Taguchi Experimental Design Research Articles

Facile encapsulation of cyanoacrylate-based bioadhesive by electrospray method and investigation of the process parameters

Polymer microcapsules containing cyanoacrylates have represented a promising option to develop self-healing biomaterials. This study aims to develop an electrospray method for the preparation of capsules using poly(methyl methacrylate) (PMMA) as the encapsulant and ethyl 2-cyanoacrylate (EC) as the encapsulate. It also aims to study the effect of the electrospray process parameters on the size and morphology of the capsules. The capsules were characterized using Fourier-transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and field-emission scanning electron microscopy (FE-SEM). Moreover, the effects of electrospray process parameters on the size were investigated by Taguchi experimental design. FTIR and TGA approved the presence of both PMMA and EC without further reaction. FE-SEM micrograph demonstrated that an appropriate choice of solvents, utilizing an appropriate PMMA:EC ratio and sufficient PMMA concentration are critical factors to produce capsules dominantly with an intact and spherical morphology. Utilizing various flow rates (0.3–0.5 ml/h) and applied voltage (18–26 kV), capsules were obtained with a 600–1000 nm size range. At constantly applied voltages, the increase in flow rate increased the capsule size up to 40% (ANOVA, p ≤ 0.05), while at constant flow rates, the increase in applied voltage reduced the average capsule size by 3.4–26% (ANOVA, p ≤ 0.05). The results from the Taguchi design represented the significance of solution flow rate, applied voltage, and solution concentration. It was shown that the most effective parameter on the size of capsules is flow rate. This research demonstrated that electrospray can be utilized as a convenient method for the preparation of sub-micron PMMA capsules containing EC. Furthermore, the morphology of the capsules is dominated by solvents, PMMA concentration, and PMMA:EC ratio, while the average size of the capsules can be altered by adjusting the flow rate and applied voltage of the electrospray process.

Investigation of micro-electro-discharge machining process parameters during machining of M2 hardened die-steel

Micro-electro-discharge machining (micro-EDM) is a well-known micromachining method to create microstructures on hard-to-cut materials. To further exploit the capability of micro-EDM, the present research work investigates the effect of various important micro-EDM process parameters on the material removal rate (MRR), tool wear rate (TWR), and overcut (OC) during micro-cavity machining of M2-hardened die-steel. A Taguchi experimental design was used to study the effects of three process parameters namely pulse-on-time (Ton), peak current (Ip), and gap voltage (Vg). The experimental results showed that the most influencing process parameter in the present experimental investigation is peak current, Ip followed by pulse-on-time, Ton and gap voltage, Vg. Furthermore, a recent and effective method i.e. Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) has been successfully employed to determine optimal process parametric combination for micro-EDM process to achieve maximum MRR and minimum TWR and OC during machining of micro-cavity on M2-hardened die-steel. From the present research investigation, the optimal micro-EDM process parametric combinations obtained for higher MRR, lower TWR and OC are found as 1 A of peak current, 30 μs pulse-on time and gap voltage of 60 V. Furthermore, through various scanning electron microscope micrographs of micro-cavities and micro-machined surfaces, analysis has been done to find out the micro-cavity with best geometrical accuracy and surface feature to validate the best and most ideal combinations of micro-EDM input parameters for micromachining operations. The results of this study show that micro-EDM is a promising process for machining M2-hardened die-steel. The optimization of process parameters using the MARCOS methodology can further improve the machining efficiency and accuracy of micro-EDM.

