Composite Desirability Function Research Articles

Electrical discharge machining of super alloy incoloy 925: a study based on box behnken design and response surface methodology

Incoloy 925, a Nikel-based superalloy, exhibits low machinability with conventional machining techniques due to its inhomogeneous properties. Therefore, there is a need to establish a non-conventional method to efficiently machine this alloy. This work is a novel attempt to present the electric discharge machining (EDM) of the superalloy Incoloy 925 and subsequent multi-response optimization. The model for the analysis was designed using the Box Behnken Design (BBD) technique, and the Response Surface Methodology (RSM) was used for the optimization of the results. The machining was performed using a cylindrical copper tool of 11 mm diameter. The effect of pulse-on time (Ton), current, and pulse-off time (Toff) on the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) was investigated. The results from variance analysis confirmed the significance of all the three input factors. The investigation revealed that the maximum MRR (99.2154 mm3/min) was obtained at a pulse-on time of 90 μs, pulse-off time of 5 μs, and current of 30 A. The minimum TWR (0.8866 mm3/min) were achieved at Ton = 60 μs, Toff = 8 μs and current = 10 A. The microscopic images of the machined surfaces revealed very few micro-voids and globules and no cracks, resulting in a fine surface finish of 1.2436 μm, achieved through optimal discharge energy transfer and copper electrodes. The optimal values MRR, TWR, and SR according to composite desirability function are 99.1524 mm3/min, 1.0915 mm3/min, and 1.3925 μm. The experimental results were accurately predicted using RSM and an artificial neural network (ANN), with the ANN showing a predicted correlation coefficient (R) close to 1 indicating high accuracy of the model.

Immersion cooling of a cylindrical battery module: An optimum configuration using CFD, NSGA-II and desirability function approach

Immersion cooling offers promising advantages for cylindrical battery modules, particularly in applications where compact design, efficient thermal management, and enhanced safety are critical factors. In this study, the geometric, thermal, and dynamic parameters of an immersion-cooled battery module with 14 NCM cylindrical cells are analyzed using CFD methods. The developed battery model is experimentally validated, establishing the constraints through numerical analysis of the impact of design parameters on response values. The design parameters are optimized using a multi-objective optimization technique. The study models the battery module with mineral oil cooling and examines the impact of each parameter on variables such as maximum battery module temperature (Tmax), pressure drop (ΔP), maximum temperature variance (ΔTmax), and battery module mass index (Im). A Design of Experiment set, created with Central Composite Design (CCD), is solved using CFD methods, and quadratic correlation equations are derived using backward regression. The regression models for Tmax, ΔTmax, ΔP, and Im show high accuracy with R2 values close to 1.0. Optimization is performed using the entropy weight-based composite desirability function approach, yielding optimum values for cell center spacing (t), coolant mass flow rate (ṁc), and coolant inlet temperature (Ti) at 20.606 mm, 1.5 kg/s, and 23 °C, respectively. The optimized design sets are validated through CFD analysis, showing a maximum deviation of 4.27 %. Reducing t boosts thermal performance but lowers hydraulic performance, while the reverse occurs for ṁc. As Ti decreases, effective cooling improves, but temperature uniformity suffers.

Optimization of microwave-produced coal-derived char insulation foam using response surface methodology

A new char-based insulation foam (CIF) is developed using coal-derived pyrolysis char (PC) and lignin, a paper and pulp industry byproduct. In this study, CIF is prepared by adopting the microwave method and Response Surface Methodology (RSM) is utilized to optimize predictions about the properties such as thermal conductivity, foam texture, compressive strength, and pore distribution of CIF. The three independent variables including percent char content, lignin/char ratio, and foaming agent/polyol ratio are considered in the RSM methodology. The face-centered central composite design is selected to obtain 17 test runs. Optimization of the predicted responses is performed to find the recipe considering a composite desirability function. Confirmatory test results show agreement between the predicted and observed values, with the percent error ranging from 0.00 % to 11.35 %, thereby confirming the accuracy of the prediction equations. The optimized CIF exhibits a thermal conductivity of 0.0633 W/m.K and a compressive strength of 695 kPa making it comparable to or better than traditional building insulation products such as fiber insulation, polymer composite, particle board, and ceramic glass foam. Therefore, this new CIF has the potential to be used as an energy-saving insulation material for building envelopes.

