Defect classification of glass substrate using deep neuro-fuzzy network with optimal parameter combination
Tóm tắt
Currently, numerous smart products are based on glass substrates. However, defects that occur during the production of glass substrates affect the quality and safety of the final products. Accordingly, we developed an optimal-parameter-combination-based deep neuro-fuzzy network (O-DNFN) for classifying defects in glass substrate images. The proposed O-DNFN comprises a deep neuro-fuzzy network (DNFN) and uses the Taguchi method. The fusion layer of the DNFN uses four feature fusion methods. The neuro-fuzzy network in the DNFN serves as replacement to a fully connected network for the classification of defects in glass substrate images. Because O-DNFN model parameter selection is challenging, we used the Taguchi method to determine the optimal parameter combination through fewer experiments. The experimental results revealed that the accuracy rates of the proposed O-DNFN with global max pooling fusion and an LeNet model in classifying defects in glass substrate images were 91.8% and 88%, respectively.
Tài liệu tham khảo
Abbasi N, Khan MF, Khan E, Alruzaiqi A, Al-Hmouz R (2022) Fuzzy histogram equalization of hazy images: a concept using a type-2-guided type-1 fuzzy membership function. Granul Comput. https://doi.org/10.1007/s41066-022-00351-0
Antony J, Antony FJ (2001) Teaching the Taguchi method to industrial engineers. Work Study 50(4):141–149. https://doi.org/10.1108/00438020110391873
Dhar P, Dutta S, Mukherjee V (2021) Cross-wavelet assisted convolution neural network (AlexNet) approach for phonocardiogram signals classification. Biomed Signal Process Control 63:102142. https://doi.org/10.1016/j.bspc.2020.102142
Huang H, Zhan Q, Xie X, Ye D, Liu G (2021) Glass defect detection via multi-scale feature fusion. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/2216/1/012099
Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Mark 31:685–695. https://doi.org/10.1007/s12525-021-00475-2
Jiang J, Cheng J, Tao D (2012) Color biological features-based solder paste defects detection and classification on printed circuit boards. IEEE Trans Compon Packag Manuf Technol 2(9):1536–1544. https://doi.org/10.1109/TCPMT.2012.2205149
Jiang J, Cao P, Lu Z, Lou W, Yang Y (2020) Surface defect detection for mobile phone back glass based on symmetric convolutional neural network deep learning. Appl Sci 10:3621. https://doi.org/10.3390/app10103621
Li J, Jhang JY, Lin CJ, Lin XQ (2022) Arrhythmia detection using a Taguchi-based convolutional neuro-fuzzy network. Sens Mater 34(7):2853–2867. https://doi.org/10.18494/SAM3924
Lin M, Chen Q, Yan S (2013) Network in network. https://doi.org/10.48550/arXiv.1312.4400
Lin HY, Lin CJ (2022) Neural fuzzy inference network based on differential evolution for nonlinear system control. Sens Mater 34(1):105–120
Lu RS, Shi YQ, Li Q, Yu QP (2010) AOI techniques for surface defect inspection. Appl Mech Mater 36:297–302. https://doi.org/10.4028/www.scientific.net/AMM.36.297
Ma Z, Chang D, Xie J, Ding Y, Wen S, Li X, Guo J (2019) Fine-grained vehicle classification with channel max pooling modified CNNs. IEEE Trans Veh Technol 68(4):3224–3233. https://doi.org/10.1109/TVT.2019.2899972
Mustaqeem M, Saqib M (2021) Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection. Cluster Comput 24:2581–2595. https://doi.org/10.1007/s10586-021-03282-8
Reshadat V, Kapteijns RA (2021) Improving the performance of automated optical Inspection (AOI) using machine learning classifiers. In: 2021 International Conference on Data and Software Engineering (ICoDSE), pp 1–5
Shirmohammadi S, Ferrero A (2014) Camera as the instrument: the rising trend of vision based measurement. IEEE Trans Instrum Meas 17(3):41–47. https://doi.org/10.1109/MIM.2014.6825388
Thielen N, Werner D, Schmidt K, Seidel R, Reinhardt A, Franke J (2020) A machine learning based approach to detect false calls in SMT manufacturing. In: 2020 43rd International Spring Seminar on Electronics Technology (ISSE), pp 1–6
Wang KJ, Hao FJ, Lee YX (2022a) A multiple-stage defect detection model by convolutional neural network. Comput Ind Eng 168:108096. https://doi.org/10.1016/j.cie.2022.108096
Wang X, Yang J, Lu W (2022) Bearing fault diagnosis algorithm based on granular computing. Granul Comput. https://doi.org/10.1007/s41066-022-00328-z
Yang WH, Tang YS (1998) Design optimization of cutting parameters for turning operations based on Taguchi method. J Mater Process Technol 84:122–129. https://doi.org/10.1016/S0924-0136(98)00079-X
Yang X, Han M, Tang H, Li Q, Luo X (2020) Detecting defects with support vector machine in logistics packaging boxes for edge computing. IEEE Access 8:64002–64010. https://doi.org/10.1109/ACCESS.2020.2984539
Zhang DD, Jin Y, Hu BY (2019) Glass defect recognition method based on improved convolutional neural networks. J Comput 30:168–180. https://doi.org/10.3966/199115992019123006013
Zhao H, Liu H (2020) Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition. Granul Comput 5:411–418. https://doi.org/10.1007/s41066-019-00158-6