Optimasi Variasi Media Pendingin Dari Minyak Nabati Terhadap Kekasaran Permukaan Baja AISI 1045 Pada Proses Bubut CNC Menggunakan Metode Taguchi

Along with customer demand, the manufacturing industry must improve the quality of its products. In addition to product quality, environmental, safety, and health and welfare considerations also have a significant impact on the manufacturing business. One of the products of the manufacturing industry is a shaft which in its manufacture refers to the level of surface roughness. To minimize the surface roughness of the workpiece as a result of CNC lathe machining, coolant can be used. One of them is using coolant from vegetable oil. Vegetable oil is known as an ecologically acceptable and safe cooling medium for use in manufacturing operations. So a study was conducted to determine the surface roughness value of the workpiece (AISI 1045 steel) using 2 types of vegetable oil as a cooling medium (coconut oil and palm oil). Taguchi experimental design was used in this study. Based on the measurement results, the surface roughness value of AISI 1045 steel with palm oil coolant is 1.614 m, and coconut oil is 1.554 m. The coconut oil coolant type can optimize the minimum AISI 1045 steel surface roughness value with a value of 1,554 m at a cutting speed of 1273 rpm, a depth of cut of 0.50 mm, and a cutting motion of 0.16 mm/put.

Highly-efficient additive manufacturing of Inconel 625 thin wall using hot-wire laser metal deposition: Process optimization, microstructure, and mechanical properties

Hot-wire-based laser metal deposition (HW-LMD) technique was used for additive manufacturing of Inconel 625 thin-walled parts. The desired process window was obtained through the Taguchi experiment design, and the defect-free Inconel 625 thin-walled structures were efficiently manufactured with a wire deposition rate of 1.72 kg/h. The microstructure, phase composition, microhardness, and tensile properties of the Inconel 625 thin walls were studied in detail. The results showed that the microstructure of the Inconel 625 thin wall was mainly composed of a large number of columnar dendrites with an average grains size of 12.5 μm, and the growth direction of the columnar dendrites was perpendicular to the substrate surface. γ-Ni was the base phase of the as-deposited Inconel 625 thin walls and irregularly shaped Laves precipitates were observed in the inter-dendritic region. The microhardness of the Inconel 625 thin wall over the build direction was homogenous and the average microhardness was 258 HV1, which was 35.7 % higher than that of the wrought alloy. The tensile strength of the as-deposited Inconel 625 thin walls exhibited anisotropy according to the relationships between stress loading direction and microstructural texture. The maximum tensile strength and elongation of the as-deposited Inconel 625 thin wall were respectively 825.91 MPa and 55.62 %, which were close to the wrought alloy 625. Meanwhile, through comparative analysis, it was found that the tensile properties of the Inconel 625 samples fabricated in this study were superior to samples produced using conventional arc additive manufacturing methods.

Scale-Up of the Fermentation Process for the Production and Purification of Serratiopeptidase Using Silkworm Pupae as a Substrate.

Serratiopeptidase, a bacterial metalloprotease known for its pain-relieving and anti-inflammatory properties, can be produced through fermentation with S. marcescens. This study aimed to identify key factors related to nutrient composition and physicochemical conditions for production in Erlenmeyer flasks and to scale up the mixture to a bioreactor to obtain the maximum proteolytic activity. A Plackett-Burman design was used to determine whether the presence of silkworm pupae (at 1.5%) was a significant parameter for serratiopeptidase production. Along with the variables pH, temperature, and time, they were optimized using a Taguchi experimental design, resulting in values of 7, 25 °C, and 36 h, respectively. Scaling up with a kLa of 25.45 ± 3.12 h-1 showed the highest serratiopeptidase production at 24 h. A factorial design was used for ultrafiltration, resulting in an LMH (liters per square meter per hour) of 960 L/m2h, a TMP (transmembrane pressure) of 15 psi, and a concentration factor of five, with a specific activity of 24,325.81 ± 1515.69 U/mg. Afterward, the retentate was purified using strong anion exchange chromatography and ultrafiltration, yielding a 19.94 ± 3.07% recovery and a purification factor of 1.59 ± 0.31. In conclusion, waste from the sericulture industry can be used for serratiopeptidase production.

A Machine-Learning Strategy to Detect Mura Defects in a Low-Contrast Image by Piecewise Gamma Correction.

