A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing

Assembly stations are important hubs that connect massive material, information, human labor, etc. The fixed-position assembly systems for complex products may deal with hundreds of thousands of processes, making them vulnerable to manufacturing exceptions. Many scheduling problems were described and solved in the past decades, however, the gap between theoretical models and industrial practices still exist. To achieve a practical method for the dynamic scheduling in case of exceptions while reducing the impact brought by the exceptions, an Intelligent Collaborative Mechanism (ICM) was proposed where negotiations on resource configuration may happen among tasks (i.e. assembly processes). The intercommunication among resources was guaranteed by the data-driven ICM framework. The Petri-net-based workflow analysis and the constraint matrix can pick out the tasks that are currently not bound by other ones. The dynamic priority of the processes was defined and obtained using grey relational analysis. The matching strategy among the selected tasks and operators can provide a scheduling plan that is close to the initial plan, so the assembly systems may remain effective even when exceptions occur. The proposed models were analyzed in a case scenario, where the impact brought by exceptions can decrease by 44.3% in terms of the operators’ utilization rate, and by 60.26% in terms of the assembly time. This research has provided a practical strategy to improve the flexibility and effectiveness of assembly systems for complex products.     

A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries

To reduce the production costs and breakdown risks in industrial manufacturing systems, condition-based maintenance has been actively pursued for prediction of equipment degradation and optimization of maintenance schedules. In this paper, a two-stage maintenance framework using data-driven techniques under two training types will be developed to predict the degradation status in industrial applications. The proposed framework consists of three main blocks, namely, Primary Maintenance Block (PMB), Secondary Maintenance Block (SMB), and degradation status determination block. As the popular methods with deterministic training, back-propagation Neural Network (NN) and evolvable NN are employed in PMB for the degradation prediction. Another two data-driven methods with probabilistic training, namely, restricted Boltzmann machine and deep belief network are applied in SMB as the backup of PMB to model non-stationary processes with the complicated underlying characteristics. Finally, the multiple regression forecasting is adopted in both blocks to check prediction accuracies. The effectiveness of our proposed two-stage maintenance framework is testified with extensive computation and experimental studies on an industrial case of the wafer fabrication plant in semiconductor manufactories, achieving up to 74.1% in testing accuracies for equipment degradation prediction.     

A knowledge-based process planning framework for wire arc additive manufacturing

Wire arc additive manufacturing (WAAM) provides a rapid and cost-effective solution for fabricating low-to-medium complexity and medium-to-large size metal parts. In WAAM, process settings are well-recognized as fundamental factors that determine the performance of the fabricated parts such as geometry accuracy and microstructure. However, decision-making on process variables for WAAM still heavily relies on knowledge from domain experts. For achieving reliable and automated production, process planning systems that can capture, store, and reuse knowledge are needed. This study proposes a process planning framework by integrating a WAAM knowledge base together with our in-house developed computer-aided tools. The knowledge base is construed with a data-knowledge-service structure to incorporate various data and knowledge including metamodels and planning rules. Process configurations are generated from the knowledge base and then used as inputs to computer-aided tools. Moreover, the process planning system also supports the early-stage design of products in the context of design for additive manufacturing. The proposed framework is demonstrated in a digital workflow of fabricating industrial-grade components with overhang features.     

A framework for tool-path airtime optimization in material extrusion additive manufacturing

Building time is an important issue in material extrusion-based additive manufacturing because in such a process head repositioning is always required between deposition segments (contours and rasters). The length of head repositioning, usually referred to as tool-path airtime or non-productive time, can be minimized by applying optimization algorithms. A particular issue in this area is the size of the problem to be solved. In this work, this problem is detailed and a framework for its decomposition and simplification is presented. The framework was divided in four (4) main steps and was designed to be used with different optimization methods. The first step was designed with some innovative methods to reduce the problem size. Hybrid mixed integer linear programming (MILP) models were implemented to solve steps two (2) to four (4). Three cases of different complexities were analyzed and compared with the solution from a classic greedy optimization algorithm. The results show that the framework was effective in dealing with this problem and, particularly for the cases analyzed, it was possible to reduce the repositioning distance considerably over a non-optimized route and by greedy optimization (with the best case reaching 69% and 17.8%, respectively).     

