Tool wear monitoring is one of the critical issues in the automated industry. Though use of artificial neural networks for tool wear monitoring is widely reported in the literature, the models are built only for dry machining. In the present work, a neural network model for cutting fluid assisted machining is proposed. Experimentation has been carried out using different cutting fluids and the results were used to build up and test the model. Further, an improvement in the network is proposed using simulated annealing, which can automatically and effectively optimize the network architecture, as opposed to the conventional trial and error method.

Cutting fluids are widely used in metal cutting industries for centuries. To cater the needs of state-of-the-art machining processes, several fluid formulations are available in the present day market. Of such available fluids, water-soluble fluids are prominent. Although the need of cutting fluids is well stressed upon and is much explored, the role of different ingredients of the fluid on the machining parameters is not much investigated. The present work is an attempt to study the role of emulsifier on the effectiveness of the cutting fluid.

Cutting fluids occupied a place of prominence in metal cutting industries for centuries. To cater to the needs of state-of-art machining processes, several fluid formulations are available in the present day market. Of such available fluids, water-soluble fluids occupy a role of prominence. Though the need of cutting fluids is well stressed upon and is much explored, the role of different ingredients of the fluid on the surface of the machined component is not much investigated. The present work is an attempt to study the role of emulsifier on the machined surface through estimation of surface roughness and change in hardness of the samples.