Topics: Algorithms , Design , Evaluation methods , Machinery , Manufacturing , Chain , Tolerance analysis , Maintenance , Deformation , Artificial neural networks

Prototyping Human-Centered Products in the Age of Industry 4.0

Salman Ahmed, Lukman Irshad, H. Onan Demirel

J. Mech. Des. July 2021, 143(7): 071102. doi: https://doi.org/10.1115/1.4050736

Abstract

View articletitled, Prototyping Human-Centered Products in the Age of Industry 4.0

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Topics: Computer-aided design , Design , Engineering prototypes , Ergonomics , Modeling , Assembly lines

Design Theory and Methodology

Are Two Heads Better Than One for Computer-Aided Design?

Vrushank Phadnis, Hamza Arshad, David Wallace, Alison Olechowski

J. Mech. Des. July 2021, 143(7): 071401. doi: https://doi.org/10.1115/1.4050734

Abstract

View articletitled, Are Two Heads Better Than One for Computer-Aided Design?

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Topics: Computer-aided design , Design

Design Automation

Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation

Qiliang Chen, Babak Heydari, Mohsen Moghaddam

J. Mech. Des. July 2021, 143(7): 071701. doi: https://doi.org/10.1115/1.4049531

Abstract

View articletitled, Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation

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Topics: Algorithms , Artificial neural networks , Manufacturing , Reinforcement learning , Robotics , Robots , Machinery , Resilience , Inspection , Materials handling

Design of Trustworthy Cyber–Physical–Social Systems With Discrete Bayesian Optimization

Yan Wang

J. Mech. Des. July 2021, 143(7): 071702. doi: https://doi.org/10.1115/1.4049532

Abstract

View articletitled, Design of Trustworthy Cyber–Physical–Social Systems With Discrete Bayesian Optimization

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Topics: Design , Optimization

Designing a Shape–Performance Integrated Digital Twin Based on Multiple Models and Dynamic Data: A Boom Crane Example

Xiaonan Lai, Shuo Wang, Zhenggang Guo, Chao Zhang, Wei Sun, Xueguan Song

J. Mech. Des. July 2021, 143(7): 071703. doi: https://doi.org/10.1115/1.4049861

Abstract

View articletitled, Designing a Shape–Performance Integrated Digital Twin Based on Multiple Models and Dynamic Data: A Boom Crane Example

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Topics: Cranes , Sensors , Design , Shapes , Computer simulation , Finite element model , Stress

Design for Manufacture and the Life Cycle

Knowledge-Based Design Guidance System for Cloud-Based Decision Support in the Design of Complex Engineered Systems

Ru Wang, Jelena Milisavljevic-Syed, Lin Guo, Yu Huang, Guoxin Wang

J. Mech. Des. July 2021, 143(7): 072001. doi: https://doi.org/10.1115/1.4050247

Abstract

View articletitled, Knowledge-Based Design Guidance System for Cloud-Based Decision Support in the Design of Complex Engineered Systems

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Topics: Design , Uncertainty

Resource-Constrained Scheduling for Multi-Robot Cooperative Three-Dimensional Printing

Laxmi Poudel, Wenchao Zhou, Zhenghui Sha

J. Mech. Des. July 2021, 143(7): 072002. doi: https://doi.org/10.1115/1.4050380

Abstract

View articletitled, Resource-Constrained Scheduling for Multi-Robot Cooperative Three-Dimensional Printing

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Topics: Printing , Robots , Collisions (Physics)

Low-Cycle Fatigue Lifetime Estimation and Predictive Maintenance for a Gas Turbine Compressor Vane Carrier Under Varying Operating Conditions

Zixi Han, Zixian Jiang, Sophie Ehrt, Mian Li

J. Mech. Des. July 2021, 143(7): 072003. doi: https://doi.org/10.1115/1.4049968

Abstract

View articletitled, Low-Cycle Fatigue Lifetime Estimation and Predictive Maintenance for a Gas Turbine Compressor Vane Carrier Under Varying Operating Conditions

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Topics: Algorithms , Cycles , Damage , Engines , Gas turbines , Low cycle fatigue , Maintenance , Statistical distributions , Stress , Compressors

Fault-Tolerant Control of Programmable Logic Controller-Based Production Systems With Deep Reinforcement Learning

Jonas Zinn, Birgit Vogel-Heuser, Marius Gruber

J. Mech. Des. July 2021, 143(7): 072004. doi: https://doi.org/10.1115/1.4050624

Abstract

View articletitled, Fault-Tolerant Control of Programmable Logic Controller-Based Production Systems With Deep Reinforcement Learning

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Topics: Actuators , Algorithms , Cranes , Manufacturing systems , Reinforcement learning , Simulation , Space , Control equipment , Sensors , Multi-agent systems