materials), hacking (re-using/combining produced goods), and coding (producing and re-using digital goods). The article also defines four different modes of social manufacturing depending on the technologies used (open or proprietary) and the location of manufacturing (local or industrial). The article contributes to the literature on collaborative spaces by explaining prospective scenarios of development in relationship to new modes of production. It also complements the literature by contextualizing the physical spaces where social manufacturing takes place.JEL codes: D20, L23, L60

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Shared manufacturing is recognized as a new point-to-point manufacturing mode in the digital era. Shared manufacturing is referred to as a new manufacturing mode to realize the dynamic allocation of manufacturing tasks and resources. Compared with the traditional mode, shared manufacturing offers more abundant manufacturing resources and flexible configuration options. This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment, and the characteristics of shared manufacturing resource allocation. The execution of manufacturing tasks, in which candidate manufacturing resources enter or exit at various time nodes, enables the dynamic allocation of manufacturing tasks and resources. Then non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithms are designed to solve the model. The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers. In addition, the proposed model’s efficiency, which considers the entries and exits of manufacturing resources in the shared manufacturing environment, is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.

Reform of Talent Training Mode of Mechanical Manufacturing and Automation under the "1+X" Certificate System

In recent years, in order to cultivate the skills of vocational college students, major vocational schools have increased their professional skills training. At present, in the course of mechanical manufacturing and automation, the school mostly adopts the cooperative learning mode. It conducts computer manufacturing skills practice for graduates. At the learning stage, middle school students and company personnel assess them together to improve their professional skills. Under the background of "1+X" certificate, the education of mechanical manufacturing and automation major in vocational colleges continues to realize the "1+X" certificate system, helping students practice knowledge in the process. Based on the "1+X" certificate system, this paper studies how to realize the effective combination of mechanical manufacturing and automation teaching and the "1+X" certification system.

Arc-flow formulations for the one-dimensional cutting stock problem with multiple manufacturing modes

In this paper, an integration of the one-dimensional cutting stock problem with an operational problem that arises in the manufacture of concrete poles is studied. Seeing that poles have a steel structure, different thicknesses of steel bars can be used in their manufacture. This variety in combining the materials to produce the structure of the poles is known as alternative production modes or multiple manufacturing modes. The problem considered here has the objective of minimizing the total cost to meet the demand for poles using the different available configurations. This problem has already been introduced in the literature and it has been formulated as an integer programming problem. To solve it, the column generation procedure was used. The contribution of this paper is to reformulate the cutting stock problem with multiple manufacturing modes using arc-flow formulations. Arc-flow formulations are promising tools to model and solve complex combinatorial problems. Computational tests are performed comparing the formulations using instances from the literature, which were generated based on the data from a civil construction plant. The arc-flow formulations increased the number of instances solved to proven optimality and also reduced solution time. Lower and upper bounds are also improved when compared with the solution proposed in the literature.

Service-Oriented Manufacturing Mode for Agricultural Machinery Manufacturing Industry

Service-Oriented Manufacturing Mode for Agricultural Machinery Manufacturing Industry

Numerical analysis on flow and surface topography of the molten pool in laser additive remanufacturing

Laser additive remanufacturing technology is an important technology in the field of manufacturing technology innovation and development, which has largely revolutionized the design and manufacturing mode of high-end devices to a large extent, and is a key component of green remanufacturing that can effectively promote the sustainable and sound development of the manufacturing industry. A multiphase model of laser additive remanufacturing with pulsed lasers is developed to analyze the evolution of the molten pool, the trend of the surface tension for different process parameters, and their relation to the molten pool morphology. The results demonstrate that the maximum flow velocity at the surface of the molten pool is inversely proportional to the pulsed laser frequency and to the duty ratio. While the trend of the height of the cladding layer corresponds to the trend of the heat accumulation in the molten pool, the width and penetration depth of the cladding layer are governed by the size of the heat-affected zone. The cladding layer and substrate will have poor metallurgical bonding if the pulsed laser frequency is too high. When the duty ratio is too large, the likelihood of over-melting increases, affecting the substrate properties and increasing the surface roughness, which is detrimental to the surface finish of the cladding layer.

Post-depositional disturbance and spatial organization at exposed open-air sites: Examples from the Middle Stone Age of the Makgadikgadi Basin, Botswana

The influence of natural factors such as bioturbation or sediment movement caused by wind and water is a perennial concern for Stone Age site selection and subsequent interpretation. This paper discusses the spatial artefact distribution of five recently excavated, open-air exposed Middle Stone Age (MSA) sites in Ntwetwe Pan, Botswana. The finds comprise lithic assemblages dominated by MSA points, manufactured in a variety of silcretes. The sites were examined following the assumption that archaeological sites are the product of a combination of natural and cultural factors, occurring both during and after artefacts are deposited. The results indicate that some of these exposed pan floor sites do preserve cultural artefact distribution patterns, and that the level of post-depositional disturbance varies locally. Refitting was an important tool of analysis, especially on the largest site, MAK33, where it was possible to identify working areas that focussed on different modes of lithic manufacture. In combination with a chaîne opératoire analysis of lithic production stages, it was then possible to map movement of artefacts across the site. We argue that the spatial organization of open-air sites may preserve behavioural records that are not present at caves and rock shelters, and provide a view into the short-term, single-use locations that likely formed the basis of MSA occupation patterns.

