Epoxy matrix composites filled with micro-sized LD sludge: wear characterization and analysis

5th National Conference on Processing and Characterization of Materials

Purohit, Abhilash^1 ; Satapathy, Alok^1

Department of Mechanical Engineering, National Institute of Technology, Rourkela

769008, India^1

关键词: Different proportions;    Epoxy composite;    Epoxy-matrix composite;    LD Sludge;    Sliding wear;    Sliding wear behaviors;    Taguchi's orthogonal arrays;    Wear characterization;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/115/1/012006/pdf
DOI  :  10.1088/1757-899X/115/1/012006

来源: IOP

【 摘 要 】

Owing to the very high cost of conventional filler materials in polymer composites, exploring the possibility of using low cost minerals and industrial wastes for this purpose has become the need of the hour. In view of this, the present work includes the development and the wear performance evaluation of a new class of composites consisting of epoxy and microsized LD sludge. LD sludge or the Linz-Donawitz Sludge (LDS) are the fine solid particles recovered after wet cleaning of the gas emerging from LD convertors during steel making. Epoxy composites filled with different proportions (0, 5, 10, 15 and 20 wt %) of LDS are fabricated by conventional hand lay-up technique. Dry sliding wear trials are performed on the composite specimens under different test conditions as per ASTM G 99 following a design of experiment approach based on Taguchi's orthogonal arrays. The Taguchi approach leads to the recognition of most powerful variables that predominantly control the wear rate. This parametric analysis reveals that LDS content and sliding velocity affects the specific wear rate more significantly than normal load and sliding distance. Furthermore with increase in LDS content specific wear rate of the composite decreases for a constant sliding velocity. The sliding wear behavior of these composites under an extended range of test conditions is predicted by a model based on the artificial neural network (ANN).