Development of the Analysis and Optimization Strategies for Prediction of Residual Stresses Induced by Turning Processes

Difficult-to-machine materials are widely used in the aerospace and automotive industries including landing gears of aircrafts, drive-shafts of automobiles, and high strength bolts and frame parts of airplanes and motorsports due to their high toughness, less sensitivity to heat, and high resistance to fatigue and corrosion. Machining these materials is accompanied by high cutting temperatures and forces, which cause high residual stresses. It is known that high temperature leads to inaccuracies in component dimensions and causes phase transformation in the material. High cutting forces also raise the power consumption of turning machines and result in an excessive deflection and consequently breakage of the tool. Also, both large cutting temperatures and forces cause high tool wear. Most importantly, machining-induced tensile residual stresses have detrimental effects on the performance of components due to having the tendency to open tiny cracks and speed up crack propagation, which subsequently results in decreasing the resistance to fatigue and corrosion. In contrast, high compressive residual stresses have beneficial effects as they tend to close cracks and slow down crack propagation, which consequently increases the fatigue life considerably. The machining process is required to be efficient by removing as large amount of material as possible, meaning to have a high material removal rate. Machining forces, temperature, residual stresses, and material removal rate depend highly on machining parameters including cutting conditions and tool geometry. Therefore, a thorough optimization study is required to be conducted to identify optimal machining parameters including cutting speed, feed rate, edge radius, rake angle, and clearance angle to improve response variables specially residual stresses, which will be highly desirable and of paramount importance to the industry. More particularly, when the optimization is carried out based on Finite Element Method (FEM), by which the expensive, time-consuming process of experimental tests is avoided, the outcome will be more economical for the industry. Finite Element (FE) modeling of orthogonal turning is considered as an open-ended subject as most of the phenomena involved in the orthogonal turning, which also exist in other machining operations, are not fully understood. In the present research work, first, a predictive high-fidelity finite element model is developed using Abaqus software to obtain response variables of cutting temperature, machining forces, and residual stresses induced by orthogonal turning 300M Steel. The validity of the developed FE model is then verified by comparing the predicted machining forces, chip thickness, and residual stresses with those of experimental tests obtained in turn using a piezoelectric dynamometer, a digital micrometer, and ‘X-Ray diffraction apparatus, electropolishing equipment, and a profilometer machine’. The FE model is then utilized to systematically derive response functions (Meta or surrogate models) for desired FE outputs using D-optimal Design of Experiment (DoE) and Response Surface Method (RSM). The derived response functions explicitly relate the desired responses to identified design parameters, and therefore, can be effectively utilized for design optimization problems without using the FE model. Finally, multi-criteria optimization problems are formulated to reduce superficial residual stresses individually and improve a combination of residual stresses, cutting temperature, cutting and thrust forces, and material removal rate by obtaining optimum values of machining parameters including cutting speed, feed rate, edge radius, rake angle, and clearance angle. Special attention is devoted to minimizing the machining-induced residual stresses. Optimization is conducted using a hybrid method of Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) technique in order to accurately capture the global optimum values of machining parameters and response variables. The optimization results show considerable improvement in the total objective function and especially residual stresses. Since there are no research studies on the finite element simulation, experimental test, and most importantly, constrained and unconstrained multi-performance optimization of machining characteristics and residual stresses for radial turning of 300M steel, the results of the present research can be utilized as a reference for future works along this field

Grain size and temperature evolutions during linear friction welding of Ni-base superalloy Waspaloy: Simulations and experimental validations

This research study was aimed at investigating the influence of linear friction welding parameters on grain size alteration and temperature distribution of Ni-base superalloy Waspaloy. A 3D finite element model was developed to predict average grain size and peak temperature as responses. The linear friction welding parameters consisted of oscillation amplitude, oscillation frequency, and applied pressure. Initially, the evolution of the average grain size as a function of the most influential process parameters was subsequently modeled based on the Johnson-Mehl-Avrami-Kolmogorov recrystallization model and were then validated with experimental results. Then, D-optimal design of experiments and analysis of variance were conducted to determine the most influential process parameters that affect the average grain size and peak temperature of the welded joint. Thereafter, response surface method was employed to obtain the regression models of the responses. The analysis of variance demonstrated that the P-value of the regression models was smaller than 5% and R2, Radj2, and RPred2 were between 87% and 97%, which showed that the predictive regression models of PT and AGS can be used with a high level of confidence. The regression models were then validated by selecting two extra LFW tests in the space of the DoE. The optimum values of the welding parameters were determined to minimize the responses. The multi-criteria optimization analysis showed that both average grain size and peak temperature were more dependent on pressure than oscillation amplitude and frequency. The developed finite element and regression models can be utilized as a predictive tool for the design of joining industrial components, which minimize expensive and time-consuming experimental tests and measurements