Desirability Function Analysis (DFA) in Multiple Responses Optimization of Abrasive Water Jet Cutting Process

This paper introduces optimization of machining parameters for high-pressure abrasive water jet cutting of Hardox 500 steel utilizing desirability function analysis (DFA). The tests were carried out according to the orthogonal matrix (Taguchi) L9. The control parameters of the process such as pressure, abrasive flow rate, and traverse speed was optimized under multi-response conditions namely cutting depth and surface roughness. The optimal set of control parameters was established on the basis of the composite desirability value obtained from desirability function analysis and the significance of these parameters was determined by analysis of variance (ANOVA). The effects show that optimal sets for high cutting depth and small surface roughness is high pressure, middle abrasive flow rate, and small traverse speed. A confirmation test was also leaded to validate the test results. Results of the research have shown that machining efficiency at keeping good level quality of cut surface can be improved this approach.

Optimization of multiple quality characteristics for end milling under dry cutting environment using desirability function

Milling AA6082T6 materials is a difficult venture because of their heterogeneity and a slew of problems, inclusive of surface roughness, that get up for the duration of the machining method and are connected to the material’s homes and slicing settings. The optimization of machining parameters is a crucial section inside the manufacturing method. This research introduces a unique approach for improving machining settings whilst milling aluminum alloy. A technique notorious as desirability function analysis (DFA) turned into worn to optimize machining parameters. DFA is a effective tool for optimizing multi-reaction problems. Milling research for aluminum alloy were completed using tungsten carbide end milling inserts in dry situations, based totally on Taguchi’s L9 orthogonal array. Multi-response issues, along with machining pressure and surface roughness, are used to optimize machining parameters including feed charge, spindle speed, and depth of reduce. person desirability values from the desirability characteristic analysis are used to create a composite desirability cost for the multi-responses. The most effective ranges of parameters had been discovered based at the composite desirability fee and substantial contribution of parameters has been determined the usage of analysis of variance.

MACHINABILITY ASSESSMENT OF SHAPE MEMORY ALLOY NITINOL DURING WEDM OPERATION: APPLICATION POTENTIAL OF TAGUCHI BASED AHP–DFA TECHNIQUE

This paper exemplifies the feasibility of expanding a multi-criteria decision-making (MCDM) method to select optimum process parameters during the wire electrical discharge machining (WEDM) of nitinol. The application potential of combined desirability function analysis (DFA) and analytical hierarchy process (AHP) has been reported. Nitinol, a shape memory alloy (SMA), can memorize or retain its original shape when subjected to thermo-mechanical or magnetic loads. Four key input variables, like pulse on time ([Formula: see text], pulse off time ([Formula: see text], wire tension (WT), and wire feed (WF) have been studied to optimize three correlated responses, like kerf width, material removal rate (MRR), and surface roughness ([Formula: see text]. Process parameter permutations [Formula: see text]s, [Formula: see text]s, [Formula: see text] kg-F and [Formula: see text][Formula: see text]m/min were found to yield the optimum results. For the desired kerf width, MRR and [Formula: see text], the optimum process parameters were also achieved expending Taguchi’s signal-to-noise ratio. Validation results affirmed that the MCDM approach, AHP–DFA is a proficient strategy to select optimal input parameters for a preferred output eminence for WEDM of nitinol.

Machinability Studies and Optimization of AA 6082/Fly Ash/Carbonized Eggshell Matrix Composite

Machinability studies of aluminium matrix composites (AMCs) is a necessary investigation required to understand their behaviour during machining to produce components effectively and efficiently. This established need has led to the investigation into the machinability of AA 6082 reinforced with 2.5 wt.% fly ash and 2.5 wt.% carbonized eggshell fabricated via stir casting. The studied machinability indices were material removal rate (MRR), cutting temperature, built-up edges (BUE) formation and chip morphology while the selected inputs were cutting speed (100 mm/min, 200 mm/min, 300 mm/min), feed (0.1 mm/rev, 0.2 mm/rev, 0.3 mm/rev) and depth of cut (0.5 mm, 1 mm, 1.5 mm). For the experimental design, the L9 orthogonal array was preferred to create 9 experimental runs. The analysis of the built-up edges showed that it increased at lower cutting speeds and increased feed and depth of cut. The examination of the produced chips after each experimental run showed the presence of c-shaped, helically shaped and ribbon-shaped chips. The analysis of variance (ANOVA) for both MRR and cutting temperature indicated that the depth of cut was the most influential factor on both responses. Multi-objective optimization using desirability function analysis showed that the optimum combination of parameters was 300 mm/min, 0.2 mm/rev and 1.0 mm for the cutting speed, feed and depth of cut respectively. The ANOVA of the composite desirability indicated that the cutting speed was the most contributing factor.