A novel, intelligent and computer-assisted electrochemical sensor for extraction and simultaneous determination of patulin and citrinin in apple and pear fruit samples

In this work, a novel electrochemical sensor was fabricated for simultaneous determination of patulin (PT) and citrinin (CT) in apple and pear fruit samples. A glassy carbon electrode (GCE) was modified with graphene-multiwalled carbon nanotubes-ionic liquid (Gr-MWCNTs-IL) which was used as a platform to electrochemical synthesis of molecularly imprinted polymers (MIPs) by using PT and CT as templates, maleic acid as a functional monomer, and ethylene glycol dimethacrylate as a cross linker with the aim of preconcentration and simultaneous determination of the PT and CT. Experimental variables affecting fabrication of the structure of the sensor and hydrodynamic differential pulse voltammetric (HDPV) response of the sensor were optimized by a small central composite design and desirability function. After optimization, the HDPV responses of the sensor were calibrated by multivariate calibration methods in the ranges of 0.5–13 fM and 1.5–18 fM for PT and CT, respectively, with the help of PLS-1, RBF-PLS, rPLS, LS-SVM, and RBF-ANN with the aim of selecting the best algorithm to assist the sensor. Our results confirmed the best performance was observed from RBF-ANN which was used for the analysis of apple and pear fruit samples. Limit of detections of the sensor assisted by RBF-ANN for determination of PT and CT were 0.08 and 0.61 fM, respectively. Several commercial brands were analyzed by the use of sensor assisted by RBF-ANN and HPLC-UV, and the results confirmed performance of the sensor was admirable and comparable with the reference method with lower cost, faster response, and easier procedure which made it to be a reliable alternative method for simultaneous determination of PT and CT in real matrices.

Removal of an organophosphate insecticide from aqueous media using phyllosilicate clay: multivariable optimization, nonlinear kinetic modelling and thermodynamic study

The excessive and irrational use of organophosphate insecticides in our lives has resulted in a serious environmental problem, necessitating the proposal of novel and cost-effective plans to remove these organophosphate insecticides from agricultural or industrial wastewater. In this approach, we investigated the use of phyllosilicate clay as a low-cost and environmentally friendly adsorbent to eliminate an organophosphate insecticide (Diazinon DZN) from an aqueous medium. To achieve an adequate elimination of this insecticide from contaminated water, three main parameters including clay dose ( D c ) (0.1–1.5 g L−1), medium pH (5–9), and initial DZN concentrations ( C DZN ) (55–40 mg L−1) were optimized using a multivariable optimization based on Box-Behnken Design (BBD). The response surface method (RSM) and composite desirability function technique were used to find the ideal conditions for the three input variables and the adsorption amount ( Q e ) was selected as the output variable. The higher adsorption amount ( Q max ) was achieved with an adsorbent amount of 1.5 g L−1 in an acidic medium (pH = 5) at room temperature and a Diazinon initial dose of 25.85 mg L−1, with a maximum adsorbent loading of 12.20 mg g−1. The nonlinear modeling study showed that the adsorption kinetics of Diazinon follows the pseudo-second-order model. The thermodynamic study shows that the DZN removal process is exothermic, spontaneous, and of a stable configuration.

