| dc.description.abstract | The textile industry is an indispensable industry in Taiwan. Today, the production process encounters three key challenges: rapid response, stable quality, and delivery control. Finding the best parameter range combination with a lower defect rate in the parameter setting stage of the machine is the key to maintaining stable quality. Rule mining is the focus of this research. The total number of defects in the database ranks in the top four types of defects, and data analysis is carried out according to their related fabric properties and machine parameters. The purpose of the research is to find out the most influential feature set for each type of defect value, and integrate the obtained features into a rule type. In the end, the rules for each type of flaws need to be combined in a specific way, aiming to effectively reduce the four flaws at the same time and bring global benefits. How much to reduce the percentage of total imperfections.
The experiment comprehensively analyzes various aspects, including the selection of different features [criterion, scoring], the development of a self-decision tree mechanism to extract candidate rules, the expansion or reduction of the parameter range of candidate rules, and the combination of merging rules, etc. After using nested cross-validation, the global rules with benefit greater than or equal to 0.8, or support greater than or equal to 0.5 or approximately 0.5 can be mined at each time split point. The support degree is used to evaluate the amount of test data covered by the rule, and it can also be interpreted as the degree of influence of the rule on the stability of the machine state. | en_US |