Simulation Optimization Framework for Online Deployment and Adjustment of Reconfigurable Machines in Job Shops

Feng, Xuechen; Zhao, Zhiwei; Zhang, Canrong

doi:10.1109/ieem45057.2020.9309782

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…This would also provide them opportunities within automated systems, such as 3D printing toolsets. Where necessary, data files will automatically download onto designated devices during processing time, so there is no human involvement [9]. With the help of AI and sensors enhanced by cloud computing and edge computing [10], the initiative of the digital twin becomes a distributed control system capable of handling increasingly complex operational problems.…”

Section: General Developmentsmentioning

confidence: 99%

Smart Manufacturing with Artificial Intelligence and Digital Twin: A Brief Review

Malik

Rajaguru

Azzawi

2022

2022 8th International Conference on Information Technology Trends (ITT)

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Digital twin and artificial intelligence technologies have proliferated as crucial enablers for Industry 4.0. With a digital twin, companies can digitally test and validate a product before it exists in the real world. By digitally recreating the planned production process for real-world use, engineers can identify any potential process changes before they happen. This brief survey summarizes their general developments and the current state of AI integration in smart manufacturing and advanced robotics. This survey also covers industrial automation and emerging techniques, such as 3D printing.

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Section: General Developmentsmentioning

confidence: 99%

Smart Manufacturing with Artificial Intelligence and Digital Twin: A Brief Review

Malik

Rajaguru

Azzawi

2022

2022 8th International Conference on Information Technology Trends (ITT)

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“…The megatrend toward the volatile market environment and individual customer demand poses new challenges, primarily for production systems and management in industrial enterprises, which can significantly influence the profitability and productivity of manufacturing. Within the various presented concepts and frameworks [ 34 , 35 , 36 , 37 , 38 , 39 ], automated production systems, including mixed reality assistance systems [ 40 , 41 ], could be rapidly modularized [ 42 ] and reconfigured [ 43 , 44 , 45 ], enhanced with AI [ 46 , 47 ] and sensors [ 48 , 49 ] and, in combination with cloud and edge computing [ 50 ], transformed into distributed control systems, while detailed production environments can be generated and updated in the form of 3D point clouds [ 51 , 52 , 53 , 54 , 55 , 56 ]. Based on these infrastructures, DT demonstrates the capability of handling increasingly complex operational problems, such as production planning and scheduling [ 57 , 58 , 59 , 60 ], production monitoring and control [ 61 , 62 , 63 , 64 , 65 , 66 ], quality control and management [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ], as well as logistics […”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

194

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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“…They place the DT in the group of novel and emerging technologies in 4.0 settings and contemplate the importance that the simulation models built with virtual simulation technology have so that smart agents can forecast results themselves, build their own multiobjective optimization models and solve them to better schedule with available algorithms. Within the framework of proposals of DT models acting as a tool to enable the production order scheduling function in the job-shop, Feng et al [23] study the problem of deploying and adjusting reconfigurable machines in a job-shop with several products. This problem requires a solution before being able to make online adjustments to reconfigurable machines.…”

Section: B Virtual Jssp Replication By Implementing the Dtmentioning

confidence: 99%

Smart Digital Twin for ZDM-based job-shop scheduling

Serrano-Ruiz

Mula

2021

2021 IEEE International Workshop on Metrology for Industry 4.0 &Amp; IoT (MetroInd4.0&IoT)

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The growing digitization of manufacturing processes is revolutionizing the production job-shop by leading it toward the Smart Manufacturing (SM) paradigm. For a process to be smart, it is necessary to combine a given blend of data technologies, information and knowledge that enable it to perceive its environment and to autonomously perform actions that maximize its success possibilities in its assigned tasks. Of all the different ways leading to this transformation, both the generation of virtual replicas of processes and applying artificial intelligence (AI) techniques provide a wide range of possibilities whose exploration is today a far from negligible sources of opportunities to increase industrial companies' competitiveness. As a complex manufacturing process, production order scheduling in the job-shop is a necessary scenario to act by implementing these technologies. This research work considers an initial conceptual smart digital twin (SDT) framework for scheduling job-shop orders in a zero-defect manufacturing (ZDM) environment. The SDT virtually replicates the job-shop scheduling issue to simulate it and, based on the deep reinforcement learning (DRL) methodology, trains a prescriber agent and a process monitor. This simulation and training setting will facilitate analyses, optimization, defect and failure avoidance and, in short, decision making, to improve job-shop scheduling.

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Simulation Optimization Framework for Online Deployment and Adjustment of Reconfigurable Machines in Job Shops

Cited by 8 publications

References 8 publications

Smart Manufacturing with Artificial Intelligence and Digital Twin: A Brief Review

Smart Manufacturing with Artificial Intelligence and Digital Twin: A Brief Review

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Smart Digital Twin for ZDM-based job-shop scheduling

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