In many manufacturing processes, equipment reliability plays a crucial role for product quality assurance. It is important to consider the effect of equipment degradation for the quality-process model. In this article, we propose a dynamic quality-process model to characterize the varying effects of a process to product quality due to equipment degradation. The proposed model considers the effects of process variables on product quality as piecewise linear functions with respect to the equipment degradation. It can automatically estimate the dynamic effects via a meaningful parameter regularization, leading to accurate parameter estimation and model prediction. The merits of the proposed method are illustrated by both simulations and a real case study in a crystal growth manufacturing process.

For the single crystal furnace used in the photovoltaic industry, growth problems occur frequently due to dislocations during the shouldering and cylindrical growth steps of the Czochralski (CZ) crystal growth. Detecting the dislocation phenomenon in the cylindrical growth step is very important for entire automation of the CZ crystal furnace, since this process usually lasts for more than 48h. The irregular nature of different patterns of dislocation would impose a big challenge for a traditional machine vision-based detection method. As almost no publications have been dedicated to detecting this phenomenon, to address this issue, after analyzing the characteristics of the silicon ingot image of this process, this paper proposes a kind of deep learning-based dislocation detection method along with tracking strategy to simulate manual inspection. The model has a good detection effect whether there is occlusion or not, the experimental results show that the detection accuracy is 97.33%, and the inference speed is about 14.7 frames per second (FPS). It can achieve the purpose of reducing energy consumption and improving process automation by monitoring this process.