The choice of green manufacturing modes under carbon tax and carbon quota

Green manufacturing has become an important measure for energy conservation and emission reduction. The government has issued a series of policies, including carbon taxes and quotas, to promote green manufacturing. This study identifies effective green manufacturing modes under government carbon tax and carbon quota policies, considers the automobile manufacturing industry as the research object, and constructs three green manufacturing modes: (i) Product Remanufacturing Mode (PRM), (ii) Green Product Manufacturing Mode (GPMM), and (iii) Hybrid Manufacturing Mode. The analysis reveals that (i) Contrary to common sense, when the government carbon quota is larger, the carbon emission of PRM is lower than that of GPMM. However, when the government reduces carbon allowances, the carbon emissions of PRM are higher than that of GPMM. (ii) When the R&D of remanufacturing technology is more difficult than that of green product innovation technology (without considering price competition), if the government sets a low carbon tax, companies will generate more profit by choosing PRM. Otherwise, companies will obtain more profits by choosing GPMM. However, considering price competition, if the carbon tax is low, companies will obtain more profits by choosing GPMM. Or the company will obtain more profits by choosing PRM. (iii) However, when R&D of remanufacturing technology is less difficult than that of green product innovation technology contrary conclusions of (ii) follow.

On Paradigm of Industrial Big Data Analytics: From Evolution to Revolution

The arrival of the intelligent manufacturing and industrial internet era brings more and more opportunities and challenges to modern industry. Specifically, the revolution of the production mode of traditional manufacturing is undergoing thanks to the techniques including but not limited to digits, network, intelligence, and industrial automation fields. As the core link between intelligent manufacturing and industrial internet platform, industrial Big Data analytics has been paid more and more attention by academia and industry. The efficient mining of the high-value information covered under industrial Big Data and the utilization of the real-life industrial process are among the hottest topics at present. Meanwhile, with the advanced development of industrial automation toward knowledge automation, the learning paradigm of industrial Big Data analytics is also evolving accordingly. Therefore, starting from the perspective of industrial Big Data analytics and aiming at the corresponding industrial scenarios, this article actively explores the revolution of the learning paradigm under the background of industrial Big Data: 1) The evolution of the industry Big Data analytics paradigm is analyzed, that is, from isolated learning to lifelong learning, and their relationships are further summarized; 2) Mainstream directions of lifelong learning are listed, and their applications in industrial scenarios are discussed in detail; 3) Prospects and future directions are given.

Intelligent Blanking of Silicon Steel Coil in a Transformer Core Oriented to Green Manufacturing

For transformer enterprises, energy consumption and environmental pollution mainly occur in the manufacturing process. The conventional manufacturing mode does not conform to the current green manufacturing mode. The green manufacturing mode requires enterprises to improve the transformer production technology, the utilization rate of materials and equipment, and the production efficiency and to achieve clean production through energy conservation and consumption reduction. The main objective of this research is to schedule the blanking of multiple transformer cores together rather than the conventional calculation conducted one by one. An optimization model of the silicon steel coil blanking is established, an evaluation method for blanking schemes is proposed, algorithms to solve the optimization model are analyzed in detail, and the solving processes and results are compared through a case study. Compared with the current manual calculation, this paper is of practical significance for transformer manufacturers to improve the production efficiency, reduce material waste, meet the personalized market demand characterized by multiple varieties and small batches, and achieve the green manufacturing of transformers.

Reform of personnel training Mode of Mechanical Design, Manufacturing and Automation Specialty for Innovation and Entrepreneurship Education

Under the condition of new economy, how to implement and promote the new education and teaching concept of "student-centered, achievement-oriented and continuous improvement" in the professional certification of engineering education in China, training high-quality talents to meet the needs of the industry and society is the primary issue of the new engineering reform of traditional engineering majors. Under the background of engineering education certification, the mechanical design, manufacturing and automation major of Qilu University of Technology is oriented to the innovation and reform of the professional training mode of innovation and entrepreneurship education. Its experience can provide reference for the development of traditional engineering majors under the background of new engineering construction.

A Case Study in Social Manufacturing: From Social Manufacturing to Social Value Chain

A new manufacturing mode, called social manufacturing, has been developing widely, and employed in many enterprises across the business value chain in recent years. Faced with this increasing dynamic, both enterprises and customers have to be more aware of the potential opportunity and benefit to be derived from this new manufacturing mode. One benefit is more value-adding potential for both enterprises upstream and customers downstream across the business value chain, compared with the normal mode. This research extends the application of social manufacturing to the entire business value chain system to bring new opportunities and value-adding potential for enterprises. This paper proposes a social value chain system that applies the social manufacturing mode to the entire value chain and contributes to three areas: (1) a new way of thinking for enterprises to create new opportunities to add value throughout the value chain by employing the social manufacturing mode; (2) establishing the social value chain system for all participants/enterprises across the chain in order to gain a win–win situation for all participants; and (3) suggesting some idea of a suitable performance measurement to monitor and evaluate the proposed social value chain system.

Review: Rubens in Repeat: The Logic of the Copy in Colonial Latin America, by Aaron M. Hyman; Clothing the New World Church: Liturgical Textiles of Spanish America, 1520–1820, by Maya Stanfield-Mazzi

Review: <i>Rubens in Repeat: The Logic of the Copy in Colonial Latin America</i>, by Aaron M. Hyman; <i>Clothing the New World Church: Liturgical Textiles of Spanish America, 1520–1820</i>, by Maya Stanfield-Mazzi

Evolutionary game-based incentive models for sustainable trust enhancement in a blockchained shared manufacturing network

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.