While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.

Float-Zone (FZ) crystal growth process allows for producing higher purity silicon crystal with much lower concentrations of impurities, in particular low oxygen content. Nevertheless, the FZ process occasionally faces the problem of small contamination from oxidation. This can come in the form of a thin oxide layer that may form on un-melted polysilicon surface. The appearance of the oxide layer indicates degraded machine performance and the need for machine maintenance. Therefore, oxide investigation is important for improving both the FZ process and FZ machines, and the first step is oxide recognition. In this study, we characterized oxide into mainly three varieties, according to their surface texture characteristics, which are: (i) spot (ii) shadow and (iii) ghost curtain. We leveraged FZ images captured from the vision system integrated on the FZ machine to establish an oxide dataset. Targeted for data imbalance problem in our dataset, a method based on transfer learning and asymmetric loss for multi-label oxide classification is presented in this work. The results showed that the pre-trained model and the asymmetric loss used for training outperformed the baseline models and improved the classification performance. Furthermore, this study deeply investigated the effectiveness of the components of asymmetric loss. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to explain decision process of the models in order to adopt them in the industry.