Convolutional neural networks for engineering design validation

The growth of artificial intelligence has led to the widespread use of convolutional neural networks (CNNs) for computer vision applications, traditionally for binary and categorical classification tasks. However, there remains untapped potential for advancing computer vision through deep learning in regression tasks. Design engineers across many disciplines use computer-aided design software to model their designs. These computer-integrated designs often require machinery for construction or fabrication. For many engineering designs, precision and tolerancing is essential for the proper function and performance of the design. The engineering process typically involves manual testing and parameter measurements to ensure the proper function of the design before it is marketed. However, training a neural network to automate these tests and provide accurate numeric estimates of system parameters without manual intervention can significantly increase efficiency and decrease the time to market for many products. This shift from manual to automated testing allows for a heightened focus on innovation and project development while minimizing the time and resource dedication for validation. This article outlines the implementation of CNN models designed to enhance the efficiency of manually validating engineered projects. Our approach involves utilizing computer-aided design simulation image captures as training data for our pipeline. We integrate a real-time color-filtering and fiducial rotation scaling normalization process on any fabricated design image. Through these pre-processing methods, our algorithm can perceive these images in a consistent manner with simulation images from the model training. <br/> <br/> Our current model is trained with only 1020 simulation images and achieves a 1.99% average training prediction error on this dataset after training. Before, our errors were a 10.51% average error in our initial model implementation and 3.63% in our second implementation. On our test set, consisting of six captured and preprocessed sample fabricated design images, our model achieves a 3.40% average prediction error. The performance of a regression label neural network of this nature depends largely on the amount and range of data and simulation scenarios considered. As we continue to expand our training dataset through an optimized pipeline, we anticipate a significant improvement in model performance.