On the optimized fused filament fabrication of polylactic acid using multiresponse central composite design and desirability function algorithm

Efficient optimization of polymeric materials in fused filament fabrication 3D printing (FFF 3DP) is crucial for productivity, cost reduction, resource conservation, consistency, and enhanced part performance. This study employed a multiresponse central composite design of experiments (CCD-DOE) with the desirability function algorithm (DFA) to optimize printing settings on polylactic acid (PLA) using a commercial FFF 3D printer. The goal was to identify optimal parameters for faster build time and reduced material usage in PLA part fabrication. The fabrication process involved computer-aided design and modeling of standard PLA dogbone specimens, meeting ASTM-D638 Type 1 tensile test standards. These specimens were then 3D printed using Ultimaker Green RAL 6018 PLA filament and a 2+ model printer set at varying print parameters. Reduced second-order polynomial models for printing time and PLA weight were generated using stepwise regression, eliminating noninfluential parameters. The models revealed that higher layer thickness, increased print speed, and lower infill density resulted in faster printing times, while lower infill density and higher layer thickness led to lighter PLA prints. DFA analysis determined the optimal settings as a layer thickness of 0.26–0.30 mm and an infill density of 35% for minimizing printing time and PLA weight. The stress–strain curves displayed characteristic high-strength, brittle behavior under tension, while tensile testing of optimized PLA parts revealed increased strength with low strain at the break when layers were aligned parallel to the applied force. These findings advance additive manufacturing and provide practical guidelines for high-quality 3D-printed PLA components. Optimizing FFF 3DP parameters enables efficient production with reduced time and material usage, enhancing cost-effectiveness and the fabrication of high-performance 3D printed products.

Optimization of the QuEChERS Extraction of Mycotoxins in Maize Using Response Surface Methodology (RSM)

Several methods have been developed for the analysis and detection of mycotoxins in food; however, most do not make use of critical statistics and mathematical tools for precise optimization. This study developed, optimized, and validated a modified quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction procedure for the extraction of multiple mycotoxins in maize and subsequent validation using liquid chromatography tandem mass spectrometry (LC–MS/MS). Central composite design (CCD) was used to optimize extraction conditions. Data analysis of full factorial screening experiments revealed that MeCN (%), FA (%), and extraction time significantly affected the mycotoxins recovery. Assessment of the statistical significance of the generated model using analysis of variance (ANOVA), coefficient tables, and surface plots showed the relative interactions of factors and the adequacy of the model. Thus, P values from the lack of fit (LOF) test ranged from 0.137– 0.467 and a composite desirability function of 0.91 was obtained. Using the optimum extraction conditions of 0.1% (v/v) FA in 80.2% MeCN for 74 min, 10 mycotoxins were effectively extracted with satisfactory recoveries (85–114%), coefficients of regression (R2 > 0.98), coefficients of variation (CVs < 15%), limit of quantifications (LOQs) (0.33–60.45 µg/kg), and other associated method validation parameters. The method validation was carried out according to Commission Implementing Regulation 2021/808 and Commission Regulation (EC) No 401/2006 of 23 February 2006. Application of this method to 20 maize samples collected from markets in Botswana showed detectable mycotoxins in 13 samples, with 2 exceeding the European Union (EU) maximum aflatoxin B1 (AFB1) limit, suggesting potential exposure to high levels of toxic mycotoxins in Botswana.

A Box-Behnken design-based chemometric approach to optimize the sono-photodegradation of hydroxychloroquine in water media using the Fe(0)/S2O82-/UV system.

