...Omey M. Manyar; Junyan Cheng; Reuben Levine; Vihan Krishnan; Jernej Barbič; Satyandra K. Gupta Deep learning-based image segmentation methods have showcased tremendous potential in defect detection applications for several manufacturing processes. Currently, majority of deep learning research...

... ferrograph (OLVF) apparatus is used to obtain online measurements of wear particle quantities, and monitor the wearing of a four-ball tribometer under different lubrication conditions, and several popular deep learning algorithms are evaluated for their effectiveness in providing maintenance decisions...

... , “ Network Decoupling: From Regular to Depthwise Separable Convolutions ,” 29th British Machine Vision Conference (BMVC 2018) , Newcastle, UK , Sept. 2–6 , https://arxiv.org/abs/1808.05517 [18] Chollet , F. , 2016 , “ Xception: Deep Learning With Depthwise Separable Convolutions ,” CoRR abs...

... a new solution. Compared with traditional machine learning models, deep learning models possess more powerful nonlinear expression capabilities and feature extraction capabilities. Therefore, this study focuses on studying the RUL prediction algorithm for turbofan engines based on the fused deep...

... in the remaining useful life (RUL) estimation of critical industrial systems. In this paper, long short-term memory (LSTM) and bidirectional-LSTM (bi-LSTM) deep neural architecture-based predictive algorithms are proposed for the RUL estimation of the lathe spindle unit. The deep learning algorithm is embedded...

... a specific class of problems. However, these techniques do not handle noise, variations in lighting conditions, and backgrounds with complex textures. In recent times, deep learning has been widely explored for use in automation of defect detection. This survey article presents three different ways...

... of the advantages of mechanism synthesis domain over other domains such as the domain of natural images or natural language processing is the availability of clean synthetic data. We exploit this advantage to create a carefully tuned dataset to improve the prediction performance of the deep learning models. One can...

... supervised machine learning deep learning container routing automated labeling applied artificial intelligence artificial intelligence computer-aided manufacturing engineering informatics machine learning for engineering applications manufacturing automation model-based systems engineering...

... to handle the features extracted from the vibration signal because of the large data quantity, complex feature relations, and limited degeneration mechanisms. In this paper, a deep learning-based approach is proposed to predict the failure of the complex equipment. To supply plenty of features, three...

... prediction errors than SCSNet on three loss functions, with a mean relative error of 2.04% for testing. These results suggest that deep learning models may offer a promising alternative to classical methods in structural design and topology optimization. Code and dataset are available. 2 References [1...