Abstract
The diagnosis of product defects is an important task in manufacturing, and machine learning-based approaches have attracted interest from both the industry and academia. A high-quality dataset is necessary to develop a machine learning model, but the manufacturing industry faces several data-collection issues including partially shuffled data, which arises when a product ID is not perfectly inferred and yields an unstable machine learning model. This paper introduces latent variables to formulate a supervised learning model that addresses the problem of partially shuffled data. The experimental results show that our graphical model deals with the shuffling of product order and can detect a defective product far more effectively than a model that ignores shuffling.
Highlights
The diagnosis of product defects is essential for improving productivity in manufacturing
Data-driven involves the development defect diagnosis of a classifier f by using a training dataset that consists of x(i), y(i) for i = 1, 2, · · ·, n, where x(i) is a feature vector and y(i) is the label of product i, as well as using the classifier to diagnose product defects
We propose a graphical model to diagnose product defects with partially shuffled equipment data
Summary
The diagnosis of product defects is essential for improving productivity in manufacturing. Multi-source data fusion that combines or merges data from multiple sources (e.g., multiple sensors and two or more machines) is necessary for realistic defect diagnosis with equipment data [5]. It can Processes 2019, 7, 934; doi:10.3390/pr7120934 www.mdpi.com/journal/processes. We propose a graphical model to diagnose product defects with partially shuffled equipment data. It proposes a graphical model to diagnose product defects with partially shuffled equipment data with a consideration of product shuffling based on Q j,r.
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