The huge utilization of hydroxychloroquine in autoimmune infections led to an abnormal increment in its concentration in wastewater, which can pose a real risk to the environment, necessitating the development of a pretreatment technique. To do this, we are interested in researching how hydroxychloroquine degrades in contaminated water. The main goal of this investigation is to optimize the operating conditions for the sono-photodegradation of hydroxychloroquine in water using an ultrasound-assisted Fe(0)/ /UV system. To get adequate removal of HCQ, a chemometric method based on the Box-Behnken design was applied to optimize the influence of the empirical parameters selected, including Fe(0) dose, concentration, pH, and initial HCQ concentration. The quadratic regression model representing the HCQ removal rate (η(%)) was evolved and validated by ANOVA. The optimal conditions as a result of the above-mentioned trade-off between the four input variables, with η(%) as the dependent output variable, were captured using RSM methodology and the composite desirability function approach. For HCQ full decomposition, the optimal values of the operating factors are as follows: dose, 194.309 mg/L; Fe(0) quantity, 198.83 mg/L; pH = 2.017, and HCQ initial dose of 296.406 mg/L. Under these conditions, the HCQ removal rate, achieved after 60 min of reaction, attained 98.95%.

Thermo-hydrodynamical optimization of direct absorption solar collector from Stokes to low Reynolds numbers regimes of nanofluid flow

It has been proven that optical properties of base working fluids are significantly enhanced, where very small amount of nanoparticles are exploited. In the current study, numerical investigation of microchannel-based direct absorption flat solar collector (DAFSC) filled with graphite water based nanofluids at various volume fractions in an independent-scattering regime map of nanofluids were taken into account to evaluate thermo-hydrodynamical performance of DAFSC. Furthermore, multi-response optimization of DAFSC including thermodynamics second law efficiency (ηII), thermal efficiency (η(th)), heat loss rate (Q̇(out)), dimensionless temperature difference (θ), pressure drop (Δp), and also cost function (Cost) was carried out by response surface methodology. Fifty four numerical designs proposed by face-centred central composite designs (FCCD) have been used to provide sufficient data to obtain mathematical regression models of response functions in which the significant model's terms were evaluated by analysis of variance. Furthermore, composite desirability function approach was taken into account for multi-response optimization of direct absorption solar collector to maximize ηII, η(th), θ, and to minimize Q̇(out),Δp and Cost functions. Strikingly, the CFD results unveiled that maximum thermal efficiency of DAFSC with black back absorber surface, which was enhanced up to 18% compare to base case, is mainly shape-dependent rather than to that of nanoparticles volume fractions, which can be justified by presence of different dominant heat absorption mechanisms transforming from volumetric absorption for nanofluids to surface one for pure water.

Utilization of Biowaste for Sustainable Production of Coal Briquettes

In energy scarcity, particularly in Agri-based developing economies, bio-coal briquetting is the most suitable means of meeting sustainable energy needs utilizing agricultural waste. In this study, briquettes were made from an indigenously designed briquetting machine for investigating coal–biomass proportion blend using coal from Dara Adam Khel, Khyber Pakhtunkhwa in Pakistan, and pomegranate/olive waste to analyze their resulting calorific value, strength, and geochemical properties. A central composite design (CCD) and response surface methodology (RSM) were employed to design the experiments and analyze the data. Regression models were developed for each biomass type, demonstrating their adequacy and reliability for further analysis and prediction. Energy Dispersive X-ray Spectroscopy (EDX) analysis provided insights into the elemental composition of the coal briquettes. Mechanical analysis was performed to assess the strength of the briquettes, with varying compositions showing different levels of strength. Optimization using a composite desirability function yielded an optimal calorific value of 6728 kJ/kg. In summary, this study underscores the viability of bio-coal briquetting as a transformative solution to alleviate energy scarcity. Repurposing agricultural waste curtails resource depletion while mitigating waste disposal challenges. The optimized calorific value aligns with eco-friendly energy initiatives, advancing a greener path toward energy security.

