Development of Prediction Models for Spark Erosion Machining of SS304 Using Regression Analysis Sree Vidyanikethan Engineering Colllege

Format:

Book

Conference/Event

Author/Creator:

Natarajan, Manikandan, author.

Contributor:

Katta, Lakshmi Narasimhamu

Pasupuleti, Thejasree

R, Ramesh

Silambarasan, R.

Conference Name:

10TH SAE India International Mobility Conference (2022-10-12 : Bangalore, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2022

Summary:

Stainless Steel 304 (SS304) is a nickelchromiumbased alloy that is regularly used in valves, refrigeration components, evaporators, and cryogenic containers due to its greater corrosion resistance, high ductility, and non-magnetic properties, as well as good weldability and formability. Multiple regression analysis was used to establish empirical relationships between process variables. Additionally, the established regression equations are employed to predict and compare experimental data. Due to the increasing demands for high-quality surface finishes and complex geometries, traditional methods are being replaced by non-conventional techniques such as wire EDM. This process, which emerged from the electrical discharge machining concept, mainly involves creating intricate components. WEDM results in a high degree of precision and excellent surface quality. Due to the complexity of WEDM, the processing parameters cannot be selected by using the trial-and-error method. The various parameters that are used in a process such as machining will have a huge impact on the production rate and quality of a component. In addition to the surface finishes, the other factors that affect the performance of a machine are also taken into account. This study aims to analyze the three pulse on time (Ton'), pulse off (Toff'), and applied current parameters of WEDM. An experimental study of WEDM of SS304 alloy was conducted utilizing Taguchi's response analysis technique, with a particular emphasis on the building of multiple regression models. The results of this study show that the predicted values are almost similar to the experimental values. The findings of this study will provide manufacturers with a comprehensive guide on how to improve the quality and production rate of their components using the WEDM method

Notes:

Vendor supplied data

Publisher Number:

2022-28-0339

Access Restriction:

Restricted for use by site license