In this paper, the influence of machining parameters, Cutting Speed, Feed Rate, and Depth of cut, on surface finish during dry orthogonal turning of Al 6061 – T6 alloy, is studied using the response surface methodology (RSM). This paper proposes a unique way to predict the surface finish in turning, using the effective plastic strain (PEEQ) values obtained from the simulations. A comprehensive finite element model was proposed to predict the surface finish accurately, by correlating the variance of the PEEQ. The Johnson-Cook damage model is used to define the damage criteria and Johnson-Cook material model is used to explain the material constitutive behavior. A dynamic, explicit method is used along with the Adaptive Lagrangian-Eulerian (ALE) method to predict material flow accurately. The influence of machining parameters was studied by assuming Central Composite Design (CCD). The output response, PEEQ, was fitted into analytical quadratic polynomial models using regression analysis, which shows that feed rate was the most dominant factor for PEEQ than the other parameters considered in this study. Using the individual desirability function method, the objective, optimal setting of the machining parameters was obtained for better surface finish.

This paper presents a detailed finite element model to predict deformation and other machining characteristics involved in high-speed orthogonal machining (cutting speed > 54 m/min) of hard-to-deform materials like Ti6Al4V. The influence of various cutting parameters like feed rate, spindle speed, and rake angle, on the output parameters like cutting force and surface finish, was analysed. The paper tries to relate the degree of surface finish with the variance of the effective plastic strain. The Johnson-Cook material model is used to describe the material constitutive behaviour, and the Johnson-Cook damage model is used to establish the damage criteria. Due to the high machining costs associated with the titanium alloy, the model is first validated using aluminium alloy (Al2024-T351), and the same model is then extended to predict the results for titanium alloy. The matrix for the design of experiments (DOE) considers a full factorial approach, with about 48 simulations, for a proper understanding on the influence of the major machining parameters. A dynamic, explicit integration scheme is used along with the arbitrary Lagrangian-Eulerian (ALE) technique to accurately predict material flow. This paper also presents a unique method to tackle the commonly encountered numerical issues involved in modelling self-contact.

Ensuring high surface quality while minimizing tool wear remains a significant challenge in machining titanium alloys, particularly for applications in the aerospace, biomedical, and precision manufacturing industries. This study presents a novel integrated approach that combines experimental design, statistical analysis, intelligent modeling, and optimization to predict and minimize surface roughness (Ra) during the dry turning of Ti-6Al-4 V. Spindle speed (SS), feed rate (FR), and depth of cut (DoC) were selected as machining parameters, while cutting force (Fz), surface roughness (Ra), chip thickness (CW), and flank wear served as response variables. A Design of Experiments (DoE) with 20 trials was generated using Response Surface Methodology (RSM), and each trial was repeated thrice for accuracy using a fresh tool tip to eliminate tool wear effects. Analysis of Variance (ANOVA) identified significant parameters, and regression models were developed with R2 values of 97.4% (Fz), 86.64% (Ra), 88.35% (CW), and 83.96% (flank wear). A Fuzzy Inference System (FIS) dataset was used to train an Adaptive Neuro-Fuzzy Inference System (ANFIS) model, which was validated through 12 additional experiments. The ANFIS model demonstrated high prediction accuracy, with RMSE values ranging from 0.015 to 0.038 µm for Ra. Finally, a Genetic Algorithm (GA) was applied to identify optimal machining conditions: SS = 660.10 rpm, FR = 30 mm/min, and DoC = 0.3 mm, resulting in a minimum predicted surface roughness of 0.3392 µm. This integrated framework provides a robust solution for predictive quality control and process optimization in intelligent manufacturing environments.

There is a requirement to find accurate parameters to accomplish precise dimensional accuracy, excellent surface integrity and maximum MRR. This work studies the influence of various cutting parameters on output parameters like Cutting force, Surface roughness, Flatness, and Material removal rate while face milling. A detailed finite element model was developed to simulate the face milling process. The material constitutive behavior is described by Johnson-Cook material model and the damage criteria is established by Johnson-Cook damage model. The result indicate significant effects of all three cutting parameters on MRR and both feed rate and depth of cut have significant effect on cutting force. Also, feed rate has significant effect on PEEQ and none of the parameters have effect on flatness.