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Title: Study on process parameters and computational modelling of material removal in abrasive belt grinding process
Authors: Gunasekaran, Praveen
Keywords: DRNTU::Engineering
Issue Date: 2018
Abstract: Industries such as aerospace, automobile, marine, medical, optical require compliant tools for material removal and for achieving a good surface finish in the intricate shape of the components. Hard tool operations such as milling, turning and conventional grinding are inefficient in machining intricate shapes and geometry. Abrasive belt grinding process is one of the most widely used two-body abrasive compliant machining processes wherein the abrasive belt is forced against the semifinished components of complex shape and geometry to machine off topographies such as burr, chip and to achieve the required material removal and surface finish. An abrasive compliant belt grinding system consists of an abrasive belt with fine grains, a driver unit, a compliant polymer backing of suitable hardness and a robot manipulator is used for tool path planning. Compliant belt grinding tool adapts to a part profile, and a controlled force is exerted on target component to remove material. Belt grinding process is highly nonlinear due to the complexity of the underlying unknown physical mechanism. The presence of multiple parameters working in different regimes creates a dynamic condition which is not entirely understood well in industries. These dynamics arise from the multiple parameters such as belt speed, feed rate, and grit size involved. Most industries operational in belt grinding process are still largely based on empirical rules and experience of the operator. Quantification of such a dynamic process of material removal is not yet studied in detail even in the scientific research works. This research study is conducted to understand the material removal of the aluminium workpiece with different cutting parameters such as belt speed, feed rate, roller rubber property, force, and the grit size of the cutting tool. The inter-relation and prominence of these parameters are studied by performing experimental trials using Taguchi Design of Experiments (DoE). Taguchi design of experiments is adopted here due to its timesaving factors . The trial result is then analysed using Analysis of Variance (ANOVA) to understand the effect of changes in the process parameters to that of material removal. The regression model based on multilayer artificial neural network was developed for belt grinding process to model the material removal process computationally. This study is an attempt to bridge the gap between understanding material removal of belt grinding process and contribution of process parameter involved in abrasive belt grinding process.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Theses

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