Narrow Results

Abrasive water jet (AWJ) machining does not impact material quality and modern machining processes. The trials follow the Taguchi L25 orthogonal array and the workpiece material is Ti grade 5 alloy. The optimal process parameters are determined using response surface methodology and these parameters include transverse speed (TS), pressure (P), abrasive flow rate (AFR) and stand-off distance (SoD). The aim of this research is to identify, evaluate, and improve the impression of AWJ machining parameters on response variables (machining time [MT], surface roughness [SR], and hardness). Achieving this objective involves utilizing grey relational analysis in conjunction with principal component analysis. TS has the least influence on performance while AFR has the greatest. The best configuration for the lowest MT, SR, and Highest Hardness Rockwell C scale (HRC) is (AFR = 600 g/min, SoD = 4 mm, P = 20 kpsi, and quality = 5 approximately, speed 75 mm/min). Here, quality = 5 corresponds to a TS of approximately 75 mm/min and the factor AFR has the greatest impact on material machining performance, according to the analysis of variance, followed by factors P, standoff distance, and TS. The chronological sequence of influence is as follows: AFR > P > SoD > TS with contributions of 85.45%, 8.06%, 2.90%, and 2.09%, respectively. Experiments showed that the proposed methodology enhanced AWJ machining performance by 0.3665 which is represented as multi-response performance index.

The selection of appropriate drilling parameters is essential for improving productivity and part quality, therefore, this work mainly concentrates on the investigation of drilling time, burr height, burr thickness, roundness and surface roughness. The drilling experiments were carried out on Magnesium (Mg) AZ91 with High Speed Steel (HSS) tool using the Vertical Milling Machine (VMM). The parameters reckoned are spindle speed and feed rate. Artificial Neural Network (ANN) was concerned with the building of the model that will be used to forecast the responses following the consideration of Response Surface Methodology (RSM). Conventional method of modeling (RSM) yields poorer results which redirected the study with ANN. The Genetic Algorithm (GA)-based ANN has been reckoned for developing the model. With two nodes in the parameter layer and seven nodes in the response layer, six different networks were constructed using variety of nodes in the hidden layers which are 2–6–7, 2–7–7, 2–8–7, 2–6–6–7, 2–7–6–7 and 2–8–6–7. It is observed that the 2–8–7 network offers the best ANN model in predicting the various responses. The prediction results ensure the reliability of the ANN model to analyze the effect of drilling parameters over the various responses.

Owing to the advancement in the field of materials, different range of grades have been developed. The machinability examination of these newer grades must be carried out for future applications. One such newer grade of magnesium with AZ31 is deemed for study during the drilling process. The independent parameters considered are spindle speed (SS), feed rate (FR) and drill bit diameter (DBD). The dependent parameters considered are burr height (BH), burr thickness (BT), drilling time (DT) and surface roughness (SR). Improving hole accuracy is essential for manufacturing superior products, which is discussed in this work. At the same time, the machining time has also to be minimized to increase the production rate. With these objectives, the experimental investigation is made. Further, an analytical model for predicting the responses is developed; later, optimization is carried out to obtain the desired responses through the desirability function approach. The multi-objective optimization suggests the SS of 1100rpm, the FR of 0.198mm/rev., and the DBD of 6mm for reducing the entire dependent is reckoned.

Laser Beam cutting is a type of non-conventional machining process in which the removal of materials takes place due to the melting and vaporization of material when the laser beam comes in contact with it. This work examines the impact of the cut quality characteristics of the SS347 material and to find reduced surface roughness, machining time and heat affected zone by laser beam cutting. The cutting process was assisted by CO₂ gas pressure. Power, standoff distance, speed and CO₂ gas pressure are the cutting parameters considered for this study and the output parameters measured are machining time, heat affected zone and surface roughness. In accordance with L-9 orthogonal arrays, the experiments were planned. Analysis of Variance has been used to study how input functions influence output functions which revealed that speed (59.75% and 89.75%) is the significant factor for machining time and surface roughness while power (91.27%) was the dominant factor for heat affected zone. Gas pressure did not have much influence in the output parameters. The mathematical expressions of the output are used as input for the multi-objective function of the genetic algorithm. Optimal solutions are compared to hybrid and without-hybrid functions. It is found that the hybrid function shows a higher performance compared to the without hybrid function.