A novel data-driven graph-based requirement elicitation framework in the smart product-service system context

Nowadays, industrial companies are facing ever-increasing challenges to generate new value-in-use and maintain their high competitiveness in the market. With the rapid development of Information and Communication Technology (ICT), IT is embedded in the products themselves, i.e. smart, connected products (SCPs) to generate values. Hence, an emerging value proposition paradigm, smart product-service system (Smart PSS) was introduced, by leveraging both SCPs and its generated services as a solution bundle to meet individual customer needs. Unlike other types of PSS, in Smart PSS, massive user-generated data and product-sensed data are collected during the usage phase, where potential requirements can be elicited readily in a value co-creation manner with context-awareness. Nevertheless, only few scholars discuss any systematic manner to support requirement elicitation in such context. To fill the gaps, this research proposes a novel data-driven graph-based requirement elicitation framework in the Smart PSS, so as to assist engineering/designers make better design improvement or new design concept generation in a closed-loop manner. It underlines the informatics-based approach by integrating heterogeneous data sources into a holistic consideration. Moreover, by leveraging graph-based approach, context-product-service information can be linked by the edges and nodes in-between them to derive reliable requirements. To validate its feasibility and advantages, an illustrative example of smart bicycle design improvement is further adopted. As an explorative study, it is hoped that this work provides useful insights for the requirement elicitation process in today’s smart connected environment.     

制造大数据相关技术架构分析

针对大数据技术给制造业带来的机遇和挑战,通过分析制造大数据的研究现状和产生,给出了制造大数据的定义。依据制造大数据的处理流程构建其技术架构,并介绍了相应的关键技术。最后列举了几种典型的应用场景,指出了制造大数据面临的挑战并展望下一步发展方向。     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

Secure sharing of big digital twin data for smart manufacturing based on blockchain

With the rapid development of digital twin technology, a large amount of digital twin data named as big digital twin data (BDTD), is generated in the lifecycle of equipment, which is supposed to be used in digital twin enabled applications. However, in the implementation of these applications, data sharing problem which is caused by the lack of data security as well as trust among stakeholders of equipment, limits data using value. It is a novel way to introduce blockchain technology into digital twin to solve the problem. However, current methods cannot fulfill the requirements of exponential growth and timely sharing of BDTD. Therefore, a blockchain-based framework for secure sharing of BDTD is proposed to solve the problems. Cloud storage is integrated into the framework, with which, BDTD is encrypted and stored in Cloud, while the hash of BDTD and transaction records are stored in blockchain. Some rules of generating new block are designed to improve the processing speed of blockchain. An algorithm for optimal sampling rate selection is presented to maximize total social benefits of the participants of BDTD sharing. Simulation results show that the algorithm is better than traditional method for maximizing the total social benefits. Furthermore, a protype system is developed and evaluated based on Fabric test network. Evaluation results show that BDTD can be shared securely multiple times per second through the framework, which demonstrates the feasibility of the framework in supporting timely sharing of BDTD.     

An integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing

With the development of a new generation of information technology, smart manufacturing has put forward higher requirements for supply chain. It is necessary to ensure the synchronization of the supply chain operation and maintain the reliability of the supply chain management, therefore the trust evaluation for the supply chain becomes extremely important. Traditional supply chain management has problems such as information flow is easy to be tampered with, logistics is difficult to trace, and capital flow is not true, which leads to increased opportunity costs due to the lack of trust among transaction entities in the supply chain. The emergence of blockchain technology provides an opportunity to improve the supply chain ecosystem. In this paper, an integrated framework for blockchain-enabled supply chain trust management towards smart manufacturing is proposed to explain how to enhance trust management with the help of blockchain from the perspectives of information flow, logistics, and capital flow. An optimized trust management model is designed for better entities evaluation in supply chain. A coal mine equipment manufacturing industry scenario is presented to illustrate the effectiveness of the proposed framework.     

