PREDICTION MODEL BASED MULTI-PROFILE MONITORING FOR MANUFACTURING PROCESS MANAGEMENT

In an advanced manufacturing environment, the analysis of profile data collected from the process equipment is a critical issue in improving process efficiency. In particular, multi-profile monitoring is essential for process control because an advanced manufacturing process consists of numerous pieces of equipment and their related sensors. The main goal of this study is to build a monitoring chart using a Profile Integrated Measure (PIM) from multi-profile data in order to observe an overall condition of various points in the process. To deploy the proposed algorithm, multi-profile data needed to be preprocessed and applied to the prediction model. The PIM is calculated from the prediction model and reflects the relationships between the multi-profile data property, which has normal/abnormal states. The proposed algorithm constructs a model using the PIM of a normal state and identifies the performance of the model. Experiments with the simulation datasets modified from the manufacturing process validate the effectiveness and applicability of the proposed algorithm

Feature-Learning-Based Printed Circuit Board Inspection via Speeded-Up Robust Features and Random Forest

With the coming of the 4th industrial revolution era, manufacturers produce high-tech products. As the production process is refined, inspection technologies become more important. Specifically, the inspection of a printed circuit board (PCB), which is an indispensable part of electronic products, is an essential step to improve the quality of the process and yield. Image processing techniques are utilized for inspection, but there are limitations because the backgrounds of images are different and the kinds of defects increase. In order to overcome these limitations, methods based on machine learning have been used recently. These methods can inspect without a normal image by learning fault patterns. Therefore, this paper proposes a method can detect various types of defects using machine learning. The proposed method first extracts features through speeded-up robust features (SURF), then learns the fault pattern and calculates probabilities. After that, we generate a weighted kernel density estimation (WKDE) map weighted by the probabilities to consider the density of the features. Because the probability of the WKDE map can detect an area where the defects are concentrated, it improves the performance of the inspection. To verify the proposed method, we apply the method to PCB images and confirm the performance of the method