Multi-response Optimization Technique Research Articles

Corner cutting accuracy for thin-walled CFRPC parts using HS-WEDM

Carbon Fiber-Reinforced polymer (CFRP) composite parts with thin-walled corners are in great demand in aircraft, cars, and precision instruments. Nonetheless, the fabrication of these parts is difficult due to their low stiffness. High-speed WEDM is an advanced technique for cutting thin CFRP components as it is a non-contact method for removing materials. Nonetheless, testing results demonstrate an unavoidable deformation in the thin-walled corners of the CFRP composite. The objective of this study is to improve the accuracy of corners in thin-walled CFRP composite parts. The research utilized a Taguchi L16 orthogonal array to investigate the influence of various process parameters, including pulse-on duration (Pon), pulse-off duration (Poff), and input current (I), as well as the parameter CFRP plate thickness (T), on corner inaccuracy. The CFRP thickness varied between 0.5, 1.0, 1.5, and 2.0 mm, and the corner angles examined were 30°, 60°, 90°, and 120°. Additionally, a second-order polynomial regression model was used to determine the correlation between the process parameters and corner inaccuracy at various corner angles. Also, a multi-response optimization technique using a composite desirability coupled with a generalized reduced gradient were used to find the optimal process combination across various CFRP thicknesses. According to the research findings, the most relevant process parameters impacting corner accuracy at different angles were the pulse-on duration and input current. To achieve accurate corners with different angles, the optimal process parameters were identified: Pon (40µs), Poff (15µs), and I (4A) for CFRP thicknesses 0.5 and 1.0mm, and Pon (45μs), Poff (30μs), and I (2A) for thicknesses 1.5 and 2.0mm.

Statistical study and investigation of the cut-resistant and thermo-physiological performance of protective gloves: a new prospective

Objectives. This research investigated the performance properties of protective gloves alongside improvement in thermo-physiological comfort properties. Methods. Knitted gloves prepared from filament stainless steel, filament Kevlar, staple spun Kevlar and filament glass were used in the core, while 100% viscose rayon and 50/50% polyester/viscose rayon were used in the sheath. Gloves were tested for cut resistance, tear resistance, puncture resistance and abrasion resistance as the prime focus, and thermo-physiological comfort properties were also tested. In this research, a multi-response optimization technique, i.e., principal component analysis, was applied to identify the best yarn combination for gloves based on the aforementioned properties. Thermal images were also taken in constant ambient conditions for temperature distribution maps across the hand’s surface. Results. All of the results were evaluated statistically with analysis of variance, and concluded that the effects of the core yarn on thermo-physiological properties were less significant. Conclusion. The results revealed that samples having dual-core yarn exhibited better in terms of overall properties. The sample having dual-core filament Kevlar and staple spun Kevlar ranked the best. In addition, developed samples exhibited better comfort properties than the control sample.

New Dimensions of Various Interlock Knitted Derivatives

ABSTRACT This study investigated the derivatives of interlock knitted structures by changing stitch type, location, and their percentage (%) on different performance properties. In the current study, 11 different interlock knit structures were developed by using multiple combinations of knit, tuck, and miss stitches and different types of testing, i.e., physical, mechanical, thermo-physiological comfort, tactile comfort, and dimensional properties, were performed and investigated. Analysis of variance (ANOVA) was performed to determine the statistical significance of structure on the properties. Based on the results, it was concluded that stitch type, percentage, and location have a prominent influence on the abovementioned properties of knit fabrics, which is also elaborated by interaction plots. The present research used a multi-response optimization technique, i.e., principal component analysis (PCA), to identify the best structure for industrial applications like uniforms, based on the optimization of abovementioned properties. Based on the results, it was suggested that a sample with an interlock cross relief structure was found to be the best in all aspects among all samples, followed by interlock pique and double tuck, which were ranked as 2nd and 3rd, respectively. This paper highlights the possible application of different interlock knitted structures according to end use for manufacturers.