Knowledge-based integrated product design framework towards sustainable low-carbon manufacturing

With a global challenge on the serious ecological problems, low-carbon manufacturing aiming to reduce carbon emission and resource consumption is gaining the ever-increasing attention. Due to the significant impact on the product lifecycle, low-carbon product design is considered as an effective and attractive approach to improve the eco-market trade-off of electromechanical products. Existing low-carbon product design approaches focus on solving specific low-carbon problems, and how to explore and navigate the integrative design space considering low-carbon and knowledge in a holistic perspective is rarely discussed. In response, this paper proposes a knowledge-based integrated product design framework to support low-carbon product development. An ontology-based knowledge modelling approach is put forward to represent the multidisciplinary design knowledge to facilitate knowledge sharing and integration. Subsequently, a function–structure synthesis approach based on case-based reasoning is presented to narrow down the design space to generate suitable design solutions for achieving desired functions. A multi-objective mathematical model is established, and the multi-objective particle swarm optimization is adopted to solve the low-carbon product optimization. Furthermore, a decision-making ranking approach based on the closeness degree is employed to prioritize the potential solutions from Pareto set. Finally, a case study of low-carbon product design of hydraulic machine is demonstrated to show the effectiveness.     

A framework for data-driven informatization of the construction company

With the advent of big data era, the construction industry has focused on processing large quantities of engineering data and extracting their value. However, inaccurate manual entries and delayed data collection have created difficulties in making full use of information. Meanwhile, difficulty sharing data and weak interoperability of data among business information systems also leaves company headquarters without the resource integration that can facilitate decision making. To overcome these challenges, we proposed a big data infrastructure called the enterprise integrated data platform (EIDP) for use by construction companies. We discuss a case study, and offer a framework for future business improvement that contributes to closed-loop construction supply chain management, cost management and control, knowledge discovery, and decision making. The proposed informatization solution provides a theoretical basis for realizing data sharing and interoperability between business management and project management. On this basis, it will help construction companies to improve the efficiency of both company operations and project delivery by optimizing the business process and supporting decision making.     

A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing

Effective supplier management is critical for an enterprise’s success, as supplier procurement accounts for up to approximately 70% to 80% of total manufacturing costs. Correct supplier selection can ensure the competitiveness of the enterprise and contribute to supply chain integration and innovation. In recent years, supplier evaluation frameworks based on Industry 4.0 concepts have contributed to the development of the industry. However, the novel concepts of Industry 5.0 require examination from a people-oriented and sustainable perspective. Unfortunately, at the present time, supplier evaluation frameworks based on Industry 5.0 are lacking. Therefore, the primary task of this study is to develop a novel and comprehensive supplier evaluation framework for the Industry 5.0 era. This study proposes a data-driven decision support system to execute the supplier evaluation process. First, variable precision- dominance-based rough set approach (VC-DRSA) is applied to extract the core criteria, to remove the noise factors and to generate decision rules for the decision-makers’ reference. Second, the criterion importance through intercriteria correlation (CRITIC) approach is adopted to obtain the dependency weights of the core criteria and their ranking. Finally, a modified classifiable technique for order preference by similarity to ideal solution (CTOPSIS) is used to integrate the final performance values of suppliers when new alternative suppliers are added. The research concept is in line with the conception of data-driven decision support in business intelligence and does not rely on the subjective judgments and opinions of experts. Data provided by a multinational medical equipment manufacturer are used as an example to demonstrate the proposed model. VC-DRSA retains nine core criteria from the original twenty criteria, which greatly reduces the labor and cost of supplier audits. In addition, the CRITIC results show that digital transformation, real-time information sharing, and organizational culture transformation are the three main factors affecting the development of enterprises towards Industry 5.0. The results show that CTOPSIS can be used to quickly assess the ratings of new alternative suppliers are listed.     

An IIoT-driven and AI-enabled framework for smart manufacturing system based on three-terminal collaborative platform

Smart manufacturing has great potential in the development of network collaboration, mass personalised customisation, sustainability and flexibility. Customised production can better meet the dynamic user needs, and network collaboration can significantly improve production efficiency. Industrial internet of things (IIoT) and artificial intelligence (AI) have penetrated the manufacturing environment, improving production efficiency and facilitating customised and collaborative production. However, these technologies are isolated and dispersed in the applications of machine design and manufacturing processes. It is a challenge to integrate AI and IIoT technologies based on the platform, to develop autonomous connect manufacturing machines (ACMMs), matching with smart manufacturing and to facilitate the smart manufacturing services (SMSs) from the overall product life cycle. This paper firstly proposes a three-terminal collaborative platform (TTCP) consisting of cloud servers, embedded controllers and mobile terminals to integrate AI and IIoT technologies for the ACMM design. Then, based on the ACMMs, a framework for SMS to generate more IIoT-driven and AI-enabled services is presented. Finally, as an illustrative case, a more autonomous engraving machine and a smart manufacturing scenario are designed through the above-mentioned method. This case implements basic engraving functions along with AI-enabled automatic detection of broken tool service for collaborative production, remote human-machine interface service for customised production and network collaboration, and energy consumption analysis service for production optimisation. The systematic method proposed can provide some inspirations for the manufacturing industry to generate SMSs and facilitate the optimisation production and customised and collaborative production.     

