Modeling and Multiresponse Optimization of Quality Characteristics for the Micro-EDM Drilling Process
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- 作者：U. Natarajan ; X. Hyacinth Suganthi…
- 刊名：Transactions of the Indian Institute of Metals
- 出版年：2016
- 出版时间：November 2016
- 年：2016
- 卷：69
- 期：9
- 页码：1675-1686
- 全文大小：1,198 KB
- 刊物类别：Chemistry and Materials Science
- 刊物主题：Materials Science
Metallic Materials
Materials Science, general
Tribology, Corrosion and Coatings - 出版者：Springer India
- ISSN：0975-1645
- 卷排序：69
文摘
Micro parts of various kinds are now in demand for fabricating high-tech products such as micro-electronic parts and MEMS applications. In order to supply these micro parts, the development of micromachining technology becomes increasingly important. Among the various stages of micromachining, the fabrication of holes and pins of precise nature are essential for many miniature components. Micro-hole drilling, is used in various precision industries, such as the production of automotive fuel injection nozzles, watch and camera parts to generate deeper holes with better straightness, better roundness and smoother surfaces. Electrical discharge machining (EDM) is a non-contact machining process for electrically conductive materials which can produce high aspect ratio holes, particularly useful in the tool making industry. The objective of the study on micro-EDM drilling process parameters is to analyse the effect of drilling parameters such as the feed rate (A), capacitance (B) and voltage (V) on machining stainless steel shim using a tungsten electrode of 300 µm diameter. The performance measures are surface roughness (Ra), metal removal rate and tool wear ratio. Response surface methodology has been used and experiments have been conducted using central composite design. Statistical models of the output responses have been developed using three level full factorial design of experiment. The developed models have been used for multi response optimization by desirability function approach to determine the optimum machining parameters. These optimal machining parameters are then validated experimentally, and it is observed that the response values are very close to the predicted values with less error.KeywordsMicro-drillingRSMOptimizationMRRSurface roughnessTWR