Due to its advantages of high customization and rapid production, metal laser melting manufacturing (MAM) has been widely applied in the medical industry, manufacturing, aerospace and boutique industries in recent years. However, defects during the selective laser melting (SLM) manufacturing process can result from thermal stress or hardware failure during the selective laser melting (SLM) manufacturing process. To improve the product’s quality, the use of defect detection during manufacturing is necessary. This study uses the process images recorded by powder bed fusion equipment to develop a detection method, which is based on the convolutional neural network. This uses three powder-spreading defect types: powder uneven, powder uncovered and recoater scratches. This study uses a two-stage convolutional neural network (CNN) model to finish the detection and segmentation of defects. The first stage uses the EfficientNet B7 to classify the images with/without defects, and then to locate the defects by evaluating three different instance segmentation networks in second stage. Experimental results show that the accuracy and Dice measurement of Mask-R-CNN network with ResNet 152 backbone can reach 0.9272 and 0.9438. The computational time of an image only takes approximately 0.2197 sec. The used CNN model meets the requirements of the early detected defects, regarding the SLM manufacturing process.
Ball bearings are widely used in rotating components and definitely influence the operation quality of machines.And their faults are one of the main reasons that make machines break down and this problem should be investigated. Thus, this study develops an effective and flexible diagnosis method for ball bearings. By setting up a rotor bearing-experiment platform and pre-designing failures on bearings or rotating shaft to simulate the service of bearings. Sensors hired are two accelerometers, a microphone, an acoustic emission detector, and a thermal couple. Various statistical methods are used for data reduction and to extract features. Through systematic analysis, it is possible to find the most sensitive features. Those indexes are then fed into autoencorder, which is an unsupervised machine learning scheme, for training the collected data to predict the possible bearing failure type and status. For ease of visualization, the results are mapped into a three-dimensional space for examining the performance in failure diagnosis. The investigation results show that the hired machine learning method performs well with appropriate feature indexes. Finally, specific diagnosis models are created for each corresponding bearing failure condition and a novel whole diagnosis process is proposed to integrate all these models for counting possible multiple causes of failure. This proposed diagnosis flow should be able to significantly improve the prediction accuracy on the reliabilities of rotating machines and could be promoted to other related applications.
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