Impact analysis of electrode materials and EDM variables on the surface characteristics of SS316L for biomedical applications

The biocompatibility of the SS316L makes it an alluring alternative for orthopaedic and other biomedical applications. In such applications, surface characteristics of the materials are key considerations for bacterial adhesion at the surface of the metallic implant. Therefore, the current study focuses on the investigation of surface roughness and microhardness of SS316L in the electric discharge machining process using adequate electrode/tool material. Machining is performed with brass, copper, and aluminum electrodes to limit the surface roughness and enhance the microhardness of machining surfaces. Besides this, process variables including peak current, pulse off time, pulse on time and spark voltage are also optimized to obtain superior surface characteristics. Results show that adequate surface roughness of 1.08 μm can be attained with the aluminum electrode while brass electrodes offered increased microhardness of 1270.6 HV of machined surfaces. Microstructural analysis shows that aluminum electrode yields shallow and small-sized craters and few micro-cavities while brass electrode generates high peaks and valleys on the machined surface due to the deep craters, micro cracks, and cavities. The composite desirability function approach suggests that aluminum electrode along with the optimal setting of process variables such as peak current = 25 A, pulse off time = 50 μs, pulse on time = 55 μs and spark voltage = 2 V offers reasonable values of surface roughness 4.09 μm and microhardness 1051.2 HV after trading off.

Low-CO2 Optimization Design of Quaternary Binder Containing Calcined Clay, Slag, and Limestone.

Blended cement is commonly used for producing sustainable concretes. This paper presents an experimental study and an optimization design of a low-CO2 quaternary binder containing calcined clay, slag, and limestone using the response surface method. First, a Box-Behnken design with three influencing factors and three levels was used for the combination design of the quaternary composite cement. The lower limit of the mineral admixtures was 0%. The upper limits of slag, calcined clay, and limestone powder were 30%, 20%, and 10%, respectively. The water-to-binder ratio (water/binder) was 0.5. Experimental works to examine workability and strength (at 3 and 28 days) were performed for the composite cement. The CO2 emissions were calculated considering binder compositions. A second-order polynomial regression was used to evaluate the experimental results. In addition, a low-CO2 optimization design was conducted for the composite cement using a composite desirability function. The objectives of the optimization design were the target 28-day strength (30, 35, 40, and 45 MPa), target workability (160 mm flow), and low CO2 emissions. The trends of the properties of optimal combinations were consistent with those in the test results. In summary, the proposed optimization design can be used for designing composite cement considering strength, workability, and ecological aspects.

A multi-response optimization approach to mechanical properties improvement of dissimilar resistance spot welding joints

Purpose This study aims to investigate the effects of process input parameters (welding current, welding time, electrode pressure and holding time) on the output responses (nugget diameter, peak load and indentation) that control the mechanical properties and quality of the joints in dissimilar resistance spot welding (RSW) for the third generation of advanced high-strength steel (AHSS) quenching and partitioning (Q&amp;P980) and (SPFC780Y) high-strength steel spot welds. Design/methodology/approach Design of experiment approach with two level factors and center points was adopted. Destructive peel and shear tensile strengths were used to measure the responses. The significant factors were determined using analysis of variance implemented by Minitab 18 software. Finally, multiresponse optimization was carried out using the desirability function analysis method. Findings Holding time was the most significant factor influencing nugget diameter, whereas welding current had the greatest impact on peak load and indentation. Multiresponse optimization revealed that the optimal settings were a welding current of 12.5 KA, welding time of 18 cycles, electrode pressure of 420 Kgf and holding time of 10 cycles. These settings produced a nugget diameter of 8.0 mm, a peak load of 35.15 KN and an indentation of 22.5%, with a composite desirability function of 0.764. Originality/value This study provides an effective approach for multiple response optimization to the mechanical behavior of RSW joints, even though there have been few studies on the third generation of AHSS joints and none on the dissimilar joints of the materials used in this study.