Multi-Response Optimization of Drilling Parameters for Aluminum Metal Matrix Composite Using Entropy Weighted Grey Relational Analysis

The present work aims to understand the effect of drilling parameters (drill speed and feed rate) during the drilling of saffil fiber-reinforced Al metal matrix composite (MMC) under dry conditions. The effect of drilling parameters on individual response characteristics is evaluated and the optimum drilling parameters are also investigated using a multi-response optimization technique known as Entropy Weighted Grey Relational Analysis (EWGRA). The drilling parameter optimization has been performed with aim of minimizing the surface roughness of drilled hole, roundness error in drilled hole and feed force during drilling. Weights were assigned to individual responses with the Entropy weight method and Grey relational grades were calculated to obtain the optimal level for drilling parameters. To achieve the minimum values for all output responses the optimal value of drill speed is 22 m/min and the feed rate is 0.075 mm/rev.

Optimization of seismic performance in waste fibre reinforced concrete by TOPSIS method

For a sustainable environment and to tackle the pollution problem, industrial wastes can be used in concrete composite materials. This is especially beneficial in places prone to earth quack and lower temperature. In this study, five different types of waste fibres such as polyester waste, rubber waste, rock wool waste, glass fibre waste and coconut fibre waste were used as an additive in 0.5% 1%, and 1.5% by mass in concrete mix. Seismic performance related properties of the samples were examined through evaluation of compressive strength, flexural strength, impact strength, split tensile strength, and thermal conductivity. Results showed that, impact strength of the concrete significantly improved by the addition of fibre reinforcement in concrete. Split tensile strength and flexural strength were significantly reduced. Thermal conductivity was also influenced by addition of polymeric fibrous waste. Microscopic analysis was performed to examine the fractured surfaces. In order to get the optimum mix ratio, multi response optimization technique was used to determine the desired level of impact strength at an acceptable level of other properties. Rubber waste was found to be the most attractive option followed by coconut fibre waste for the seismic application of concrete. The significance and percentage contribution of each factor was obtained by Analysis of variance ANOVA (α = 0.05) and pie chart which showed that Factor A (waste fibre type) is the main contributor. Confirmatory test was done on optimized waste material and their percentage. The order preference similarity to ideal solution (TOPSIS) technique was used for developed samples to obtain solution (sample) which is closest to ideal as per given weightage and preference for the decision making. The confirmatory test gives satisfactory results with error of 6.68%. Cost of reference sample and waste rubber reinforced concrete sample was estimated, which showed that 8% higher volume was achieved with waste fibre reinforced concrete at approximately same cost as pure concrete. Concrete reinforced with recycled fibre content is potentially beneficial in terms of minimizing resource depletion and waste. The addition of polymeric fibre waste in concrete composite not only improves seismic performance related properties but also reduces the environmental pollution from waste material which has no other end use.

RSM design-based hybrid approach to multi-response optimization in milling Ti-6Al-4 V alloy: A comparative study

The Selection of appropriate process parameters in milling Ti-6Al-4 V alloy to reduce energy and power consumption while maintaining product quality and process stability is challenging. This study aims to optimize process parameters in milling Ti-6Al-4 V alloy with five primary criteria i.e., average cutting force, shear stress, specific cutting energy, power consumption, and average surface roughness. A total of 26 experimental tests was carried out with the variation of process parameters such as cutting speed (m/min), feed rate (mm/min), and depth of cut (mm) based on the Box-Behnken design (BBD) matrix of response surface method (RSM) under dry and high-pressure cooling (HPC). The criteria importance through the inter-criteria correlation (CRITIC) method was used to determine the relative weights of each response. At the same time, the evaluation of optimum process parameters was done by hybrid multi-response optimization (MRO) techniques such as complex proportional assessment (CRITIC-COPRAS), additive ratio assessment (CRITIC-ARAS), combined compromise solution (CRITIC-COCOSO) and technique for order of preference by similarity to ideal solutions (CRITIC-TOPSIS). After the evaluation, a cutting speed of 32 m/min, feed rate of 22 mm/min, and 0.75 mm depth of cut with HPC were selected as the optimum process parameters. Due to the lack of an identical solution, the sensitivity of the used MRO methods was checked out with the weight variation of the selected criteria. TOPSIS was noticed as the most robust MRO method with less sensitivity. Spearman’s Correlation coefficient was used to assess the correlation among the used hybrid MRO methods.

