Kurniawan, Laurentius Soni and Hendriana, Dena and Iskandar, Aulia Arif (2018) Modelling of Machining Parameters to Predict The Surface Roughness Using Multiple Regression and Neural Network. Masters thesis, Swiss German University.

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Abstract

Surface roughness plays an important role in a machining product especially for the functional requirements of the part. Currently, the machining process to produce a quality product is very complex, so it will be difficult to develop a comprehensive model that includes all cutting parameters. In this study, surface roughness of the workpiece with S45C material will be measured as a result of a turning machining process with different cutting parameters including cutting speed, feed rate and depth of cut. Multiple regression approaches and neural networks model are used as tools for modeling the surface roughness resulting from the turning process. The result of the Multiple Regression approach and the Artificial Neural Network model will be compared using the statistic method to determine the level of prediction accuracy. In this study the multiple regression approaches have more accurate prediction than artificial neural network model.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Surface Roughness; Multiple Regression; Artificial Neural Network
Subjects:	Q Science > QA Mathematics > QA76 Computer software > QA76.87 Neural networks (Computer science) T Technology > TA Engineering (General). Civil engineering (General) > TA418.7 Surface roughness--Measurement T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions:	Faculty of Engineering and Information Technology > Department of Mechatronics Engineering
Depositing User:	Astuti Kusumaningrum
Date Deposited:	20 Jul 2020 16:24
Last Modified:	20 Jul 2020 16:24
URI:	http://repository.sgu.ac.id/id/eprint/811

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