Digital twin-based sustainable intelligent manufacturing: a review

He, Bin; Bai, Kai-Jian

doi:10.1007/s40436-020-00302-5

Cited by 400 publications

(193 citation statements)

References 236 publications

(194 reference statements)

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“…Puolakanaho [18] simulated various mathematical models to predict and validate the system behavior to be used in a digital twin model. He and Bai [114] reviewed the use of digital twins for product maintenance and fault diagnosis through predictive models such as artificial intelligence. Smetana et al [31] delineated the evolution of Cyber-Physical Systems for material recovery and life cycle assessment through neural networks and blockchain.…”

Section: Applications Of Digital Technologiesmentioning

confidence: 99%

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

2020

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Manufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.

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Section: Applications Of Digital Technologiesmentioning

confidence: 99%

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

2020

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“…Bilberg and Malik [21] developed a DT-driven human-robot assembly system that extends the use of virtual simulation models developed in the design phase of a production system to the operations for real-time control, dynamic skill-based tasks allocation between human and robot, sequencing of tasks, and developing a robot's program accordingly. He and Bai [22] proposed the framework of DT-driven sustainable intelligent manufacturing. To provide guidance for practical development and implementation, Liu et al [23] developed a conceptual framework for DT-enabled collaborative data management for metal additive manufacturing systems, where a cloud DT communicates with distributed edge DTs in different product life-cycle stages.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Digital Twin for Engine Block Manufacturing Processes

et al. 2020

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The digital twin (DT) is undergoing an increase in interest from both an academic and industrial perspective. Although many authors proposed and described various frameworks for DT implementation in the manufacturing industry context, there is an absence of real-life implementation studies reported in the available literature. The main aim of this paper is to demonstrate feasibility of the DT implementation under real conditions of a production plant that is specializing in manufacturing of the aluminum components for the automotive industry. The implementation framework of the DT for engine block manufacturing processes consists of three layers: physical layer, virtual layer and information-processing layer. A simulation model was created using the Tecnomatix Plant Simulation (TPS) software. In order to obtain real-time status data of the production line, programmable logic control (PLC) sensors were used for raw data acquisition. To increase production line productivity, the algorithm for bottlenecks detection was developed and implemented into the DT. Despite the fact that the implementation process is still under development and only partial results are presented in this paper, the DT seems to be a prospective real-time optimization tool for the industrial partner.

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“…Some of the most frequently used modeling techniques include linear and nonlinear regression models, artificial neural networks, and fuzzy and neurofuzzy inferences systems. Other tools, such as digital twins [69], [70] and cyber-physical systems [71], can be used. For making compatible the dimensions of the indicators, they are normalized by using the equations:…”

Section: B: Second Step: Sustainability Indicators Selection and Weigmentioning

confidence: 99%

Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process

et al. 2020

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Optimization on the basis of sustainability brings important benefits to manufacturing process as sustainable productions constitute a crucial aspect in modern manufacturing. This paper presents a new formalized framework for optimizing the sustainability of manufacturing processes. Unlike previous approaches, the proposed technique combines a methodology for selecting the sustainability indicators and a multi-objective optimization for improving the three sustainability pillars (economy, environment and society). While selecting the significant sustainability indicators in the considered manufacturing process relies on the ABC judgment method, the Saaty's method enables weighting the chosen indicators in order to combine them into suitable economic, environmental and social sustainability indexes. Other technological aspects, usually taken as objectives in previous works, are considered constraints in the proposed approach. The optimization is performed by using nature inspired heuristics, which return the set of non-dominated solutions (also known as Pareto front), from which the most convenient alternative is chosen by the decision maker, depending on the specific conditions of the process. For illustrating the usage of the proposed framework, it is applied to the optimization of a submerged arc welding process. Compared with currently used welding parameters, the computed optimal solution outperforms the economic and environmental sustainability while keeps equal the social impact. The results show not only the effectiveness of the proposed approach, but also its flexibility by giving a set of possible solutions which can be chosen depending on how are ranked the sustainability pillars.

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Digital twin-based sustainable intelligent manufacturing: a review

Cited by 400 publications

References 236 publications

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

Implementation of Digital Twin for Engine Block Manufacturing Processes

Towards Sustainability of Manufacturing Processes by Multiobjective Optimization: A Case Study on a Submerged Arc Welding Process

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