Comparative study of nano fluid lubricant in face milling through DFA approach

Material research in industry is a prominent part of their investigation as the customer’s demand kept changing according to their requirement. To process a hard material is always a stimulating charge for the industry. One of such hard material EN31 has been kept under consideration here in this work. The idea in the standing work is to improvise the deduction rate of material and to observe the power consumption rate. These two responses have been focussed under controlled machining environment with or without Nano fluid particles. Thus a comparative analysis provided the significance by exploiting the Nano particles in lubricant along with least amount of lubrication. As there are two output responses, a multi response optimization technique has to be adopted. Hence the multi response system desirability functional analysis (DFA) has been practiced. Depth of cut (DC) analysed as the best affecting input for both environment situations. The experimental array was created using the Taguchi approach. A total of eighteen number of try-outs were executed and on comparing it has been found that no big difference seen in material removal rate (MR) whereas power consumption (PC)gets lower on using Nano fluid particles in minimum quantity lubrication (MQL). For instance, in seventeenth experiment, PC reduced from 965 to 740 W and MR only increases from 420 to 435 mm3/min.

Investigation of Al-6061 alloy in face milling through DFA approach

The manufacturing of the components through the machining process considering the higher production rate and desired quality is always a matter of concern for the industrial field. Also, achieving this with a green machining process using near-dry cooling strategies such as minimum quantity lubrication (MQL), cryogenic cooling etc. is a challenging task. In this work, an attempt has been made to implement the MQL for the machining of the aluminium alloy Al-6061 workpiece. The outputs such as temperature and surface roughness have been focused under the controlled machining environment. As there are two output responses, a multi-response optimization technique system desirability functional analysis (DFA) has been exploited. The experimental array was created using the Taguchi approach. A total of eighteen try-outs were executed and the best setting achieved in terms of the input factor levels is CFR = 150 ml/hr, Speed = 1000 rpm, 175 ml/hr, feed of tool = 60 mm/min and cutting depth = 0.2 mm. The values of temperature and surface roughness are noted at optimal settings as 60 °C and 0.32 µm respectively with an enlargement of 2.67 % in the multi-response factor.

Multi-objective optimization of a fluid catalytic cracking unit using response surface methodology

Abstract In oil refineries, fluid catalytic cracking (FCC) is a major unit consisting of several process variables and multiple products. Since FCC units are given prime importance as they are contributing a large share in profits, the optimal operation of FCC is always desirable while considering the changing economic scenarios with respect to the products. However, optimization of FCC is quite challenging due to the complex nature of the process. In this work, using Aspen HYSYS V9® catcracker module, process data of FCC was obtained using central composite design (CCD). Second order regression equations for the selected responses were obtained using Analysis of variance (ANOVA) approach. The interaction effects of feed flow, feed temperature, feed pressure, air blower discharge temperature and catalyst circulation rate on responses (yield of products) were presented. Further, the optimization was performed based on a multi-response optimization technique in the Design expert software and the optimal values of the input variables were obtained for the chosen objectives (representing various operation scenarios). The optimal operation scenarios that were obtained for the objectives were validated successfully. This work highlights the use of statistics based soft computing techniques for the optimization of complex chemical engineering operations such as FCC.

Investigation of machining characteristics during micro-hole fabrication in CFRP composite by ECDM process

