... polynomial-based features. These features are normalized and visualized using partial dependence plot (PDP) and individual conditional expectation (ICE). Subsequently, ten machine learning classifiers are trained using four features, and their statistical hypothesis test is performed using a 5 × 2 paired t...

...Dehao Liu; Pranav Pusarla; Yan Wang 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...

.... Email: yan.wang@me.gatech.edu Contributed by the Computers and Information Division of ASME for publication in the J ournal of C omputing and I nformation S cience in E ngineering . 01 04 2022 01 10 2022 03 10 2022 08 11 2022 machine learning artificial...

... ,” ASME 2007 International Manufacturing Science and Engineering Conference , Atlanta, GA , Oct. 15–18 , pp. 17 – 26 . [17] Greis , N. P. , Nogueira , M. L. , Bhattacharya , S. , and Schmitz , T. , 2020 , “ Physics-Guided Machine Learning for Self-Aware Machining ,” 2020 AAAI...

... through machine learning. First, this study voxelized 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...

...-ray computed tomography (XCT) images machine learning artificial intelligence data-driven engineering machine learning for engineering applications National Institute of Standards and Technology 10.13039/100000161 70NANB19H097 With years of development, additive manufacturing...

... considered in the evaluation process to establish the information association between EEG and performance levels. Moreover, intelligent psycho-physiological analysis that incorporates EEG into the fuzzy comprehensive evaluation (FCE) and machine learning methods is adopted within the proposed method...

... learning is a learning method that uses existing knowledge to solve problems in different but related fields. It relaxes two basic assumptions in traditional machine learning to migrate existing knowledge to deal with learning problems in the target area where there is only a small quantity of tagged...

...Shrinath Deshpande; Anurag Purwar This paper brings together computer vision, mechanism synthesis, and machine learning to create an image-based variational path synthesis approach for linkage mechanisms. An image-based approach is particularly amenable to mechanism synthesis when the input from...

... (FDM). The predictive model of flexural strength is trained using machine learning and validated on experimental data. The relationship between three structural design factors, including the number of fiber layers, the number of fiber rings as well as polymer infill patterns, and the flexural strength...

...Bin He; Xuanren Zhu; Dong Zhang 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, the accuracy of the classical Monte Carlo...

... 08 2019 31 01 2020 03 02 2020 14 02 2020 natural language processing machine learning cloud manufacturing artificial intelligence knowledge engineering machine learning for engineering applications Cloud manufacturing has been regarded as a disruptive manufacturing...

... network machine learning artificial intelligence computational geometry computer-aided manufacturing engineering informatics machine learning for engineering applications multiscale modeling and simulation Micro and nano manufacturing technologies are widely recognized as promising...

... images using first-order and second-order statistical methods, and measured 3D surface roughness parameters are used for characterizing the SLM surfaces. A comparative study of roughness prediction models developed using various machine learning approaches is also presented. Among the models...

.... 785 – 794 . [15] Probst , P. , Bischl , B. , and Boulesteix , A.-L. , 2019 , “ Tunability: Importance of Hyperparameters of Machine Learning Algorithms ,” J. Mach. Learn. Res. , 20 ( 53 ), pp. 1 – 32 . [16] International , A. , 2015 , ASTM D638-14, Standard Test...

... constraints of formulating the problem as a discrete precision position problem and limitations of the methods, which ignore the continuity information in the input. In this paper, we bring together diverse fields of pattern recognition, machine learning, artificial neural network, and computational...