LM26 is a cast aluminum-silicon alloy that exhibits superior castability, moderate strength, and high corrosion resistance. Silicon is typically the primary alloying agent in aluminium alloy LM26. The present study investigates the development of hybrid composites and the machining of aluminium alloy LM26 reinforced with varying weight percentages (0.25%, 0.5%, and 0.75%) of multi-wall carbon nanotubes (MWCNTs), with a focus on sustainable machining. The casting was performed by stir casting at 700°C and 650 rpm. In this study, the Central Composite design is used to design the experiment, and Response Surface Methodology (RSM) is employed to develop a quadratic (polynomial) equation using Design-Expert software V13. The milling experiment was conducted on MWCNT-reinforced LM26 material using a milling machine, with spindle speed, feed rate, depth of cut, and MWCNT percentage as parameters, to measure surface roughness (Ra) and temperature (T). Microstructural characterization of the composite was performed. The performance characteristics were analyzed using ANOVA. This result shows that wt.% of MWCNT is an influential factor in minimizing Ra, while spindle speed affects Ra, and feed rate affects temperature. Based on the results, it has been concluded that adding 0.25% wt. of MWCNTs has improved machinability; however, increasing the MWCNT content increases Ra and temperature. To achieve better machining performance that meets sustainability criteria, a Multi-Objective Genetic Algorithm was employed to optimize the objectives. The MOGA was adopted to solve the optimization problem, yielding 21 non-dominated Pareto-optimal solutions. This study has identified several alternatives to help academics and industry develop environmentally friendly, sustainable machining techniques.

The main goal of this research is to compare the various optimization strategies (Response Surface Methodology, Taguchi, and Teaching Learning Based Optimization) for orthogonal turning of Hard to Machine materials. The workpiece material in this work is Ti6Al4V alloys. After selecting cutting speeds in the High-Speed Machining range, orthogonal turning tests are performed on the material for a specific combination of machining parameters – Depth of Cut, Cutting Speed, and, Feed Rate. A Lathe Tool Dynamometer is used to record the cutting forces from the trials. After combining Johnson Cook Material and Damage models, a comprehensive Finite Element Model is created to model the Orthogonal Turning of Ti6Al4V alloys. Experiments conducted previously validate the developed model. Three different strategies, namely RSM, Taguchi, and TLBO, were used to optimise machining parameters for minimal Cutting Force. The approaches are compared for the best combination of machining parameters and the best Cutting Force value. Analysis of Variance is used to study the impact of machining factors on Cutting Force.

The number of types of wire welding that circulate on the market and the development of welding technology, especially related to current welding, create a lack of information related to the quality of welding results for several types of materials. This can cause the strength of the welding connection to not be maximized. Therefore, this study aims to optimize the use of three types of wire welding and three current welding on the strength of the welding connection in two types of material testing using the response surface methodology. Box-Behnken, coupled with the RSM and the desirability function, was used to optimize the strength of the welding connection of the wire types (RD-46, LB-52, and RB-26), and current welding (100, 130, and 160 A) against two types of material testing (IWF-150 and ASTM-A517-G70). The strength of the welding connection observed in response included the tensile strength, Charpy impact-absorbed energy, hardness values in the welding metal and the hardness values in the main metal. Optimization of the strength of the welding connection in this study recommends the application of wire types RB-26, current welding of 100 A, and ASTM-A517-G70 material testing with the highest desirability value of 71.6%. Optimization of tensile strength, Charpy impact-absorbed energy, hardness values in welding metal, and hardness values in main metal by applying this parameter are 575.64 MPa, 110.69 J, 216.75 (HV10) and 126.6 (HV10), respectively. The results proved that an appropriate welding connection strength could be achieved using wire welding types and current welding in material testing.

In this study, several series of experiments on turning process of AISI 12L14 free cutting steel characterized by its self-lubrication and the high percentage of lead in its composition were performed to rate the influence of cutting conditions (Vc, f and ap) on the machining performance such as surface roughness, cutting force, cutting power and material removal rate. A computer generated optimal design of experiment based on the I-optimality criteria along with analysis of variance was created to study the characterizations in turning of this steel, and desirability function was utilized for the optimization. The global optimization, combined high surface quality and productivity with low cutting power consumption, gave 12 optimal setting points provided high desirability values. The obtained correlation for surface roughness, cutting force, material removal rate and cutting power were 99.4%, 95.5%, 99.7% and 94.3%, respectively.