Carbon fiber reinforced polymer (CFRP) composites have gained significant popularity in the field of aerospace, automotive, electronic, and sports due to its versatile properties. Although, fabrication of micro-holes in CFRP composite is troublesome by the conventional processes because of its anisotropic and abrasive behaviour. To overcome this difficulty, recently, an unconventional hybrid process such as electrochemical discharge machining (ECDM) is extensively used to machine such types of materials. This paper highlights the experimental and statistical analysis of machined micro-holes which was fabricated in CFRP composite using ECDM process. The experimentation was performed according to Taguchi’s L9 orthogonal array. The input parameters were (a) voltage, (b) tool rotation, (c) tool travel rate, and (d) duty cycle, each at three levels. Hole overcut and taper was measured as output responses. TOPSIS-Entropy based multi-response optimization technique was applied to optimize the chosen parameters. The hole overcut and taper was measured and analysed using SEM images. The parametric combination of 50 V, 360 RPM, 0.5 mm min−1, and 60% resulted minimum overcut and taper of 227 μm and 72 μm, respectively. The morphology of the machined hole exhibited better surface characteristics at a high tool travel rate and low duty cycle. Tool wear in terms of lateral wear and flank wear was observed during machining at high voltage and high duty cycle. From the TOPSIS Entropy analysis, the optimal condition for both overcut and taper was found as 50 V, 360 RPM, 0.5 mm min−1, and 60%.

Multicriteria optimal design of emamectin benzoate microparticles obtained by spray drying and ionic gelation

Emamectin benzoate (EB) is an antiparasitic used to control Caligus rogercresseyi in Chile. However, it has lost efficacy, and the parasite has been exposed to a sublethal dose. Microencapsulation has been suggested as an alternative method to protect and control the release of poorly absorbed drugs to ensure their lethal doses. Accordingly, design of experiments (DOE) and response surface methodology (RSM) were applied to find optimal conditions for EB's ionic gelation (IG) and spray drying (SD) microencapsulation. We used multiobjective (MOO) and multi-response optimization techniques as the desirability function (DFA) to obtain optimal conditions that produce microparticles that satisfy several criteria, such as low gastric digestion (GD) and high yield (Y), encapsulation efficiency (EE), load capacity (LC), and intestinal digestion (ID). The optimization process prioritized the digestion responses and was constrained according to a mass balance. MOO generated theoretical solutions that were better than any of the DOE experimental solutions. Both optimization methods achieved a more balanced performance than the responses obtained in the experimental design. Each optimization method produced better experimental responses than the other in some responses. In SD, DFA yielded higher LC, GD, and ID than MOO by 7.5%, 9.3%, and 2.1%, respectively. In contrast, MOO obtained higher Y and EE than DFA by 6.2% and 10.1%, respectively. In IG, the DFA method yielded a solution with better responses than MOO in LC (3.7%), GD (7.4%), and ID (3.2%), while the MOO solution was better in Y (14.2%) and EE (19.3%). Both multicriteria optimization techniques were suitable for obtaining optimal solutions; however, neither proved superior in all cases.

Exploration of Effects of Graduated Compression Stocking Structures on Performance Properties Using Principal Component Analysis: A Promising Method for Simultaneous Optimization of Properties.

This paper focuses on the comfort properties of graduated and preventive compression stockings for people who work long hours in standing postures and for athletes for proper blood circulation. The present study was conducted in order to investigate the effects of the yarn insertion density and inlaid stitches on the performance of the compression stockings. The effects of these parameters on the thermo-physiological comfort properties were tested with standard and developed methods of testing. All compression stockings were maintained with class 1 pressure as per German standards. The structural parameters of the knitted fabric structures were investigated. The stretching and recovery properties were also investigated to determine the performance properties. The theoretical pressure was predicated using the Laplace’s law by testing the stockings’ tensile properties. The compression interface pressures of all stockings were also investigated using a medical stocking tester (MST) from Salzmann AG, St. Gallen, Switzerland. Correlation between the theoretical pressures and pressures measured using the MST system were also assessed. The current research used a multi-response optimization technique, i.e., principal component analysis (PCA), to identify the best structure based on the optimalization of the above-mentioned properties. The results also revealed that samples with higher insertion density levels exhibit better comfort properties. The results showed that sample R1 was the best sample, followed by R2 and P. In addition, all developed stocking samples exhibited better comfort properties than the control sample from the market.