A detection and classification machine-learning model to inspect Thin Film Transistor Liquid Crystal Display (TFT-LCD) Mura is proposed in this study. To improve the capability of the machine-learning model to inspect panels' low-contrast grayscale images, piecewise gamma correction and a Selective Search algorithm are applied to detect and optimize the feature regions based on the Semiconductor Equipment and Materials International Mura (SEMU) specifications. In this process, matching the segment proportions to gamma values of piecewise gamma is a task that involves derivative-free optimization which is trained by adaptive particle swarm optimization. The detection accuracy rate (DAR) is approximately 93.75%. An enhanced convolutional neural network model is then applied to classify the Mura type through using the Taguchi experimental design method that identifies the optimal combination of the convolution kernel and the maximum pooling kernel sizes. A remarkable defect classification accuracy rate (CAR) of approximately 96.67% is ultimately achieved. The entire defect detection and classification process can be completed in about 3 milliseconds.

Machining performance optimization of graphene carbon fiber hybrid composite using TOPSIS-Taguchi approach

AbstractOptimization of process factors plays a significant role in process efficiency and effectiveness. In this context, an attempt has been made to access the optimized machining factors for polymer nanocomposites including Graphene oxide (GO)/Carbon fiber (CF). To do this, graphene concentration (wt%), feed rate (FR), and spindle speed (SS) have been chosen as governing factors and their performances have been characterized by delamination value (DV) and thrust force (TF). After defining the levels for these factors, the Taguchi experiment design method was used to obtain the experimental trial series. A TiAlN SiC-coated 06 mm drill bit was used in a CNC machine configuration to drill holes. Their corresponding performance values were noted down as DV and TF. TOPSIS method has been incorporated for accessing the measured performance dataset and relative closeness values have been calculated. These relative closeness values have been further subjected to Taguchi’s signal-to-noise ratio (S/N ratio) leading to the evaluation of an optimized parametric combination. 2 wt% of graphene, 100 mm/min of feed rate (FR), and 2100 rpm of spindle speed (SS) make up the ideal machining configuration. The mean response table indicated the SS as the most influential governing contrariant on the TF and DV. In addition, an assessment was conducted to determine the suitability of the model, and it was determined that the stated model does not exhibit any deficiencies or complications.

Mechanochemical preparation of nanocrystalline stannite/chatkalite composite: kinetics of synthesis and thermoelectric properties

AbstractIn this study we demonstrate the use of Cu, Fe, Sn and S elemental precursors to synthesize chatkalite/stannite nanocomposite by high-energy milling in both lab-scale and industrial-scale mills. The products were characterized by XRD, SEM, HRTEM, EDX, Mössbauer spectroscopy and magnetometry. For the determination of optimum milling conditions, the magnetization data of the synthesized samples were used as output values using a Taguchi experimental design. The formation of the product proceeds via a multistep process comprising binary sulphides (SnS and CuS) formation at the beginning and chatkalite Cu6FeSn2S8/stannite Cu2FeSnS4 composite as the end products. The TEM images show tightly packed nanosized crystallites with the sizes in the range of tens of nanometers. Mössbauer study unveiled that iron is present in 3+ state instead of 2+ expected for stoichiometric phases. This discrepancy was explained by the presence of a small number of vacancies on Sn sites. Based on the thermoelectric measurements of the hot-pressed pellets, the calculated figure of merit reached a value of ZT = 0.27 at 600 K. The aim of this study is to prepare a representative of a promising class of environmentally acceptable thermoelectric materials in an environmentally sound solvent-free manner and characterize them while studying the kinetics of the reaction via magnetometry and the possibility to scale-up the solid-state synthesis process.

