Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Huang, Chih-Yung; Dzulfikri, Zaky

doi:10.3390/s21010262

Cited by 16 publications

(8 citation statements)

References 20 publications

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“…Huang and Dzulfikri transformed acceleration signals into the frequency domain and used the entire power density spectrum as input for a one dimensional CNN. They demonstrated that they can distinguish seven different wear states with more than 99% accuracy [39]. Unterberg et al recorded magnetic barkhausen noise from different material coils, created recurrence plots of the time series and used them as inputs for a two dimensional CNN.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Molitor

Kubik

Hetfleisch

et al. 2022

Prod. Eng. Res. Devel.

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Blanking processes belong to the most widely used manufacturing techniques due to their economic efficiency. Their economic viability depends to a large extent on the resulting product quality and the associated customer satisfaction as well as on possible downtimes. In particular, the occurrence of increased tool wear reduces the product quality and leads to downtimes, which is why considerable research has been carried out in recent years with regard to wear detection. While processes have widely been monitored based on force and acceleration signals, a new approach is pursued in this paper. Blanked workpieces manufactured by punches with 16 different wear states are photographed and then used as inputs for Deep Convolutional Neural Networks to classify wear states. The results show that wear states can be predicted with surprisingly high accuracy, opening up new possibilities and research opportunities for tool wear monitoring of blanking processes.

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Section: Deep Learning Approachesmentioning

confidence: 99%

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Molitor

Kubik

Hetfleisch

et al. 2022

Prod. Eng. Res. Devel.

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“…The data set used in the current study was extensively used in our previous study [29]. It contains progressive stamping die vibration signals acquired using an accelerometer with a sampling rate of 25.6 kHz and an axis parallel to the stroke direction of the stamping machine.…”

Section: Datasetmentioning

confidence: 99%

“…CNNs, which employ neural nodes as a type of filter, enable deep learning structures to extract features from complex and highly nonlinear signals. In our previous work [29], a CNN was used to evaluate the condition of a stamping tool, achieving favorable results; however, several problems remain to be resolved. First, to add a new class, the model must be retrained, which requires a high computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…Herein, we propose a solution for stamping tool condition diagnosis that offers improvements over the method introduced in our previous work [29], the main improvement that we aim to achieve is the flexibility of adding new class while still retaining the advantages gained from the previous work. The solution is based on the incorporation of DML with several distance metric loss and batch strategies.…”

Section: Introductionmentioning

confidence: 99%

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Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach

Dzulfikri

Huang

2021

Applied Sciences

Self Cite

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Stamping processes remain crucial in manufacturing processes; therefore, diagnosing the condition of stamping tools is critical. One of the challenges in diagnosing stamping tool conditions is that traditionally, the tools need to be visually checked, and the production processes thus need to be halted. With the development of Industry 4.0, intelligent monitoring systems have been developed by using accelerometers and algorithms to diagnose the wear classification of stamping tools. Although several deep learning models such as the convolutional neural network (CNN), auto encoder (AE), and recurrent neural network (RNN) models have demonstrated promising results for classifying complex signals including accelerometer signals, the practicality of those methods are restricted due to the flexibility of adding new classes and low accuracy when faced to low numbers of samples per class. In this study, we applied deep metric learning (DML) methods to overcome these problems. DML involves extracting meaningful features using feature extraction modules to map inputs into embedding features. We compared the probability method, the contrastive method, and a triplet network to determine which method was most suitable for our case. The experimental results revealed that, compared with other models, a triplet network can be more effectively trained with limited training data. The triplet network demonstrated the best test results of the compared methods in the noised test data. Finally, when tested using unseen class, the triplet network and the probability method demonstrated similar results.

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“…The proposed method has less energy consumption which is suitable to be integrated in embedded system. Therefore, the methodology has been investigated in bearing, motor running noise and stamping quality monitoring areas (Wang et al , 2020; Huang and Dzulfikri, 2021; Ince, 2019; Zhang et al , 2017). Furthermore, residual learning Der-1DCNN model was studied.…”

Section: Introductionmentioning

confidence: 99%

Press-fit process fault diagnosis using 1DCNN-LSTM method

2022

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Purpose Press-fit with force and displacement monitoring is commonly adopted in automotive mechatronic system assembling. However, suitable methods for the press-fit study are still at initial investigation phase. The sequential data physical meaning, small data sets from different resources and computing efficiency should be considered. Therefore, this paper aims to better identify press-fit fault types. Design/methodology/approach This paper proposed one-dimensional convolutional neural network (1DCNN)–long short-term memory (LSTM) method to perform press-fit fault diagnosis into automotive assembling practice which is in accordance with current product development procedure. Specialized data augmentation method is proposed to merge different data resources and increase the sample size. Referring one-way sequential data characteristics, LSTM and batch normalization layers are integrated in 1DCNN to improve the performance. Findings The proposed 1DCNN-LSTM method is feasible with small data sets from different sources. Using data augmentation to make data unified and sample size increased, the accuracy could reach more than 99%. Training time has reduced from 90 s/Epoch to 4 s/Epoch compare to pure LSTM method. Originality/value The proposed method shows better performance with less training time compared to LSTM. Therefore, the method has practical value and is worthy of industrial application.

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Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Cited by 16 publications

References 20 publications

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach

Press-fit process fault diagnosis using 1DCNN-LSTM method

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