(Georgia Institute of Technology, 2019-03-25)
Louhichi, Wafa
The surface finish of a machined part has an important effect on friction, wear, and aesthetics. The surface finish became a critical quality measure since 1980s mainly due to demands from automotive industry. Visual inspection and quality control have been traditionally done by human experts. Normally, it takes a substantial amount of operators time to stop the process and compare the quality of the produced piece with a surface roughness gauge. This manual process does not guarantee a consistent quality of the surface and is subject to human error and dependent upon the subjective opinion of the expert. Current advances in image processing, computer vision, and machine learning have created a path towards an automated surface finish inspection increasing the automation level of the whole process even further than it is now. In this thesis work, we propose a deep learning approach to replicate human judgment without using a surface roughness gauge. We used a Convolutional Neural Network (CNN) to train a surface finish classifier. Because of data scarcity, we generated our own image dataset of aluminum pieces produced from turning and boring operations on a Computer Numerical Control (CNC) lathe, which consists of a total of 980 training images, 160 validation images, and 140 test images. Considering the limited dataset and the computational cost of training deep neural networks from scratch, we applied transfer learning technique to models pre-trained on the publicly available ImageNet benchmark dataset. We used PyTorch Deep Learning framework and both CPU and GPU to train ResNet18 CNN. The training on CPU took 1h21min55s with a test accuracy of 97.14% while the training on GPU took 1min47s with a test accuracy = 97.86%. We used Keras API that runs on top of TensorFlow to train a MobileNet model. The training using Colaboratory’s GPU took 1h32m14s with an accuracy of 98.57%. The deep CNN models provided surprisingly high accuracy missclassifying only a few of 140 testing images. The MobileNet model allowed to run the inference efficiently on mobile devices. The affordable and easy-to-use solution provides a viable new method of automated surface inspection systems (ASIS).
(Georgia Institute of Technology, 2016-04-28)
Henneking, Stefan
In a parallel vertex-centered finite element multigrid solver, segmental refinement can be used to avoid all inter-process communication on the fine grids. While domain decomposition methods generally require coupled subdomain processing for the numerical solution to a nonlinear elliptic boundary value problem, segmental refinement exploits that subdomains are almost decoupled with respect to high-frequency error components. This allows to perform multigrid with fully decoupled subdomains on the fine grids, which was proposed as a sequential low-storage algorithm by Brandt in the 1970s, and as a parallel algorithm by Brandt and Diskin in 1994. Adams published the first numerical results from a multilevel segmental refinement solver in 2014, confirming the asymptotic exactness of the scheme for a cell-centered finite volume implementation. We continue Brandt’s and Adams’ research by experimentally investigating the scheme’s accuracy with a vertex-centered finite element segmental refinement solver. We confirm that full multigrid accuracy can be preserved for a few segmental refinement levels, although we observe a different dependency on the segmental refinement parameter space. We show that various strategies for the grid transfers between the finest conventional multigrid level and the segmental refinement subdomains affect the solver accuracy. Scaling results are reported for a Cray XC30 with up to 4096 cores.