Design optimization of additively manufactured sandwich beams through experimentation, machine learning, and imperialist competitive algorithm

The primary aim of this study was to optimize 3D-printed sandwich beams incorporating auxetic cores. Four key design variables were considered: core thickness, width, and two specific geometric parameters related to the auxetic core–the cell's wall thickness-to-length ratio and the cell angle. Initially, sixteen sandwich samples, each varying in geometric parameters, were printed following Taguchi's experimental design. These samples underwent a three-point bending test, and the resulting data were used to train a deep neural network (DNN). The trained DNN proficiently predicted the bending response of the structures. Another network, using a similar architecture, was trained to precisely predict the structure's mass. These trained networks were then integrated into the imperialist competitive algorithm (ICA) to optimize the sandwich beams design for minimal mass. A contour plot was subsequently generated to visually illustrate the minimum required mass distribution for sandwich beams under various conditions and constraints, considering applied bending forces and maximum allowable deflection. The optimization results underscored the importance of fabricating and testing an adequate number of sandwich samples, aligning with the number of design parameters. The results provide a valuable reference for future research on designing advanced 3D-printed lightweight structures, including integrating machine learning techniques and metaheuristic algorithms.

Adsorption of methyl ester sulfonate surfactant on Berea sandstone: Parametric optimization, kinetics, isotherm and thermodynamics studies

Anionic surfactants are utilized as an enhanced oil recovery (EOR) additive in sandstone reservoirs due to their excellent surface-activity and self-assembly behaviour. Considering the toxicity of some of the conventional surfactants and the clamour for sustainability approach for oil and gas operations, this present study focuses on application of anionic bio-based surfactant (methyl ester sulfonate, MES) synthesized from used cooking oil in EOR. Studies on MES adsorption onto sandstone reservoir rock were conducted, and the influence of static conditions on adsorption rate using Taguchi experimental design approach was explored. Adsorption data were analyzed using various isotherm and kinetics models. The obtained findings from optimization studies showed that salinity had a significant effect on the process, followed by temperature and pH. The concentration of surfactant had the least effect on the static adsorption process. At an optimum initial MES concentration of 500 ppm, salinity of 3.0 wt%, pH of 4.0, and temperature of 30 °C, the maximum extent of MES adsorbed by the sandstone was 98.9±0.69%. Langmuir isotherm and pseudo-second-order kinetics models were found to fit well with the adsorption equilibrium and kinetics data, respectively. The obtained values of thermodynamic parameters confirmed that MES adsorption onto sandstone was feasible, spontaneous, and exothermic.

INTEGRATION OF TAGUCHI AND PROMETHEE FOR CNC MILLING MACHINING PARAMETER OPTIMIZATION ON AA6061

In the manufacturing industry, machining has developed quite rapidly from the use of conventional machines to unconventional machines. Unconventional machines that are often used today are optimize computer numerically controlled (CNC), the use of CNC in the manufacturing industry provides many benefits in product quality and productivity. One of them is CNC milling, this type is one of the main machines on the production floor. Machining optimization becomes the main goal to achieve the ideal response in order to produce products with good and consistent quality and productivity. Surface quality leads to surface roughness, while productivity leads to material removal rate. This study aims to optimize CNC milling machining parameters on AA6061 with Taguchi experimental design and preference ranking organization method for enrichment evaluation (PROMETHEE) method. Machining was controlled using wet machining conditions to maintain temperature during machining. Experiments were conducted nine times with three factors and levels. These factors included spindle speed, feed rate, and depth of cut. The result of this research is the ideal value of the combination of surface roughness and material burning rate which is 0.565 (experiment 3). This best experiment is influenced by spindle speed 2600 rpm, feed rate 65 mm/min, and depth of cut 2.5 mm. Feed rate has the largest contribution in influencing the response which is 43.23%, followed by depth of cut 25.24%, and spindle speed 15.91%.

