Optimization of three-dimensional surface topography in hard turning

Hard Turning has the potential to provide an alternative to grinding in some finish machining applications. The technical and economic viability of hard turned components has been well documented and includes elimination of coolant, lower energy consumption, high metal removal rates, ability to machine thin wall sections, easy swarf removal, ability to machine a variety of component profiles on the same machine tool, increased flexibility and speed, decreased set-up times, reduced production times and surface quality comparable if not better than those obtained by grinding. Clearly, a fuller understanding of finish hard turning will require knowledge of the complete system and of the mutual interactions between the machine characteristics, material properties and process parameters. Tool wear, residual surface profiles, surface integrity and surface texture obtainable are problem areas that need to be addressed. This research aims to develop a methodology to aid in the process planning of hard turning in order to optimize surface texture, a key measure of functionality and reliability of a component in service. Typically Average Surface Roughness Ra has been widely used in industry to establish surface texture needed for a given application. It is now known that the single parameter Ra is inadequate to define the functionality of a surface texture. The quality of a surface can be determined by the nature of its interaction with another surface. Thus a surface with significant peaks will not make as good a bearing surface as a surface with deep valleys and low peaks. Thus, while two different surfaces may have similar values of Ra, they behave differently under fatigue loading conditions. 3-D visualization of expected surface texture will facilitate optimization of machining parameters to produce function specific surfaces. An investigation of some surface texture prediction models available in literature is carried out. The advantages and shortcomings of these models are discussed. Also, criteria for an efficient and feasible 3-D surface texture prediction model are developed. A new prediction method based on neuro-fuzzy techniques is proposed. Also, optimization using some proposed 3-D surface parameters is carried out and compared with the results of those obtained using 2-D parameters.;The software programs developed can play a vital role in process planning in manufacturing industry to justify the potential transfer to hard turning from grinding. More importantly, they provide a mechanism to visualize, predict, and optimize the functionally useful 3-D surface topography and reduce the proliferation or rash of scale-type 2-D parameters.