In electronic manufacturing, anomaly detection of surface mount devices (SMDs) through computer vision (CV) is an important task to control the production quality of SMDs. The difficulty of the detection is that some anomalous regions on the surfaces of SMDs are very minor and with variable shapes, which leads to poor detection efficiency. To solve this problem, based on the assumption that normal samples can be reconstructed more accurately than anomalous samples, a self-supervised image anomaly detection framework with a multiscale two-branch feature fusion strategy is proposed. Specifically, it adopts autoencoder (AE) as the basic framework, and to enhance the reconstruction error between input anomalous samples and the reconstructed ones, a self-supervised learning task of reconstructing images is introduced to have the model neglect the encoding of the suspected anomalous regions found by a contextual attention mask (CAM) module. Meanwhile, a multiscale feature fusion strategy is developed to fuse texture and structure features in the decoder to reconstruct samples. Moreover, a multilevel anomalous score criterion is proposed to enlarge the scores for the samples with very minor anomalies. At last, an SMD-capacitor anomaly detection dataset (SMDC-DET) is built to evaluate the proposed method. The experiments show that the proposed method achieves an average area under the curve (AUC) accuracy of 98.82%, much better when compared to the start-of-the-art existing anomaly detection methods.

This paper studies the coordination of a heterogenous flywheel energy storage matrix system aiming at simultaneous reference power tracking and state-of-energy balancing. It is first revealed that this problem is solvable if and only if the state-of-energy of all the flywheel systems synchronize to a common time-varying manifold governed by a nonautonomous dynamic system. Next, by treating this nonautonomous dynamic system as an external model, the coordination problem can be decoupled into two separate problems, namely, the global double layer estimation problem and the local tracking problem. Then, a distributed control scheme is proposed to solve the coordination problem by integrating the adaptive distributed observer approach and the certainty equivalence control method. Comprehensive case studies are provided to show the performance of the proposed control scheme.

This paper analyzes optimization algorithms of assembly time for a multi-head mounter. A step-by-step heuristic optimization algorithm is proposed to minimize the PCB assembly time for a multi-head mounter. By relaxing the restrictions on the problem and employing a flexible approach for feeder and head assignment, the algorithm reduces the assembly time by minimizing cycles of pick and placement, constructing the simultaneous pickup and implementing the nearest placement. The production results show that the proposed algorithm has good performance in practice.

In this paper, we discuss the process of low-volume and high-mix PCB assembly production in the electronic assembly industry and aim at an effective scheduling method to determine assembly sequence of different types of PCBs to reducing whole production time. The optimization problem can be modeled as an approximate traveling salesman problem (TSP) when considering the effect of machine setup time on whole production time. An optimization algorithm based on scatter search method is proposed to search for good solutions to minimize machine setup time. The simulation results show that the proposed algorithm can output good scheduling solutions.