Multi-objective optimization of magnetic abrasive finishing by desirability function with genetic algorithm

Thermal stability and Surface hardness of the super-finished surface is a very important aspect to preserve the surface texture of workpiece in the MAF process. In this present study, the multi-objective optimization of EN-31 finished through the MAF process. “Increase in Temperature” and “Increase in Hardness” are considered for optimization to diminish their impact on the super-finished surface of EN-31. In present work Desirability function analysis (DFA) has been used to optimize the desired responses of the MAF process. Experiments were designed according to Taguchi L9 orthogonal array for the finishing of EN-31. The experiment results are processed using DFA and Desirability fitness function is established to convert the single response to multi-response. Genetic Algorithm (GA) is used to enhance the results of DFA and the regression model was developed to obtain the objective function of Genetic algorithm. Smaller-the-best criteria were used for ‘Increase in Temperature’ and ‘Increase in Hardness’ for obtaining favorable process parameters. The best optimal parametric combination is obtained by using the GA-DFA hybrid approach is at 2.5 mm (working gap), 20 gm (abrasive weight), and 2.0 A (Current) and 300 rpm (rotational speed).

Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool

Clean technological machining operations can improve traditional methods’ environmental, economic, and technical viability, resulting in sustainability, compatibility, and human-centered machining. This, this work focuses on sustainable machining of Al-Mg-Zr alloy with minimum quantity lubricant (MQL)-assisted machining using a polycrystalline diamond (PCD) tool. The effect of various process parameters on the surface roughness and cutting temperature were analyzed. The Taguchi L25 orthogonal array-based experimental design has been utilized. Experiments have been carried out in the MQL environment, and pressure was maintained at 8 bar. The multiple responses were optimized using desirability function analysis (DFA). Analysis of variance (ANOVA) shows that cutting speed and depth of cut are the most prominent factors for surface roughness and cutting temperature. Therefore, the DFA suggested that, to attain reasonable response values, a lower to moderate value of depth of cut, cutting speed and feed rate are appreciable. An artificial neural network (ANN) model with four different learning algorithms was used to predict the surface roughness and temperature. Apart from this, to address the sustainability aspect, life cycle assessment (LCA) of MQL-assisted and dry machining has been carried out. Energy consumption, CO2 emissions, and processing time have been determined for MQL-assisted and dry machining. The results showed that MQL-machining required a very nominal amount of cutting fluid, which produced a smaller carbon footprint. Moreover, very little energy consumption is required in MQL-machining to achieve high material removal and very low tool change.

Optimization of the ANNs Predictive Capability Using the Taguchi Approach: A Case Study

Artificial neural networks (ANNs) are a valid alternative predictive method to the traditional statistical techniques currently used in many research fields where a massive amount of data is challenging to manage. In environmental analysis, ANNs can analyze pollution sources in large areas, estimating difficult and expensive to detect contaminants from other easily measurable pollutants, especially for screening procedures. In this study, organic micropollutants have been predicted from heavy metals concentration using ANNs. Sampling was performed in an agricultural field where organic and inorganic contaminants concentrations are beyond the legal limits. A critical problem of a neural network design is to select its parametric topology, which can prejudice the reliability of the model. Therefore, it is very important to assess the performance of ANNs when applying different types of parameters of the net. In this work, based on Taguchi L12 orthogonal array, turning experiments were conducted to identify the best parametric set of an ANNs design, considering different combinations of sample number, scaling, training rate, activation functions, number of hidden layers, and epochs. The composite desirability value for the multi-response variables has been obtained through the desirability function analysis (DFA). The parameters’ optimum levels have been identified using this methodology.