Form deviation of machined components need to be controlled within the required tolerance values for proper assembly and to serve the intended functional requirements. Methods like minimum zone circle (MZC) method, minimum circumscribed circle (MCC) method, maximum inscribed circle (MIC) method and least square circle (LSC) method are used to evaluate roundness error. Roundness error evaluation includes collection of co-ordinate points on the surface of the compo-nent and measurement using any of the above methods. Since, manual measurement of roundness error from these coordinate points is time consuming and less accurate, use of algorithms is highly appreciated. A detailed study of various optimization techniques showed that all evolutionary and swarm intelligence-based optimization algorithms require common control parameters and algorithm specific parameters. Improper tuning of these parameters either increases the computational effort or results in local optimal solution. Teaching Learning Based Optimization (TLBO) algorithm is used in this work as it does not require any algorithm specific parameters for the evaluation of roundness error. The results obtained are then compared with the results of Particle Swarm Optimization (PSO) algorithm to know the merits and demerits of both the algorithms when used for form measurement.

The NanoMQL technique is used to overcome the limitations of wet grinding due to economic and ecological problems. The performance measure is largely influenced by the process parameters such as table speed, depth of cut, air pressure, coolant flow rate and nanofluid concentration. In this paper, the performance of NanoMQL technique in terms of surface roughness was evaluated for hard and soft EN31 steel. The Experiments were conducted by response surface methodology (RSM) using statistical software to develop regression model of surface roughness and optimization was carried out using Jaya algorithm. The result shows that lowest value of surface roughness was obtained for NanoMQL of hard steel in comparison with soft steel under grinding environ-ments such as wet, MQL and NanoMQL. Hence to improve the performance of soft steel, the modeling and optimization of surface roughness were carried out. The significant parameters were considered for model development and validity of model determined through ANOVA (Analysis of variance). Lastly, the optimal values were determined using Jaya algorithm for minimum surface roughness and the percentage error observed to be close with the experimental test.

WEDM has evolved as a well admired technique for machining of difficult to cut materials such as superalloys. WEDM produces intricate shape and profiles of superalloys by thermoelectric erosion process. But as the process is carried out at very high temperature, the formation of heat affected zone, microcracks, recast layer, porosity etc. resulted in decreased surface integrity of machined specimen and becomes a big problem in WEDM. Discharge energy is the most influencing param-eters that affect the surface integrity of WEDmed samples. In this study, Inconel 825, widely used in aerospace industry for making of combustor casing and turbine blades, was machined with WEDM under different discharge energy. The surface topography of the WEDMed specimen was carried cut by using SEM, XRD and EDX techniques. It was observed from the SEM micrograph that the machined surface includes cracks, pockmarks, craters, and pulled out material. The density and sice of craters increase with increase in discharge energy. Surface crack density of 0.0138 μm/μm2 and recast layer thickness of 34.62μm was obtained for the machined sample at high value of discharge energy while at low value surface crack density of 0.0016 μm/μm2 and recast layer thickness of 20.99μm was observed. EDX and XRD analysis of the specimen showed that an ap-preciable amount of elements viz. Fe (Ferrous), Cr (Chromium), Cu (Copper), Ni (Nickel) are mi-grated to the surface of the workpiece at high value of pulse on time.