Cascade Quality Prediction Method Using Multiple PCA+ID3 for Multi-Stage Manufacturing System

AbstractQuality prediction model, as the key to realize the real-time online quality monitoring process, has been developed using various data mining techniques. However, most of quality prediction models are developed in single-stage manufacturing system, where the relationship between manufacturing operation and quality variables is straightforward. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing system due to the complex variable relationships. This study is intended to propose a data mining method to develop quality prediction model which is able to deal with the complex variable relationships in multi-stage manufacturing system. This method, named Cascade Quality Prediction Method (CQPM), is developed by considering the complex variables relationships in multi-stage manufacturing system. CQPM employs the combination of multiple Principal Component Analysis and Iterative Dichotomiser 3 algorithm. A case study in semiconductor manufacturing shows that the prediction model that has been developed using CQPM is performed better in predicting both positive and negative classes compared to others

MACHINE LEARNING CLASSIFICATION MODELS FOR DETECTION OF THE FRACTURE LOCATION IN DISSIMILAR FRICTION STIR WELDED JOINT

Data analysis is divided into two categories i.e. classification and prediction. These two categories can be used for extraction of models from the dataset and further determine future data trends or important set of classes available in the dataset. The aim of the present work is to determine location of the fracture failure in dissimilar friction stir welded joint by using various machine learning classification models such as Decision Tree, Support Vector Machine (SVM), Random Forest, Naïve Bayes and Artificial Neural Network (ANN). It is observed that out of these classification algorithms, Artificial Neural Network results have the best accuracy score o

An anomaly detection approach to identify chronic brain infarcts on MRI

The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how 'normal' tissue looks like. In this work, we propose an anomaly detection method using a neural network architecture for the detection of chronic brain infarcts on brain MR images. The neural network was trained to learn the visual appearance of normal appearing brains of 697 patients. We evaluated its performance on the detection of chronic brain infarcts in 225 patients, which were previously labeled. Our proposed method detected 374 chronic brain infarcts (68% of the total amount of brain infarcts) which represented 97.5% of the total infarct volume. Additionally, 26 new brain infarcts were identified that were originally missed by the radiologist during radiological reading. Our proposed method also detected white matter hyperintensities, anomalous calcifications, and imaging artefacts. This work shows that anomaly detection is a powerful approach for the detection of multiple brain abnormalities, and can potentially be used to improve the radiological workflow efficiency by guiding radiologists to brain anomalies which otherwise remain unnoticed

Approach to identify product and process state drivers in manufacturing systems using supervised machine learning

The developed concept allows identifying relevant state drivers of complex, multi-stage manufacturing systems holistically. It is able to utilize complex, diverse and high-dimensional data sets which often occur in manufacturing applications and integrate the important process intra- and inter-relations. The evaluation was conducted by using three different scenarios from distinctive manufacturing domains (aviation, chemical and semiconductor). The evaluation confirmed that it is possible to incorporate implicit process intra- and inter-relations on process as well as programme level through applying SVM based feature ranking. The analysis outcome presents a direct benefit for practitioners in form of the most important process parameters and state characteristics, so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control