Yuta Wakutsu , Satoshi Natori , Hiroki Ochiai , Kazuya Suda , Hiromasa Kaneko
{"title":"Distortion prediction model considering process types in film manufacturing process and identification of critical process variables","authors":"Yuta Wakutsu , Satoshi Natori , Hiroki Ochiai , Kazuya Suda , Hiromasa Kaneko","doi":"10.1016/j.chemolab.2025.105474","DOIUrl":null,"url":null,"abstract":"<div><div>Optical films are used in flat panel displays, touch sensors and other devices. The film is wound and sent to the next process, but defects are generated in the winding, indicating an issue. Defects are often identified by customers after shipment. It is therefore anticipated that the identification of characteristics associated with defects at the end of the process will facilitate the detection of defective products in advance, or alternatively, result in a reduction of the defects themselves. One of the factors that contribute to the occurrence of defects is the distortion of the film on the roll surface. The degree of distortion is determined by calculating the difference between the instantaneous and average distance between the film surface and the sensor, as measured by the displacement meter installed before the winding. The objective of this study was to identify the process conditions that cause the distortion of the film as measured by the displacement meter through the application of machine learning techniques. A model was constructed between the sensor data of the process conditions and the distortion index, the relationship between them was identified, and the process causing the distortion was estimated by analyzing the model. The results of this study successfully narrowed down the process variables that are common causes of distortion among three displacement meters with different measurement positions.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"264 ","pages":"Article 105474"},"PeriodicalIF":3.7000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemometrics and Intelligent Laboratory Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169743925001595","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Optical films are used in flat panel displays, touch sensors and other devices. The film is wound and sent to the next process, but defects are generated in the winding, indicating an issue. Defects are often identified by customers after shipment. It is therefore anticipated that the identification of characteristics associated with defects at the end of the process will facilitate the detection of defective products in advance, or alternatively, result in a reduction of the defects themselves. One of the factors that contribute to the occurrence of defects is the distortion of the film on the roll surface. The degree of distortion is determined by calculating the difference between the instantaneous and average distance between the film surface and the sensor, as measured by the displacement meter installed before the winding. The objective of this study was to identify the process conditions that cause the distortion of the film as measured by the displacement meter through the application of machine learning techniques. A model was constructed between the sensor data of the process conditions and the distortion index, the relationship between them was identified, and the process causing the distortion was estimated by analyzing the model. The results of this study successfully narrowed down the process variables that are common causes of distortion among three displacement meters with different measurement positions.
期刊介绍:
Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines.
Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data.
The journal deals with the following topics:
1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.)
2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered.
3) Development of new software that provides novel tools or truly advances the use of chemometrical methods.
4) Well characterized data sets to test performance for the new methods and software.
The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.