Date of Award

2018

Document Type

Open Access Dissertation

Department

Mechanical Engineering

Sub-Department

College of Engineering and Computing

First Advisor

Xiaomin Deng

Abstract

Manufacturing processes such as machining and cutting often produce metal wastes (e.g. machining chips) that can be costly to recycle using conventional methods that involve melting. The Friction Extrusion Process (FEP) and Friction Consolidation Process (FCP) provide a novel method of recycling machining chips to produce useful products such as wires or consolidated disks without melting. These solid-state processes do not require complicated equipment and offer a cost effective and environment friendly alternative route to metal waste recycling.

The current study was aimed at achieving an understanding of the mechanical and thermal behavior of machining chips during compaction and consolidation processes that occur in FEP and FCP, which is currently lacking. An integrated experimental and numerical approach was employed. Experiments were carried out to provide opportunities to measure and extract stress, strain and thermal response information on machining chip specimens during and/or after compaction and consolidation tests. The experimental data was analyzed and findings were used as a basis to develop mathematical models for the mechanical and thermal behavior of the chips material during and after compaction and consolidation. These models took into account the change in density of the chips material during compaction and consolidation process. The model parameter values as functions of the relative density were extracted from experimental measurements of mechanical and thermal responses. These models were implemented in user subroutines (UMAT) and user defined functions (UDFs) for a commercial finite element and numerical simulation software packages. The numerical simulations of validation experiments were carried out to predict the mechanical and thermal behavior of chips material in the validation experiments. Model predictions were validated through comparisons with experimental measurements and were found to agree well with experimental measurements.

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