INVESTIGATION INTO AN ECO-FRIENDLY REACTIVE DYEING PROCESS OF COTTON FABRICS USING AN ETHANOL-WATER MIXTURE THROUGH DESIGN OF EXPERIMENT

Textile industry professionals are unanimous that there is a great need to develop environmentally sustainable methods of cotton dyeing. Among various problems related to conventional dyeing, some of the main problems consist in the large volumes of discharge effluent with a high concentration of salt and their impact on the environment. This investigation explores the use of ethanol as a solvent in the reactive dyeing of cotton fabric, to reduce the concentration of salt and the volume of water used. The dyeing process employed a 90:10 (v/v) ethanol-water mixture. Time and temperature were varied periodically to improve the degree of exhaustion, and the effects of time, temperature and salt content were studied and compared with conventional reactive dyeing. It was observed that the colour strength of solvent-assisted reactive dyeing is comparable to the conventional process. The dyed sample under the optimum condition had good wash fastness, both in terms of shade change and staining, and its dry crocking fastnesses were found similar or acceptable in comparison with the water-dyed sample. However, all the samples of the solvent-assisted reactive dyeing process have better wet crocking fastnesses compared to the conventional reactive dyeing process. The process parameters of eco-friendly reactive dyeing have been optimised using the composite desirability function. The optimal process parameters for the solvent-assisted reactive dyeing process were found to be 60 minutes of dyeing at 80 °C with 20 g/L of salt.

Competitive adsorptive removal of promazine and promethazine from wastewater using olive tree pruning biochar: operational parameters, kinetics, and equilibrium investigations

This research aims to remove two phenothiazines, promazine (PRO) and promethazine (PMT), from their individual and binary mixtures using olive tree pruning biochar (BC-OTPR). The impact of individual and combinatory effects of operational variables was evaluated for the first time using central composite design (CCD). Simultaneous removal of both drugs was maximized utilizing the composite desirability function. At low concentrations, the uptake of PRO and PMT from their individual solutions was achieved with high efficiency of 98.64%, 47.20 mg/g and 95.87%, 38.16 mg/g, respectively. No major differences in the removal capacity were observed for the binary mixtures. Characterization of BC-OTPR confirmed successful adsorption and showed that the OTPR surface was predominantly mesoporous. Equilibrium investigations revealed that the Langmuir isotherm model best describes the sorption of PRO/PMT from their individual solutions with maximum adsorption capacities of 640.7 and 346.95 mg/g, respectively. The sorption of PRO/PMT conforms to the pseudo-second-order kinetic model. Regeneration of the adsorbent surface was successfully done with desorption efficiencies of 94.06% and 98.54% for PRO and PMT, respectively, for six cycles.Graphical abstract

EXPERIMENTAL STUDY ON THE SURFACE ROUGHNESS AND OPTIMIZATION OF CUTTING PARAMETERS IN THE HARD TURNING USING BIOCOMPATIBLE TiAlN-COATED AND UNCOATED CARBIDE INSERTS

Machining of difficult-to-cut materials has always been a focus of research. In terms of surface roughness, it is one of the most important machinability indicators used to evaluate the performance of machining processes. This research aims to investigate the effect of biocompatible TiAlN-coated and uncoated carbide inserts, as well as the effect of cutting parameters such as feed, rotational speed, and depth of cut on surface roughness in the hard turning of M2 tool steel at 64 HRC. The central composite design is used to create the experimental layout. Surface roughness values are measured using separate experiments for coated and uncoated inserts. A quadratic model is selected, and an analysis of variance (ANOVA) is performed to test the adequacy of the developed model. From the ANOVA, it is found that feed and rotational speed are the most significant parameter while hard turning with TiAlN-coated and uncoated inserts, respectively. Cutting parameters are ranked according to their importance using the Pareto chart. The composite desirability function is employed to determine the optimal setting of cutting parameters to minimize the surface roughness and a confirmation experiment is conducted to validate the optimization results. Confirmation results are very close to the predicted value and the error between experimental and predicted results are 7.93% and 9.36% with TiAlN-coated and uncoated carbide inserts, respectively. TiAlN-coated carbide insert gives better surface roughness compared to an uncoated carbide insert.