Parametric optimization of durable sheeting fabric using Taguchi Grey Relational Analysis

ObjectivesThe present work describes the application of Taguchi Grey Relational Analysis in order to optimize the durability of sheeting fabrics. The existing study investigates the multi-response optimization of certain parameters of yarn process including FL, RS and RT on tensile strength, tear strength and abrasion resistance of durable fabric sheets after washing. MethodsIn the development process L9 orthogonal array in Taguchi design was used. The results have been analyzed by using statistical multi-response optimization technique, grey relational analysis to set the process parameters and to decide the simultaneous optimization of responses including tensile strength, tear strength and abrasion resistance. In addition, the analysis of variance (ANOVA) was used to determine the most significant factors. Reference sheeting fabric with same construction parameters was also tested. ResultsThe results show a greater improvement in parameters of developed samples quality. The experimental results show parameter fiber length has the most significant effect on the multiple performance characteristics. ConclusionsThe developed fabric sheet having durable life would help to reduce consumption of resources of both consumers and manufacturers. Manufacturers can have great chance to establish their image and can increase the profit margin over products. Therefore, the integration of grey relational analysis and the Taguchi Method can be applicable for the optimization of process parameters and help to conserve resources by extending the lifetime of sheeting fabrics.

Study on the performance and emission characteristics of pyrolytic waste plastic oil operated CI engine using response surface methodology

Depletion of fossil fuels, strict pollution rules, and potential generation of waste plastics strive to focus another facet of value addition by conversion of plastic wastes into alternate fuel for a diesel engine. The current study is intended to optimize the combined influences of engine load, fuel blend, and compression ratio on the performance and emission of a normal diesel engine fueled with waste plastic oil diesel (WPO) blend by using response surface methodology (RSM). Optimum engine performance i.e.maximum brake thermal efficiency and minimum NOx emissions are established using the technique of response surface methodology concerning three different engine input parameters engine load, compression ratios, and fuel blend ratio. The compression ratio of 17, 10% of WPO-diesel blending at full load conditions are found to be optimum input parameters. And accordingly, the brake thermal efficiency of 28.8% and NOx emission of 162.45 ppm are obtained under multi-response optimization techniques. A sensitivity analysis has also been accomplished to clarify the significant contribution of different parameters and their orders of importance. The compression ratio has resulted as the most significant factor followed by load and % fuel blend.

GRA based optimization of tool vibration and surface roughness in face milling of hardened steel alloy

In current study, an experimental research has been carried out for the enhancement in process variables during the machining of EN31 steel. Although the effect of the process parameters such as cutting speed, feed rate and depth of cut on responses like surface roughness has been presented by many researchers, still there is a scope of improvement in determining effect of these process variables using an appropriate multi-response optimization technique. Along with the machining parameters, cooling concentration in terms of minimum quantity lubrication (MQL) is also considered. The output responses viz. surface finish achieved during machining and the effect of tool vibration are evaluated. A comprehensive analysis between the input parameters and output responses by using hybrid optimization technique consisting of Taguchi based orthogonal array (TGRA) and grey relational analysis has been accomplished.

Multiresponse optimization of end milling process parameters on ZE41A Mg alloy using Taguchi and TOPSIS approach

The effects of CNC end-milling process parameters on the machinability of Mg-alloy are discussed in the present investigation. The optimization of the process parameters namely cutting speed, feed rate and depth of cut has been carried out for material removal rate (MRR) and thermal stresses (TS) using Taguchi design of experiment and TOPSIS approach. The milling parameters have shown significant role towards the machining attributes. Analysis of variance (ANOVA) used to evaluate and analyse the relative importance of milling process parameters. For single response optimization, the most significant factor for MRR was feed rate and for thermal stress TS, it was cutting speed. However, feed rate has the greatest impact on preference value (PI), accounting for 71.85% of contribution to multi-response optimization. The recommended milling process parameters have determined to be optimum at cutting speed: 1200 RPM, feed rate: 100 mm/min and depth of cut: 1.25 mm by using the multi-response optimization technique.