A data-driven generic simulation model for logistics-embedded assembly manufacturing lines

Simulation has been used to evaluate various aspects of manufacturing systems. However, building a simulation model of a manufacturing system is time-consuming and error-prone because of the complexity of the systems. This paper introduces a generic simulation modeling framework to reduce the simulation model build time. The framework consists of layout modeling software and a data-driven generic simulation model. The generic simulation model was developed considering the processing as well as the logistics aspects of assembly manufacturing systems. The framework can be used to quickly develop an integrated simulation model of the production schedule, operation processes and logistics of a system. The framework was validated by developing simulation models of cellular and conveyor manufacturing systems.     

A parallel computing framework for big data

Big data has received great attention in research and application. However, most of the current efforts focus on system and application to handle the challenges of “volume” and “velocity”, and not much has been done on the theoretical foundation and to handle the challenge of “variety”. Based on metric-space indexing and computationalcomplexity theory, we propose a parallel computing framework for big data. This framework consists of three components, i.e., universal representation of big data by abstracting various data types into metric space, partitioning of big data based on pair-wise distances in metric space, and parallel computing of big data with the NC-class computing theory.     

A dynamic data-driven application simulation framework for contaminant transport problems

We describe, devise, and augment dynamic data-driven application simulations (DDDAS). DDDAS offers interesting computational and mathematically unsolved problems, such as, how do you analyze, compute, and predict the solution of a generalized PDE when you do not know either where or what the boundary conditions are at any given moment in the simulation in advance? A summary of DDDAS features and why this is a intellectually stimulating new field are included in the paper.We apply the DDDAS methodology to some examples from a contaminant transport problem. We demonstrate that the multiscale interpolation and backward in time error monitoring are useful to long running simulations.     

Optimizing supports for additive manufacturing

In additive manufacturing process, support structures are often required to ensure the quality of the final built part. In this article, we present mathematical models and their numerical implementations in an optimization loop, which allow us to design optimal support structures. Our models are derived with the requirement that they should be as simple as possible, computationally cheap, and, yet, based on a realistic physical modelling. Supports are optimized with respect to two different physical properties. First, they must support overhanging regions of the structure for improving the stiffness of the supported structure during the building process. Second, supports can help in channeling the heat flux produced by the source term (typically a laser beam) and thus improving the cooling down of the structure during the fabrication process. Of course, more involved constraints or manufacturability conditions could be taken into account, most notably removal of supports. Our work is just a first step, proposing a general framework for support optimization. Our optimization algorithm is based on the level set method and on the computation of shape derivatives by the Hadamard method. In a first approach, only the shape and topology of the supports are optimized, for a given and fixed structure. In a second and more elaborated strategy, both the supports and the structure are optimized, which amounts to a specific multiphase optimization problem. Numerical examples are given in 2D and 3D.     

DT-II:Digital twin enhanced Industrial Internet reference framework towards smart manufacturing

In this paper, the interplay and relationship between digital twin and Industrial Internet are discussed at first. The sensing/transmission network capability, which is one of the main characteristics of Industrial Internet, can be a carrier for providing digital twin with a means of data acquisition and transmission. Conversely, with the capability of high-fidelity virtual modeling and simulation computing/analysis, digital twin evolving from lifecycle management for a single product to application in production/manufacturing in the shop-floor/enterprise, can further greatly enhance the simulation computing and analysis of Industrial Internet. This paper proposes a digital twin enhanced Industrial Internet (DT-II) reference framework towards smart manufacturing. To further illustrate the reference framework, the implementation and operation mechanism of DT-II is discussed from three perspectives, including product lifecycle level, intra-enterprise level and inter-enterprise level. Finally, steam turbine is taken as an example to illustrate the application scenes from above three perspectives under the circumstance of DT-II. The differences between with and without DT-II for design and development of steam turbine are also presented.