Cutting betel nut machines are increasingly being designed by engineers using local material. However, the performance of the cutting betel nut machine is influenced by the moisture content of the betel nut and the rotational speed of the machine. In this study, the performance of cutting a betel nut machine under moisture content of betel nut and rotational speed of the machine was studied using response surface methodology (RSM) and desirability function. Central Composite Design (CCD) coupled with RSM and desirability function was employed to evaluate the impact of moisture content of betel nut (34.68–50.54%, w.b.) and rotational speed (600–1000 rpm) on machine capacity (kg/hr), efficiency (%), and losses (%) responses. The desirability function was then used to optimize moisture content and rotational speed yielding maximum machine capacity and efficiency at lower losses. Three verification experiments were run to ensure the empirical relationships were valid. Optimum requirements of process parameters have been seen at which moisture content of 50.54% (w.b.) and rotational speed of 1000 rpm was achieved in maximum machine capacity of 44.16 kg/hr at higher efficiency (92.72%) and lower losses (6.31%). The model's conclusions were very consistent with the confirmed values. The results proved that an appropriate performance of the machine can be achieved using moisture content of betel nut and rotational speed of machine cutting betel nut.

The present work deals with some machinability studies on tool wear and surface roughness, in finish hard turning of AISI D2 steel using PCBN, Mixed ceramic and coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters viz., cutting speed, cutting time and tool hardness on the two performance outputs (i.e. VB and Ra), are explored employing the analysis of variance (ANOVA).The relationship(s) between input variables and the response parameters are determined using a quadratic regression model. The results show that the tool wear was influenced principally by the cutting time and in the second level by the cutting tool hardness. On the other hand, cutting time was the dominant factor affecting workpiece surface roughness followed by cutting speed. Finally, the multiple response optimizations of tool wear and surface roughness were carried out using the desirability function approach (DFA).

The aim of this study is to evaluate the impact of factors such as cutting speed, feed rate, and depth of cut on surface roughness and Material Removed Rate (MRR) when machining in dry face milling AISI 1040 steel with coated carbide inserts GC1030 using the response surface methodology (RSM). For this purpose, a number of machining experiments based on statistical three-factor and three-level factorial experiment designs, completed (L27) with a statistical analysis of variance (ANOVA), were performed in order to develop mathematical models and to identify the significant factors of these technological parameters. Multi-objective optimization procedure for minimizing Ra, Ry and Rz and maximizing MRR using desirability approach has been also implementented. The current study was also carried out to investigate the tool life of the inserts. The models found the relationship between the cutting parameters (Vc, fz and ap) and the studied technological parameters. It has been found that the cutting speed was the most affecting surface roughness which is due to the geometry of the insert which has a scraping edge and enables to obtain low roughness even at important feed rate, followed by the feed rate and the depth of cut at the end. The optimal combination of cutting parameters were cutting speed of 314 m/min, feed rate of 0.16 mm/tooth and depth of cut of 0.6 mm with a composite desirability of 0.924.

This paper suggests an advanced hybrid multi output optimization technique by applying weighted principal component analysis (WPCA) incorporated with response surface methodology (RSM). This investigation has been carried out through a case study in CNC turning of Aluminum alloy 63400 for surface roughness (Ra) and tool vibration (db) optimization. Primarily, input parameters such as spindle speed (N), feed rate (S) and depth of cut (t) are designed for experiment by using RSM Box-Behnken methodology. The aluminum alloy workpieces are machined by using coated carbide tool (inserts) in dry environment. Secondly, the empirical model for the responses as the functions of cutting parameters are obtained through RSM technique and the adequacy of the models have been checked using analysis of variance (ANOVA). Finally, the process parameters are optimized using WPCA technique. The confirmatory experiment has been performed using optimized result and it reveals that multiple response performance index (MPI) value was increased by 0.2908 from initial setting. The increases in MPI value indicates that the aforesaid optimization methodology is suitably acceptable for multi response optimization for turning process.

The present work concerns an experimental study of turning with coated cermet tools with TiCN-TiN coating layer of AISI 52100 bearing steel. The main objectives are firstly focused on the effect of cutting parameters and coating material on the performances of cutting tools. Secondly, to perform a Multi-objective optimization for minimizing surface roughness (Ra) and maximizing material removal rate by desirability approach. A mathematical model was developed based on the Response Surface Methodology (RSM). ANOVA method was used to quantify the cutting parameters effects on the machining surface quality and the material removal rate. The results analysis shows that the feed rate has the most effect on the surface quality. The effect of coating layers on the surface quality is also studied. It is observed that a lower surface roughness is obtained when using PVD (TiCN-TiN) coated insert when compared with uncoated tool. The values of root mean square deviation and coefficient of correlation between the theoretical and experimental data are also given in this work where the maximum calculated error is 2.65 %.

This paper describes a comparison of surface roughness between ceramics and cubic boron nitride (CBN7020) cutting tools when machining of AISI H11 hot work steels treated at 50 HRC. Plan is designed according to Taguchi’s L18 (21×32) orthogonal array. The response surface methodology (RSM) and analysis of variance (ANOVA) were used to check the validity of multiple linear regression models and to determine the effects, contribution, significance and optimal machine settings of process parameters, namely, cutting speed, feed rate and depth of cut on machining parameters on the Ra and Rt. The results of this research work showed that, the feed rate was found to be a dominant factor on the surface roughness, followed by the cutting speed, lastly the depth of cut. The CBN7020 cutting tool showed the better performance than that of ceramic based cutting tool. In addition, the combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness.