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discontinuity or gradual corrosion. Therefore, surface roughness investigation is essential for number of applications concerned with the control of friction, fatigue, and wear of parts [1]. Nowadays, machines work at higher speeds and loads which need higher dimensional and geometrical accuracies along with surface quality of the finished parts like bearings, seals, shafts, machine ways, gears, etc. The ability of a manufacturing process to produce desired surface finish depends on machine tool, cutting process, cutting parameters, work material, and cutting tool [2].

Identification of correct working of gearbox is a very important function during end of line inspection in the assembly line while manufacturing the gearbox. Such inspection is performed by an operator by listening to the sound of gearbox while running it on a test bench. Based on the sound emitted by the gearbox combined with experience and judgment of the operator, the gearbox is passed or rejected for fitting inside the vehicle. This paper makes an attempt to use artificial intelligence techniques to identify gearbox condition in the above environment by using psychoacoustic features to replace human hearing. Experiments are carried out on a gearbox test rig and sound data are acquired for good and faulty gear conditions. Psychoacoustic features and statistical indices are extracted from the data and these are then used as input to an artificial neural network. The artificial neural network output is the condition of gearbox. Performances of psychoacoustic and statistical indices are then compared. It is found that psychoacoustic features are able to predict gearbox condition with an accuracy of 99% and 98% for good and faulty conditions, respectively, whereas the statistical features are able to do the same with 97% and 98% accuracy. Therefore, it is concluded that psychoacoustic features have the potential to be used for the end of line inspection of gearbox in manufacturing environment and the process of inspection can be made objective by eliminating operator's ability and judgment.

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This paper explores use of Teaching Learning Based Optimization (TLBO), ‘JAYA’ (Sanskrit word means Victory) and Genetic Algorithm (GA) for the combined minimization of roughness of machined surface and forces generated in cutting in turning of Ti-6Al-4V. Experimentation was carried out with Response Surface Methodology (RSM) and the Central Composite Design (CCD). Speed of cutting (m/min), feed rate (mm/min) and depth of cut (mm) were the design variables for optimization. Two responses (roughness of machined surface and force of cutting) were independently minimized. RSM was useful in finding empirical relations and the effect of each parameter and their interactions on the responses considered. Analysis of variance (ANOVA) was used to find out the effective and non-effective factors and correctness of the models. Later on, a multi-objective optimization function was developed for minimizing both – roughness in machined surface and force generated in cutting using weights method and the correctness of weights were confirmed by Analytical Hierarchy Process (AHP). After formulating the combined objective function, TLBO, ‘JAYA’ and GA methods were used for further parameter optimization of the turning process. Performance of TLBO and ‘JAYA’ algorithm was compared with that of Genetic Algorithm (GA). It is found that TLBO and ‘JAYA’ performed better than GA in the combined minimization of roughness and forces in while turning Ti-6Al-4V. It is also found from the results that higher cutting speed (171.4 m/min) and lower feed rate (55.6 mm/min) can produce better surface roughness and minimum cutting forces in machining of Ti-6Al-4V. Highlights This paper Presents, implementation of Advanced Algorithms for multi objective optimization of cutting force and surface roughness in machining of difficult to cut Ti-6Al-4V. Two newly developed advanced algorithms such as JAYA and Teaching learning based optimization (TLBO) ‘without algorithm control’ parameters are used for machining response optimization. Objective functions for surface roughness and cutting forces are developed after actual face milling operation performed in sequential manner with response surface methodology. Developed models are verified with statistical test (ANOVA, residual plots) as well as confirmation experiments. It is concluded from the results that machining parameters can be optimize using advanced algorithms. This work can help machinists to select cutting parameters based on desired machining response.

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In this paper, artificial neural network approach is used to predict surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718. Experiments were performed by using cryogenically treated and untreated inserts, and various responses were measured. Then, these measured responses were used as input to the artificial neural network to predict surface roughness. It is found that the models developed by artificial neural network are predicting surface roughness with more than 98% accuracy. Further, the predictions obtained by artificial neural network are compared with the results of regression-based prediction models earlier proposed by the authors. The modified regression models were estimating surface roughness with more than 90% accuracy. Based on correlation coefficient values, the prediction results of modified regression model are compared with those obtained by artificial neural network. Finally, it is concluded that artificial neural network models are better for estimating surface roughness than the regression models and such predictions are useful for real-time control of the process to acquire the desired surface roughness. Keywords Artificial neural network • Surface roughness • Inconel 718 Abbreviations CNC Computer numerical control RSM Response surface methodology FOE M Modified first-order equation ANN Artificial neural network BP Backpropagation BR Bayesian regularization LM Levenberg-Marquardt AE Absolute error (%) MAE Mean absolute error (%) MSE Mean square error (%) List of symbols v Cutting speed (m/min) f Feed rate (mm/rev) d Depth of cut (mm) F c Cutting force (N) S Sound pressure level (Pa) V v Vibration velocity of workpiece (m/s) n Number of experiments R ai Average of measured surface roughness in μm R ai Estimated surface roughness h Number of neurons in single hidden layer R 2 Correlation coefficient