Investigating the impact of TiO2 filler on abrasive wear characteristics of bamboo fiber-reinforced epoxy composites using the Taguchi method

Purpose The purpose of this study is to investigate the impact of titanium oxide (TiO2) filler on the abrasive wear properties of bamboo fiber reinforced epoxy composites (BFRCs) using a Taguchi approach. The study aims to enhance the abrasive wear resistance of these composites by introducing TiO2 filler as a potential reinforcement, thus contributing to the development of sustainable and environmentally friendly materials. Design/methodology/approach This study focuses on the fabrication of epoxy/bamboo composites infused with TiO2 particles within the Wt.% range of 0–8 Wt.% using hand layup techniques. The resulting composites were subjected to wear testing according to ASTM G99-05 standards. Statistical analysis of the wear results was carried out using the Taguchi design of experiments (DOE). Additionally, an analysis of variance (ANOVA) was used to determine the influential control factors impacting the specific wear rate (SWR) and coefficient of friction (COF). Findings The study illuminates how integrating TiO2 filler enhances abrasive wear in epoxy/bamboo composites. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, Wt.% of TiO2 and sliding distance. Analysis of the COF identifies normal load as the primary influential factor, followed by grit, Wt.% of TiO2 and sliding distance. The Taguchi predictive model closely aligns with experimental results, validating its reliability. The morphological study revealed significant differences between the unfilled and TiO2-filled composites. The inclusion of TiO2 improved wear resistance, as evidenced by reduced surface damage and wear debris. Originality/value This research paper aims to integrate TiO2 filler and bamboo fibers to create an innovative hybrid composite material. TiO2 micro and nanoparticles show promise as filler materials, contributing to improved tribological properties of epoxy composites. The utilization of Taguchi’s DOE and ANOVA for statistical analysis provides valuable guidance for academic researchers and practitioners in optimizing control variables, especially in the context of natural fiber reinforced composites.

Optimizing 3D-printed workpieces for radiotherapy application: modelling the CT number and print time

This study aims to analyze the influence of specific printing parameters, including infilling, print speed, and layer height, on the CT numbers and printing time of 3D-printed workpieces fabricated from Polylactic Acid (PLA). The primary objective is to optimize these parameters to attain desired CT numbers and print time for radiotherapy applications. To achieve this objective, we employed the Taguchi experimental design and regression analysis methodologies. A series of experiments were conducted to systematically assess the effects of varying infilling, print speed, and layer height values on the CT numbers and printing time of the PLA workpieces. The resulting data were then used to create mathematical models for predicting optimal parameter settings. Our investigations revealed that specific adjustments to infilling and layer height significantly influence the CT numbers and printing time of 3D-printed workpieces. By leveraging the developed mathematical models, precise predictions can be made to optimize independent parameters for the desired CT numbers and printing times, enhancing the efficacy of 3D-printed workpieces for radiotherapy applications. This research contributes to the advancement of 3D-printed workpieces utilized in radiotherapy, offering a pathway to enhance the accuracy and efficiency of treatment delivery. The optimization of printing parameters outlined in this study provides a valuable tool for clinicians and researchers in the field, ultimately benefiting patients undergoing radiotherapy treatments.

Development of thermo-mechanical simulation of WC/Inconel 625 metal matrix composites laser cladding and optimization of process parameters

Laser cladding (LC) of industrial parts with tungsten carbide (WC)/Inconel 625 composite is highly regarded for its unique properties, for applications such as repair and improvement of surface and mechanical properties. In order to achieve high quality cladding, with minimal impact on the metallurgical structure and minimal stress and distortion caused by high local heating and cooling rate, it is necessary to determine the important parameters of the process, evaluate and investigate the effect of each one. Today, the numerical solution of this process can help predict, understand and evaluate the characteristics of the LC, and control and optimize it. In this research, using the thermo-elastoplastic simulation of the WC/Inconel 625 composite cladding process using finite element method (FEM) and the arbitrary Lagrangian-Eulerian (ALE) solution method with moving mesh strategy, the properties of the created coating have been evaluated. The laser is modeled with a moving Goldak double ellipsoidal heat source and the physical and mechanical properties of the temperature-dependent materials are considered. The physical and mechanical properties of the composite were extracted from the role of mixture and Ashby's method, respectively. Using Taguchi's experimental design method, the effect of important process parameters on output parameters such as dilution, cladding geometry, heat and stress have been investigated. Finally, the optimal parameters as well as the relationship between the parameters and each characteristic have been obtained using the linear regression model.

