Journal of Computing and Information Science in Engineering: Recent submissions
Now showing items 621-640 of 1535
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Boundary Encryption-Based Monte Carlo Learning Method for Workspace Modeling
(The American Society of Mechanical Engineers (ASME), 2020)As an important branch of machine learning, Monte Carlo learning has been successfully applied to engineering design optimization and product predictive analysis, such as design optimization of heavy machinery. However, ... -
Automatic Extraction of Engineering Rules From Unstructured Text: A Natural Language Processing Approach
(The American Society of Mechanical Engineers (ASME), 2020)Manufacturers use cloud manufacturing platforms to offer their services. The literature has suggested a semantic web-based cloud manufacturing framework, in which engineering knowledge is modeled using structured syntax. ... -
Trajectory-Tracking-Based Adaptive Neural Network Sliding Mode Controller for Robot Manipulators
(The American Society of Mechanical Engineers (ASME), 2020)Unpredictable disturbances and chattering are the major challenges of the robot manipulator control. In recent years, trajectory-tracking-based controllers have been recognized by many researchers as the most promising ... -
A Gait Trajectory Control Scheme Through Successive Approximation Based on Radial Basis Function Neural Networks for the Lower Limb Exoskeleton Robot
(The American Society of Mechanical Engineers (ASME), 2020)Stability control is critical to the exoskeleton robot controller design. Considering the complex structural characteristics of lower limb exoskeleton robots, the major challenge of the controller design is the accuracy ... -
sMF-BO-2CoGP: A Sequential Multi-Fidelity Constrained Bayesian Optimization Framework for Design Applications
(The American Society of Mechanical Engineers (ASME), 2020)Bayesian optimization (BO) is an efiective surrogate-based method that has been widely used to optimize simulation-based applications. While the traditional Bayesian optimization approach only applies to single-fidelity ... -
A Supervised Machine Learning Approach for Intelligent Process Automation in Container Logistics
(The American Society of Mechanical Engineers (ASME), 2020)Process control in manufacturing industries usually lacks flexibility and adaptability. The process planning is traditionally pursued within the production scheduling and then remains unchanged until a major overhaul is ... -
Automatic Detection of Manufacturing Equipment Cycles Using Time Series
(The American Society of Mechanical Engineers (ASME), 2020)Manufacturing industry companies are increasingly interested in using less energy in order to enhance competitiveness and reduce environmental impact. To implement technologies and make decisions that lead to less energy ... -
Data-Driven Concept Network for Inspiring Designers’ Idea Generation
(The American Society of Mechanical Engineers (ASME), 2020)Big-data mining brings new challenges and opportunities for engineering design, such as customer-needs mining, sentiment analysis, knowledge discovery, etc. At the early phase of conceptual design, designers urgently need ... -
A Predictive Analytics Tool to Provide Visibility Into Completion of Work Orders in Supply Chain Systems
(The American Society of Mechanical Engineers (ASME), 2020)In current supply chain operations, original equipment manufacturers (OEMs) procure parts from hundreds of globally distributed suppliers, which are often small- and medium-scale enterprises (SMEs). The SMEs also obtain ... -
Machine Learning-Based Reverse Modeling Approach for Rapid Tool Shape Optimization in Die-Sinking Micro Electro Discharge Machining
(The American Society of Mechanical Engineers (ASME), 2020)This paper focuses on efficient computational optimization algorithms for the generation of micro electro discharge machining (µEDM) tool shapes. In a previous paper, the authors presented a reliable reverse modeling ... -
A Predictive Dispatching Rule Assisted by Multi-Layer Perceptron for Scheduling Wafer Fabrication Lines
(The American Society of Mechanical Engineers (ASME), 2020)Reentrant flow plays an important role for the allocation of limited resources in semiconductor manufacturing. In particular, over- or under-loading of workstations may deteriorate performances of the whole production line. ... -
NBLSTM: Noisy and Hybrid Convolutional Neural Network and BLSTM-Based Deep Architecture for Remaining Useful Life Estimation
(The American Society of Mechanical Engineers (ASME), 2020)Smart manufacturing and industrial Internet of things (IoT) have transformed the maintenance management concept from the conventional perspective of being reactive to being predictive. Recent advancements in this regard ... -
Automated Classification of Manufacturing Process Capability Utilizing Part Shape, Material, and Quality Attributes
(The American Society of Mechanical Engineers (ASME), 2020)The ability to classify the capabilities of different manufacturing processes based on computer-aided design (CAD) models of parts is a key missing link in cybermanufacturing. In this paper, we present a one-step approach ... -
Image Data-Based Surface Texture Characterization and Prediction Using Machine Learning Approaches for Additive Manufacturing
(The American Society of Mechanical Engineers (ASME), 2020)The increase in the use of metal additive manufacturing (AM) processes in major industries like aerospace, defense, and electronics indicates the need for maintaining a tight quality control. A quick, low-cost, and reliable ... -
An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System
(The American Society of Mechanical Engineers (ASME), 2020)A cyber-physical system (CPS) is one of the key technologies of industry 4.0. It is an integrated system that merges computing, sensors, and actuators, controlled by computer-based algorithms that integrate people and ... -
A Simulation Data-Driven Design Approach for Rapid Product Optimization
(The American Society of Mechanical Engineers (ASME), 2020)Traditional design optimization is an iterative process of design, simulation, and redesign, which requires extensive calculations and analysis. The designer needs to adjust and evaluate the design parameters manually and ... -
Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network
(The American Society of Mechanical Engineers (ASME), 2020)The failure prognosis is crucial for industrial equipment in prognostics and health management field. The vibration signal is the commonly used data for failure prognosis. The conventional prognostic approaches have ... -
An Integrated Target Acquisition Approach and Graphical User Interface Tool for Parallel Manipulator Assembly
(The American Society of Mechanical Engineers (ASME), 2020)In this paper, two integrated target identification and acquisition algorithms and a graphical user interface (GUI) simulation tool for automated assembly of parallel manipulators are proposed. They seek to identify the ... -
A Coarse-Grained Regularization Method of Convolutional Kernel for Molten Pool Defect Identification
(The American Society of Mechanical Engineers (ASME), 2020)Machine vision has a wide range of applications in the field of welding. The rise of convolutional neural network (CNN) provides a new way to extract visual features of welding. Due to the limitation of the small size of ... -
Dilated Convolution Neural Network for Remaining Useful Life Prediction
(The American Society of Mechanical Engineers (ASME), 2020)Accurate prediction of remaining useful life (RUL) plays an important role in reducing the probability of accidents and lessening the economic loss. However, traditional model-based methods for RUL are not suitable when ...