Mathematical Modeling & Multi Response Optimization for Improving Machinability of Alloy Steel Using RSM, GRA and Jaya Algorithm

In order to minimise the difficulties associated in selecting conventional coolants in any machining, cutting fluids like vegetable based oils can serve as a viable alternative. Vegetable based oils when used in combination with eco-friendly techniques like MQL/NDM can have a major impact in any machining. In the present paper, performance characteristics of surface roughness and tool wear in machining of EN 36 steel alloy under Near Dry machining conditions/ Minimum quantity lubrication using vegetable based oil lubricant is studied. The input parameters like MQL flow rate, speed, feed and depth of cut for 5 levels are used in the CCD approach of Response surface methodology. For improving the machinability of alloy steel and to predit the values a regression equation is designed and developed between the input parameter and the output parameters. A multi-response optimum model for the output responses was also developed using RSM, GRA and JAYA algorithm, It was observed from the experiment results that JAYA algorithm has been proved the best multi-response optimization technique when compared to grey relational analysis and RSM.

An improvement in drilling of SiCp/glass fiber‐reinforced polymer matrix composites using response surface methodology and multi‐objective particle swarm optimization

AbstractThe growing dominance in terms of industrial applications has helped polymer‐based composite materials in conquering new markets relentlessly. But the presence of fibrous residuals and abrasive particles as reinforcement in polymer matrix composites (PMCs) affects the output quality characteristics (OQCs) of microdrilling operations. The OQC aims at reducing overcuts and momentous material removal rate (MRR). In such perception, multi‐objective particle swarm optimization (MOPSO) evident to be a suitable optimization technique for prediction and process selection in manufacturing industries. The present paper focuses on multi‐objective optimization of electrochemical discharge drilling parameters during drilling of SiCp and glass fiber‐reinforced PMCs using MOPSO. The response surface methodology (RSM)‐based central composite design was used for the experiment planning. Electrolyte concentration, interelectrode gap, duty factor, and voltage were used as process parameters, whereas MRR and overcut were observed as OQCs. The obtained experimental results were initially optimized by RSM‐based desirability function and later with multiresponse optimization technique MOPSO to achieve best possible MRR with lower possible overcut. The comparative analysis proves that OQCs can be effectively improved by using MOPSO.

Investigation on electric erosion behavior of nickel-based super alloy (Waspaloy: Ni, Cr, Co, Mo, Ti, Al) using response surface methodology

Waspaloy is one of the nickel-based alloys and its application is mostly positioned in the aerospace industry owing to its strength retaining capacity at higher temperature, better corrosion resistance, and toughness. Multi-response optimization technique, response surface methodology (RSM) was adopted to optimize the electrical discharge machining parameters during machining of Waspaloy. Various control factors such as peak current, voltage, duty cycle, and flushing pressure were selected with three levels. The material removal rate (MRR) and surface roughness (SR) were selected as response parameters for investigating the machining characteristics. A central composite design with five center points was used for experimental design. The best optimal set of control parameters were identified for the higher MRR and lower Sr The experiments have been validated by using analysis of variance and a confirmation test was conducted. From RSM, the optimum machining conditions for drilling the hole, namely, voltage: 125 V, flushing pressure: 0.8 kg cm−2, and peak current: 22 A and duty cycle of 20% have been identified. Further, this investigation highlights the formation and the effect of the recast layer by using scanning electron microscopy.

Optimization of friction stir back extrusion mechanical properties and productivity of magnesium AZ31-B seamless tubes

Friction Stir Back Extrusion (FSBE) is a new sustainable manufacturing process capable of producing fine grained lightweight tubular shapes. The objective of this work is to investigate the impact of rotational speed and feed rate on the mechanical properties, cycle time and power consumption of friction stir back extruded Mg AZ31-B tubes and determine the optimal settings. FSBE experiments and tensile tests were performed to characterize the mechanical properties of the produced tubes. The investigation is conducted utilizing Response Surface Methodology (RSM) and desirability multi-response optimization technique. Results suggest that ultimate tensile strength and toughness are impacted by both rotational speed and feed rate and are more sensitive to speed. However, both process parameters did not show a significant statistical impact on elongation. Optimal parameters for mechanical properties were identified at a rotational speed of 1767 rpm and feed rate of 102 mm/min. The optimal settings to maximize mechanical properties, minimize power consumption, and maximize productivity were 2000 rpm and 116 mm/min.