Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88595
Title: Predictive Modelling and Analysis of Process Parameters on Material Removal Characteristics in Abrasive Belt Grinding Process
Authors: Pandiyan, Vigneashwara
Caesarendra, Wahyu
Tjahjowidodo, Tegoeh
Praveen, Gunasekaran
Keywords: Belt Grinding
ANOVA
Issue Date: 2017
Source: Pandiyan, V., Caesarendra, W., Tjahjowidodo, T., & Praveen, G. (2017). Predictive Modelling and Analysis of Process Parameters on Material Removal Characteristics in Abrasive Belt Grinding Process. Applied Sciences, 7(4), 363-.
Series/Report no.: Applied Sciences
Abstract: The surface finishing and stock removal of complicated geometries is the principal objective for grinding with compliant abrasive tools. To understand and achieve optimum material removal in a tertiary finishing process such as Abrasive Belt Grinding, it is essential to look in more detail at the process parameters/variables that affect the stock removal rate. The process variables involved in a belt grinding process include the grit and abrasive type of grinding belt, belt speed, contact wheel hardness, serration, and grinding force. Changing these process variables will affect the performance of the process. The literature survey on belt grinding shows certain limited understanding of material removal on the process variables. Experimental trials were conducted based on the Taguchi Method to evaluate the influence of individual and interactive process variables. Analysis of variance (ANOVA) was employed to investigate the belt grinding characteristics on material removal. This research work describes a systematic approach to optimise process parameters to achieve the desired stock removal in a compliant Abrasive Belt Grinding process. Experimental study showed that the removed material from a surface due to the belt grinding process has a non-linear relationship with the process variables. In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) model is used to determine material removal. Compared with the experimental results, the model accurately predicts the stock removal. With further verification of the empirical model, a better understanding of the grinding parameters involved in material removal, particularly the influence of the individual process variables and their interaction, can be obtained.
URI: https://hdl.handle.net/10356/88595
http://hdl.handle.net/10220/44674
ISSN: 2076-3417
DOI: 10.3390/app7040363
Rights: © 2017 by The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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