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• W4367183669 abstract "Abstract CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. However, to maintain these standards, it is essential to monitor the condition of these machines during production. Misalignment of the linear tables in CNCs can directly affect the quality of the manufactured parts, and the components of the linear tables wear out over time due to the heavy and fluctuating loads. To address these challenges, an intelligent monitoring system was developed to identify normal operation and misalignments. Since damaging a CNC machine for data collection is too expensive, transfer learning was used in two steps. First, a specially designed test linear table (SDTLT) was used to sample the current signal at normal and five levels of left-side misalignment conditions ranging from 0.05 mm to 0.25 mm Four different algorithm combinations were trained to detect misalignments. These combinations included a 1D convolution neural network (CNN) and Autoencoder combination, a Temporal Convolutional Network (TCN) and AE combination, a long short-term memory neural network (LSTM) and AE combination, and a CNN, LSTM, and AE combination. At the second step, Wasserstein Deep Convolutional Generative Adversarial Network (W-DCGAN) was used to generate data by integrating the observed characteristics of the SDTLT at different misalignment levels and collected limited data from the actual CNC machines. To evaluate the similarity and limited diversity of generated and real signals, t-distributed stochastic neighbor embedding (T-SNE) method was used. The hyperparameters of the model were optimized by random and grid search. The CNN, LSTM, AE combination demonstrated the best performance, which provides a practical way to detect misalignments without stopping production or cluttering the work area with sensors. The proposed intelligent monitoring system can detect misalignments of the linear tables of CNCs, thus enhancing the quality of manufactured parts and reducing production costs." @default.
• W4367183669 created "2023-04-28" @default.
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• W4367183669 date "2023-04-27" @default.
• W4367183669 modified "2023-10-16" @default.
• W4367183669 title "Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data" @default.
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• W4367183669 doi "https://doi.org/10.21203/rs.3.rs-2823164/v1" @default.
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