... network design, (3) using an ensemble of surrogates to increase the accuracy of prediction. We also demonstrate how to integrate surrogate models and machine learning with time series analysis for more accurate and faster prediction. Due to the availability of data, we use the Buffalo Reservoir in the Red...

... Abstract Data sparsity is still the main challenge to apply machine learning models to solve complex scientific and engineering problems. The root cause is the “curse of dimensionality” in training these models. Training algorithms need to explore and exploit in a very high dimensional...

... Abstract Efficient predictive models for large deflection of beams are critical for the design and analysis of compliant mechanisms. Machine learning (ML) models for predicting large tip deflection and shape of 2D cantilever beams are proposed for the analysis of compliant mechanisms...

... Abstract This work brings together rigid body kinematics with machine learning to present a mechanism synthesis pipeline for design and development of a Sit-to-Stand (STS) device. Practical device design problems require multiple constraints to be satisfied simultaneously. Most of the focus...

... for effective communication, analysis, cataloging, and classification. planar linkage mechanisms simulation machine learning deep learning object detection Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference...

... learning. First, this study voxelised 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine...

...Abstract Abstract A task motion trajectory usually needs to be determined for the training process and mechanism design for rehabilitation patients since they are not capable of providing a normal motion. In this paper, a machine-learning-based approach of gait trajectory prediction for lower...

... Deshpande, Zhijie Lyu, Anurag Purwar Computer-Aided Design and Innovation Lab Department of Mechanical Engineering Stony Brook University Stony Brook, New York, 11794-2300 ABSTRACT nar Mechanisms, Machine Learning, Deep Learning, Variational This paper brings together rigid body kinematics and ma...

..., two categories of Machine Learning (ML) and Deep Learning (DL) techniques are used to classify consumer electronics. ML models include Naïve Bayes with Bernoulli, Gaussian, Multinomial distributions, and Support Vector Machine (SVM) algorithms with four kernels of Linear, Radial Basis Function (RBF...

... Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2021 August 17-19, 2021, Virtual, Online DETC2021-71333 MACHINE LEARNING TO PREDICT MEDICAL DEVICES REPAIR AND MAINTENANCE NEEDS Hao-yu Liao Karthik...

... Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2021 August 17-19, 2021, Virtual, Online DETC2021-70613 CAN MACHINE LEARNING TOOLS SUPPORT THE IDENTIFICATION OF SUSTAINABLE DESIGN LEADS FROM...

... Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2021 August 17-19, 2021, Virtual, Online DETC2021-68469 A MACHINE LEARNING METHOD FOR STATE OF CHARGE ESTIMATION IN LEAD-ACID BATTERIES FOR HEAVY...

... Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2021 August 17-19, 2021, Virtual, Online DETC2021-69337 AN APPLICATION OF MACHINE LEARNING TO PREDICT STIFFNESS DISCRIMINATION THRESHOLDS USING...

... Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2021 August 17-19, 2021, Virtual, Online DETC2021-68177 OPTIMAL RELEASE PLANNING USING MACHINE LEARNING AND LINEAR INTEGER PROGRAMMING FOR IDEAS...

... image. Each depth image is discretized into 100 equal regions of interest and then labeled accordingly. Using the image dataset, four Machine Learning (ML) classification models are trained and compared, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN...

... transferability for different situations, increasing its versatility and applicability. This, in turn, reduces the computational burden of evaluation and improves operational efficiency. Kansei attribute evaluation convolutional neural networks machine learning Proceedings of the ASME 2021...

.... This system shell is intended to have the following capabilities: (1) interactive and system-guided design process to analyze design structure and to characterize design options, (2) to provide interactive and system-guided knowledge acquisition, classification, and retrieval to achieve machine learning...

... in debugging, but also helps the user to be convinced of the outcomes of the reasoning of PROCASE. case-based reasoning CAD/CAM process planning machine learning Computers in Engineering 1993 ASME 1993 PROCASE: A PROTOTYPE OF INTELLIGENT CASE-BASED PROCESS PLANNING SYSTEM WITH SIMULATION...

... Learning and Inference. The subject of the study was the acquisition of design knowledge for proactive design. Conclusions of the research conducted are provided. proactive design knowledge acquisition machine learning perceived quality Proceedings of the 1997 DETC The 1997 ASME Design Theory...

...DATA MINING FROM ENDMILL TOOL CATALOG INFORMATION BASED ON THE USE OF A MACHINE LEARNING METHOD Akihito Asakura1, Toshiki Hirogaki1, Eiichi Aoyama1, Hiroyuki Kodama2 1 Department of Mechanical Engineering Doshisha University 1-3 Tataramiyakodani, Kyotanabe-shi Kyoto 610 0394, Japan 2 Graduate...