{'fraction_non_alphanumeric': 0.01740294511378849, 'fraction_numerical': 0.0, 'mean_word_length': 5.678571428571429, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Quality improvement of steel products by using multivariate data analysis

This paper describes quality improvement methods based on multivariate data analysis and their application to an industrial steel process. The PCA-LDA, which combines principal component analysis and linear discriminant analysis, is used for influential factor analysis of the qualitative quality variables. In addition, Data-Driven Quality Improvement (DDQI) is used to determine the optimal operating condition that can achieve the desired product quality under a given objective function and various constraints. The PCA-LDA and the DDQI can provide useful information to improve product quality. The experimental results show the effectiveness of the proposed methods.