Load magnitude and location estimation on additively manufactured circular structures using deep learning - Romaine Byfield; Ibrahim Tansel (Next Materials)
Additively Manufactured (AM) metal parts have been used at critical engineering applications. Estimation of load and its location is important to avoid accidents. Since the excitation signals move through multiple paths, estimation of load and its location is difficult. In this study, surface response to excitation method (SuRE) was implemented with multiple steps by considering the challenges of the geometry. Two 3D printed stainless steel rocket nozzle type structures with two different sizes and a C shaped tube section were used. Two different PZT placements were used for the big nozzle while the PZTs were located at the opposite sides of the small nozzle. The large nozzle had 280 test cases for 9 categories, while the smaller nozzle had 60 test cases for 5 categories. Finally, the C shaped specimen had 48 test cases for three categories. Continuous wavelet transform (CWT) was used to obtain more representative presentation of the data to the deep learning algorithm. Convolutional Neural Networks (CNNs) were used for classification. Wavelet transformation – Deep learning combination yielded 100 % accuracy for the large nozzle and 97.14 % for the smaller nozzle. These findings demonstrate the effectiveness of this technique on curved surfaces representative of real-world parts. The proposed method estimated the load location with 100 % accuracy.