Machine learning for bending behavior of sandwich beams with 3D‐printed core and natural fiber‐reinforced composite face sheets

AbstractThe primary objective of this study was to demonstrate the effectiveness of machine learning (ML) in predicting the mechanical behavior of sandwich structures. To achieve this, the study focused on assessing the bending response of environmentally friendly sandwich beams consisting of auxetic cores made from polylactic acid (PLA) and flax/epoxy composite face sheets reinforced with halloysite nanotubes (HNTs). Two ML techniques, specifically shallow neural networks (SNNs) and deep neural networks (DNNs), were employed to predict the specific energy absorption (SEA) and load‐deflection curves of these sandwich beams, respectively. The key design parameters under consideration included the HNT content in the face sheets and three geometric characteristics of the auxetic cells. Subsequently, 16 distinct specimens were meticulously designed for manufacturing, following Taguchi's experimental design principles. The cores of the structures were produced using 3D printing techniques, while the face sheets were meticulously fabricated using a hand layup process. These prepared specimens were then subjected to a three‐point bending test, and the collected data were employed to train the aforementioned neural networks. The outcomes of this study revealed that an SNN with a single hidden layer comprising seven neurons effectively predicted the SEA of the structures across various design parameter values. Additionally, the remarkable performance of a DNN, consisting of five hidden layers with 128, 64, 32, 16, and 8 units, respectively, was demonstrated by comparing its predicted results with the experimental results for a randomly designed sandwich beam.Highlights ML techniques were used for bending behavior of sandwich beams. The beams were made of flax/epoxy/HNT face sheets and 3D‐printed auxetic core. SNN was successfully employed to predict the SEA of the structures. DNN effectively predicted the load‐deflection curves. HNT content and auxetic cells significantly impacted the bending properties.

Optimisation of mechanical behaviour of Calotropis gigantea and Prosopis juliflora natural fibre-based hybrid composites by using Taguchi-Grey relational analysis

The properties of organic fibre-based hybrid materials are influenced by a variety of factors and even minor changes in these variables can outcome in substantial discrepancies in strength. In this regard, the current study aims to optimise various influencing parameters such as weight percentage, alkaline treatment concentration, and fabrication parameters (compression moulding pressure, and temperature), with the goal of enhancing the overall strength of the composite. Calotropis gigantea-stem and Prosopis juliflora-bark fibres were used in varying weight percentages to create epoxy-based hybrid composites. After fabrication the mechanical characterisation like tensile, flexural, and impact properties of the composites were tested. Taguchi experimental design was applied, and the results were analysed using a hybrid Taguchi-grey relational investigation method. It was observed that a combination of 20 wt.% Calotropis gigantea/20 wt.% Prosopis juliflora/6 % NaOH pretreatment/100 °C temperature with 14 MPa pressure and had the most desirable mechanical properties in the fabricated composites. Calotropis gigantea ranks first in enhancing the composite strength, followed by Prosopis Juliflora, NaOH pretreatment%, compression moulding temperature and pressure. This work highlights the significant role of Calotropis gigantea and Prosopis Juliflora fibres in enhancing composite strength and provides valuable insights for future research in the field of hybrid composite development.

Enhancing Dimensional Geometry Casting using Computer Modeling

Sand casting method is used to produce many useful products for many applications. The aim of the study is to manufacture a product with excellent dimensional geometry is achieved in sand casting process at low cost. We would expect manuscripts to show how design and/or manufacturing problems have been solved using computer modeling, simulation and analysis. In this work, the important mechanical properties of hardness and surface roughness are investigated on Aluminum 6063 cast material with and without incorporating the copper tubes as a vent hole in sand casting process. Since copper has high thermal conductivity when compared to other metals, the heat transfer rate will be varying from existing system. The copper tubes have made different diameters of holes on outer surfaces with selective distance of intervals. The specific number of copper tubes with various diameters are designed by CATIA modeling software and analyzed with Taguchi Design of Experiment. Taguchi L9 orthogonal array is used proficiently in the optimal value of hardness and surface roughness. The results are revealed that the maximum hardness value of 104 BHN is attained for 10mm distance of holes made on copper tube with an angle of 90o degree. The minimum surface roughness of 2.11 micron is achieved for 20mm distance of holes made on copper tube with 45o of angle as a vent hole in sand casting process.