Performance analysis of Ti2AlN superimposed WC cutting tool

This article presents the development of superimposed Ti2AlN MAX phase coatings on cutting tools through reactive co-sputtering technique in dry machining of Ti-6Al-4 V. The effect of varying nitrogen flow rate during the reactive co-sputtering process and its influence on cutting properties has also been discussed. The predictive modeling along with validation of experimental results were carried out using response surface methodology (RSM) and optimized condition for cutting force were obtained using composite desirability function. Machining studies using the superimposed Ti2AlN coatings reduced the cutting force by 23–35 % when compared to commercial coatings. Investigation of cutting mechanics revealed an increase of shear angle by 3–5 % and decrease in friction angle by 20 %. The superimposed MAX phase coatings also reduced the friction coefficient by 34 % during dry turning of Ti-6Al-4 V. The superimposed Ti2AlN coatings were held responsible to improve the performance of the tool by 21–30 % in terms of tool life when compared to commercial tool. It could be inferred that the superimposed Ti2AlN coatings reduced the effect of chemical affinity between the tool and workpiece, eliminating the formation of built-up edge at rake face of the tool.

Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application

The use of carbon fiber reinforced plastic (CFRP) is increasing in engineering applications such as aerospace, automobiles, defense, and construction. Excellent strength-to-weight ratio, high impact toughness, and corrosion resistance make CFRP highly suitable for aerospace applications. Curing temperature, curing time, and autoclave pressure are among the most important curing parameters affecting the properties of CFRP. Tensile strength, impact toughness, and hardness of CFRP were selected as desirable properties for optimization. A 23 full factorial design of experiment (DOE) was employed by varying curing temperature (120 and 140 °C), curing time (90 and 120 min), and autoclave pressure (3 and 7 bar) while keeping the number of experiments to a minimum level. The cured samples were subjected to tensile strength, impact toughness, and hardness tests at room temperature as per relevant ASTM standards. Analysis of variance (ANOVA) was used, and it was found that tensile strength, impact toughness, and hardness were influenced most significantly by temperature and time. The maximum tensile strength and hardness were achieved for curing cycle parameters of 140 °C, 120 min, and 7 bar, and impact toughness was maximized for 140 °C, 120 min, and 3 bar. A concept of composite desirability function was used to achieve simultaneous optimization of conflicting tensile strength and impact toughness properties for the specific application of aircraft skin.

Investigation of cutting time and tool wear rate on EN-24 steel alloy by the drilling process

Modeling and optimization of cutting parameters are one of the most important factors in manufacturing process. The aim of present work is to establish the relation among input factors i.e. spindle speed, feed rate and depth of cut and response parameters i.e. cutting time and tool wear rate. The operation is performed on EN-24 alloy steel material. The analysis of variance (ANOVA) has been performed to find the significant and non-significant parameters. Mathematical model is developed for CT and TWR and optimized using composite desirability (CD) function technique. It was found that the best machining factor is depth of cut whereas spindle speed is the less significant machining parameter. The confirmation experiments have been also performed to validate the results. The given model could be utilized to select the level of drilling parameters.

Optimisation of tensile and compressive behaviour of PLA 3D printed parts using categorical response surface methodology

The present study aims to optimise process parameters of 3-D printed polylactic acid (PLA) part using response surface methodology (RSM). The input printing process parameters considered are layer height (L), infill percentage (I), raster width (R) and infill patterns (P) (i.e., linear, hexagonal and diamond), while the responses are tensile and compressive strengths. Box Behnken array design is applied for experimental runs and also to fit quadratic regression models. The results revealed that significant parameters affecting compression strength performance are I, I2, R, and interaction of I and R, for tensile strength, are L, I, I2, R, P, and interaction of P with L and I. The simultaneously optimised parameters obtained based on composite desirability function for compression and tensile strength are L = 0.1 mm, I = 100%, R = 0.4 mm, and P = hexagonal, while the obtained maximum compression and tensile strength are 9.06 kN and 1.67 kN.