Data mining techniques applied to predictive modeling of the knurling process

Autor:	Chang-Xue Jack Feng, Xian-Feng (David) Wang
Rok vydání:	2004
Předmět:	Engineering PRESS statistic Artificial neural network business.industry Fractional factorial design Regression analysis computer.software_genre Residual Industrial and Manufacturing Engineering Knurling Data mining business computer Statistic Statistical hypothesis testing
Zdroj:	IIE Transactions. 36:253-263
ISSN:	1545-8830 0740-817X
Popis:	Knurls are designed into a product to provide the correct frictional force for easy assembly and maintenance and sometimes for decorative purposes. The literature to date has merely studied how to realize a good and consistent knurl, but no predictive models of the knurling process have been presented. This paper applies two competing data mining techniques, regression analysis and artificial neural networks, to develop a predictive model of the knurling process. Fractional factorial design of experiments is used to plan the experiments. Four criteria, namely the PRESS statistic, the adjusted R 2, the C p statistic, and the residual mean square s 2, are employed to select the best regression model. Hypothesis testing is conducted to test the effectiveness of each model, and to compare the two data mining schemes. This study demonstrates that for a reasonably large set of data from structurally designed experiments, the two methods produce comparable results in both model construction (or training) and mod...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3b5429f94cfc1de1f88419e8454c60d7 https://doi.org/10.1080/07408170490274214 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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