Automatic Fuzzy Architecture Design for Defect Detection via Classifier-Assisted Multiobjective Optimization Approach

Defect recognition is an essential aspect of intelligent manufacturing, but it is a challenging task with noise and unpredictable uncertainties, where convolutional neural networks (CNNs) struggle to achieve good performance. The fuzzy neural network (FNN) emerges as a promising approach to handle uncertainties. However, conventional methods for designing FNNs are tedious and error-prone. A solution is to automatically search for efficient FNN, which can be achieved by neural architecture search (NAS). To achieve NAS for FNN, we propose an efficient classifier-assisted evolutionary multiobjective FNN framework for defect recognition. Considering the characteristics of FNN (e.g., difficult to train and prone to overfitting), we first construct the architecture search as a constrained multiobjective optimization problem, the network accuracy and the architecture size are two conflicting objectives, and the constraint is used to filter out low-quality architectures. Then, we design the search space to incorporate the fuzzy module and develop the corresponding architectural representation and evolutionary operators. Furthermore, the complex regression task of performance evaluation is transformed into a classification task, and a classifier is designed to simplify the performance evaluation process. Massive experiments on four defect recognition datasets (i.e., ELPV, CODEBRIM, MIXEDWM38, and WM-811K) show that the architectures can effectively handle inherent uncertainties from datasets. Our method achieves 94.77% accuracy on ELPV, 81.82% accuracy on CODEBRIM, 98.99% accuracy on MIXEDWM38, and 98.22% accuracy on WM-811K, respectively.