For semiconductor manufacturing, automatic optical inspections (AOIs) are important for chip quality inspection. An AOI system contains a robot arm, an industrial camera, a x-y platform, and a visual inspection module. Using the industrial camera, a wafer map can be obtained and then sent to the visual inspection module to compare with qualified chip features. There is a baseline in the x-y platform. Due to the limitations of the robot arm flexibility, it is difficult for the robot arm to control the angles between the chip orientation and the baseline every time, which decreases the defect recognition accuracy. This work aims to improve the defect recognition accuracy and efficiency of the AOI system. Specifically, an efficient method is presented to calculate the angle between the baseline and chip orientation. Then, the wafer map can be rotated, such that the angle equals to zero. Further, a powerful system is established to recode the rotated chip coordinate, such that the unqualified chip positions can be located efficiently. This method is called a central array method. The central array method with deep learning methods forms an AI-based AOI system. Extensive experiments demonstrate that our proposed method performs well in improving the chip quality inspection efficiency and accuracy. Nevertheless, the proposed method still has challenges in implementation since it requires integration with the manufacturing line.

To address the challenge of poor separation performance exhibited by conventional magnetic separation equipment when processing coarse-grained, low-grade magnetite ore, this paper proposes a novel ore recognition method that integrates empirical mode decomposition (EMD) with a convolutional neural network (CNN). First, the original signal undergoes standardization to suppress sensor baseline drift. Then, it is decomposed by using EMD to obtain a series of intrinsic mode functions (IMFs). Subsequently, based on scaling exponents and kurtosis values, IMFs containing significant feature information are selected and fused, resulting in a reconstructed signal with substantially reduced noise. To preserve effective features, the absolute values of the reconstructed signal are taken, followed by normalization and dimensional transformation to convert it into a two-dimensional matrix format, thereby constructing training, validation, and test sets. Finally, a CNN is designed and optimized to automatically extract discriminative features from the preprocessed samples, enabling accurate classification of magnetite ore grades. Experimental results demonstrate that the proposed comprehensive identification method achieves effective and stable classification performance across different ore grades. Specifically, the implementation of standardization and EMD-based denoising has been demonstrated to enhance the accuracy of CNNs in recognizing diverse ores.

In the manufacturing of iron core for high-power transformers, a cutting stock problem arises where large-width silicon steel coils must be cut into narrower coils, known as strips. Typically, the required length of each strip far exceeds that of a single coil. Therefore, the problem necessitates additional consideration of how to split the strips and arrange them on the large coils, with the goal of minimizing the total number of strips. In this paper, we propose a discrete artificial bee colony algorithm to address this problem. The algorithm replaces the stochastic roulette wheel with biased selection in the onlooker bee phase and introduces partially mapped crossover in both the onlooker and scout bee phases. These enhancements facilitate more effective utilization of information from high-quality solutions, thereby improving the algorithm’s stability and its capacity to obtain higher-quality results. Experimental results show that compared to existing methods reported in the literature, the proposed approach reduces the total number of strips by an average of over 3.9% and 7.6% for Set 2 and Set 3, respectively, while also exhibiting a faster convergence rate than other competitive algorithms.

To address the challenge of poor separation performance exhibited by conventional magnetic separation equipment when processing coarse-grained, low-grade magnetite ore, this paper proposes a novel ore recognition method that integrates empirical mode decomposition (EMD) with a convolutional neural network (CNN). First, the normalized magnetic induction intensity signals are decomposed using EMD to yield a series of intrinsic mode functions (IMFs). IMFs containing prominent characteristic information are then selected and fused based on their dominant frequency and kurtosis values, resulting in a reconstructed signal with significantly reduced noise. Subsequently, the reconstructed signals undergo inversion, re-normalization, and dimensional transformation into a two-dimensional matrix format to construct the training and testing sample datasets. A convolutional neural network is then designed and optimized to automatically extract discriminative features from these preprocessed samples, enabling accurate classification of magnetite ore grades. Experimental results demonstrate that the proposed EMD-CNN framework achieves effective and stable classification performance across different ore grades. In particular, the application of EMD for noise component removal substantially enhances the CNN’s recognition accuracy for waste rock and medium-grade ore, which are traditionally the most difficult categories to distinguish.