Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.

Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used.

Simulated annealing is a method of solving uncontrolled and controlled optimization problems. This method simulates the physical process of heating an object and then slowly lowering the temperature to minimize defects, thus reducing system power. Simulated Annealing is a Constant global search Is the optimization algorithm. The algorithm is attracted by annealing in metallurgy, where the metal is rapidly heated to a high temperature and then slowly cooled, which increases its strength and makes it easier to work with. Implements simulated anal search in the same way. With each repetition in the Simulated Annealing Algorithm, a new point Created approx. From the current point Distance to new point or amount of search, Probability distribution that is in proportion to the temperature. All of the algorithm Accepts intent to reduce new points, but will raise the target with a certain probability Accepts points as well. Accept the scope The algorithm that raises the scores avoids getting stuck in the local minima and Explore globally for possible solutions. Algorithm Continuing, to lower the temperature properly, annealing as the temperature drops, algorithm search size reduces and at least integrates

Composite laminates are being widely used in engineering industry primarily due to their high strength-to-weight ratio. Considerable research has been carried out to understand the static and dynamic behaviour of laminated composite plates. There is much demand for developing efficient finite element codes which can predict the dynamic responses of laminated structures at affordable computational cost. In this paper a nine node isoparametric plate bending element has been used for free vibration analysis of laminated composite plate. The first-order shear deformation theory (FSDT) has been incorporated in the element formulation. Composite plates with different side-to-thickness ratio (a/h), ply orientations and number of layers have been analysed. Based on comparison with literature data, we propose that the present formulation is capable of yielding highly accurate results. Laminated composites with central cut-outs are also studied. Novel data is reported for skew laminated composites. It is found that the natural frequency increases with the increase in skew angle (a) and decreases with increase in aspect ratio (b/a) and thickness (h).