Journal of Computing and Information Science in Engineering: Recent submissions
Now showing items 161-180 of 1402
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Self-Supervised Learning of Spatially Varying Process Parameter Models for Robotic Finishing Tasks
(The American Society of Mechanical Engineers (ASME), 2023)This article presents a self-supervised learning approach for a robot to learn spatially varying process parameter models for contact-based finishing tasks. In many finishing tasks, a part has spatially varying stiffness. ... -
Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation
(The American Society of Mechanical Engineers (ASME), 2023)This paper introduces a new method using deep neural networks for the interactive digital transformation and simulation of n-bar planar linkages, which consist of revolute and prismatic joints, based on hand-drawn sketches. ... -
An Automatic High-Precision Calibration Method of Legs and Feet for Quadruped Robots Using Machine Vision and Artificial Neural Networks
(The American Society of Mechanical Engineers (ASME), 2023)Kinematics calibration for quadrupled robots is essential to ensuring motion accuracy and control stability. The angle of the leg joints of the quadruped robot is error-compensated to improve its position accuracy. This ... -
An Invariant Representation of Coupler Curves Using a Variational AutoEncoder: Application to Path Synthesis of Four-Bar Mechanisms
(The American Society of Mechanical Engineers (ASME), 2023)This paper focuses on the representation and synthesis of coupler curves of planar mechanisms using a deep neural network. While the path synthesis of planar mechanisms is not a new problem, the effective representation ... -
HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in Computer-Aided Design
(The American Society of Mechanical Engineers (ASME), 2023)To support intelligent computer-aided design (CAD), we introduce a machine learning architecture, namely HG-CAD, that recommends assembly body material through joint learning of body- and assembly-level features using a ... -
Cross-Domain Transfer Learning for Galvanized Steel Strips Defect Detection and Recognition
(The American Society of Mechanical Engineers (ASME), 2023)Defect detection is a crucial direction of deep learning, which is suitable for industrial inspection of product quality in strip steel. As the strip steel production line continuously outputs products, it is necessary to ... -
Three-Dimensional-Slice-Super-Resolution-Net: A Fast Few Shooting Learning Model for 3D Super-Resolution Using Slice-Up and Slice-Reconstruction
(The American Society of Mechanical Engineers (ASME), 2023)3D modeling accurately depicts the physical world but typically requires substantial data acquisition resources and significant storage space. We introduce a novel three-dimensional slice-reconstruction model (3DSR) to ... -
Multi-Modal Machine Learning in Engineering Design: A Review and Future Directions
(The American Society of Mechanical Engineers (ASME), 2023)In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper presents a comprehensive overview of the ... -
Special Issue: Machine Learning and Representation Issues in CAD/CAM
(The American Society of Mechanical Engineers (ASME), 2023)Machine learning (ML), a sub-field of artificial intelligence (AI), is profoundly reshaping various aspects of human life. Its application in engineering systems promises to address long-standing challenges, although it ... -
Human Digital Twin, the Development and Impact on Design
(The American Society of Mechanical Engineers (ASME), 2023)In the past decade, human digital twins (HDTs) attracted attention in both digital twin (DT) applications and beyond. In this paper, we discuss the concept and the development of HDTs, focusing on their architecture, key ... -
The Role of Deep Learning in Manufacturing Applications: Challenges and Opportunities
(The American Society of Mechanical Engineers (ASME), 2023)There is a growing interest in using deep learning technologies within the manufacturing industry to improve quality, productivity, safety, and efficiency, while also reducing costs and cycle time. This position paper ... -
Designing Evolving Cyber-Physical-Social Systems: Computational Research Opportunities
(The American Society of Mechanical Engineers (ASME), 2023)Cyber-physical-social systems (CPSS) are natural extensions of cyber-physical systems that add the consideration of human interactions and cooperation with cyber systems and physical systems. CPSS are becoming increasingly ... -
Combinational Framework for Classification of Bearing Faults in Rotating Machines
(The American Society of Mechanical Engineers (ASME), 5/31/2023 )In rotating machines, roller bearings are important and prone to frequent faults. Hence, accurate classification of bearing faults is significant in the maintenance of machines. Toward this, a framework using the combination ... -
Pointing Tasks Using Spatial Audio on Smartphones for People With Vision Impairments
(The American Society of Mechanical Engineers (ASME), 5/31/2023 )We present an experimental investigation of spatial audio feedback using smartphones to support direction localization in pointing tasks for people with visual impairments (PVIs). We do this using a mobile game based on a ... -
Human Digital Twin, the Development and Impact on Design
(The American Society of Mechanical Engineers (ASME), 8/25/2023 )In the past decade, human digital twins (HDTs) attracted attention in both digital twin (DT) applications and beyond. In this paper, we discuss the concept and the development of HDTs, focusing on their architecture, key ... -
Opportunities and Challenges of Quantum Computing for Engineering Optimization
(The American Society of Mechanical Engineers (ASME), 8/14/2023 )Quantum computing as the emerging paradigm for scientific computing has attracted significant research attention in the past decade. Quantum algorithms to solve the problems of linear systems, eigenvalue, optimization, ... -
The Role of Deep Learning in Manufacturing Applications: Challenges and Opportunities
(The American Society of Mechanical Engineers (ASME), 8/3/2023 1)There is a growing interest in using deep learning technologies within the manufacturing industry to improve quality, productivity, safety, and efficiency, while also reducing costs and cycle time. This position paper ... -
Bayesian, Multifidelity Operator Learning for Complex Engineering Systems–A Position Paper
(The American Society of Mechanical Engineers (ASME), 6/15/2023 )Deep learning has significantly improved the state-of-the-art in computer vision and natural language processing, and holds great potential to design effective tools for predicting and simulating complex engineering systems. ... -
Information Embedding for Secure Manufacturing: Challenges and Research Opportunities
(The American Society of Mechanical Engineers (ASME), 6/9/2023 1)The digitization of manufacturing has transformed the product realization process across many industries, from aerospace and automotive to medicine and healthcare. While this progress has accelerated product development ... -
Zero-Trust for the System Design Lifecycle
(The American Society of Mechanical Engineers (ASME), 6/9/2023 1)In an age of worsening global threat landscape and accelerating uncertainty, the design and manufacture of systems must increase resilience and robustness across both the system itself and the entire systems design process. ...