Post curing optimization for tensile strength of hybrid ramie-carbon fiber reinforced polymer

The optimization of post-curing processes is crucial for enhancing the performance of epoxy-based fiber-reinforced polymers (FRP) by ensuring adequate cross-linking. This study focuses on optimizing the post-curing parameters for hybrid Ramie and Carbon fiber composites with the primary objective of improving tensile strength. Variations in post-curing temperature, post-curing time, and the number of synthetic fiber layers were systematically investigated across three levels using Taguchi design of experiments. The ultimate tensile stress was employed as the response parameter. Results indicate that post-curing temperature exerts a greater influence on tensile strength compared to post-curing time. A failure pattern of natural fiber followed by synthetic fibers was seen to happen progressively. A precise multivariable regression model was developed to predict the response for different combinations of post-curing parameters. Furthermore, employing particle swarm optimization revealed an optimal post-curing time of 12 h and an optimal temperature of 60°C. These findings contribute to the optimization of post-curing processes in hybrid fiber composites, thereby enhancing their mechanical properties.

Empirical Modeling and Analysis of Process Parameters in Laser Beam Cutting Process

The research has been originated based on the persuasive applications of Lasers in the field of various engineering industries, i.e., Aerospace, Automobile, Electronic and Heavy manufacturing industries to machine a variety of metals and alloys. Out of all the machining processes available, Laser Beam Machining is considered to be the best one, because of the following advantages, i.e., quick material removal, non-contact, non-wearing tool, involves highly localized heat input to the work piece, reduces distortion, and offers no tool wear, diminishes tendency of cracking. The present paper focused mainly on developing the empirical relationships between the input process parameters and the output responses in Laser beam cutting process. Laser power, Cutting Speed, Gas pressure and Focal distance are the input process parameters. The output responses considered are related to the quality of cut. Surface roughness is the first output responses considered and the second one is Burr height. Total number of experiments carried out is 27 based on the Taguchi Design of Experiments. Haste alloy C276 is the work material. Response Surface Methodology is applied for the Experimental data of 31 samples to derive the empirical relationships. Later ANOVA analysis is also carried out to check the adequacy of the derived equations. Based on the ANOVA analysis, the models are classified as significant or not significant. Further the significant models can be used for optimizing the Laser beam cutting process. Once the process is optimized, it can be automated, so that the process can be run very efficiently and economically.

Enhancing Soap Formulation from Waste Oil: An Analysis of Properties and User Satisfaction Using Taguchi Method

This research centers on modifying and optimizing waste oil soap formulation using Taguchi design of experiments (DOE). The aim of the study is to find optimal soap parameters with respect to the input variables such as water, NaOH, waste oil and agarwood dust. The output parameters to be investigated are pH, foaming rate, antimicrobial activity, and hedonic results which measure appearance, odor, textural properties, color, and general acceptability. Based on the hedonic results, Taguchi analysis reveals the rank order of parameters that influences the quality of the soap and it was noted that waste oil is the most important parameter followed by agarwood dust, water and NaOH. The optimal formulation determined consists of coconut oil, 70 ml of water, 2 g of agarwood dust, and 15 g of NaOH. Detailed properties analysis and the regression equation for this formulation are included to provide a comprehensive understanding of the soap’s performance. This systematic approach not only enhances the soap’s quality but also promotes the utilization of waste oil, contributing to environmental sustainability.