L9 Orthogonal Array Research Articles

Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method

This research explores multi-objective optimization in injection molding with a focus on identifying the optimal configuration for the moldability index in aviation propeller manufacturing. The study employs the Taguchi method and fuzzy analytic hierarchy process (FAHP) combined with the Technique for the Order Performance by Similarity to the Ideal Solution (TOPSIS) to systematically evaluate diverse objectives. The investigation specifically addresses two prevalent defects—shrinkage rate and sink mark—that impact the final quality of injection-molded components. Polypropylene is chosen as the injection material, and critical process parameters encompass melt temperature, mold temperature, filling time, cooling time, and pressure holding time. The Taguchi L25 orthogonal array is selected, considering the number of levels and parameters, and Finite Element Analysis (FEA) is applied to enhance precision in results. To validate both simulation outcomes and the proposed optimization methodology, Artificial Neural Network (ANN) analysis is conducted for the chosen component. The Fuzzy-TOPSIS method, in conjunction with ANN, is employed to ascertain the optimal levels of the selected parameters. The margin of error between the chosen optimization methods is found to be less than one percent, underscoring their suitability for injection molding optimization. The efficacy of the selected optimization method has been corroborated in prior research. Ultimately, employing the fuzzy-TOPSIS optimization method yields a minimum shrinkage value of 16.34% and a sink mark value of 0.0516 mm. Similarly, utilizing the ANN optimization method results in minimum values of 16.42% for shrinkage and 0.0519 mm for the sink mark. Doi: 10.28991/ESJ-2024-08-05-025 Full Text: PDF

Elimination of phenol by sonoelctrochemical process utilizing graphite, stainless steel, and titanium anodes: optimization by taguchi approach

Phenol is one of the worst-damaging organic pollutants, and it produces a variety of very poisonous organic intermediates, thus it is important to find efficient ways to eliminate it. One of the promising techniques is sonoelectrochemical processing. However, the type of electrodes, removal efficiency, and process cost are the biggest challenges. The main goal of the present study is to investigate the removal of phenol by a sonoelectrochemical process with different anodes, such as graphite, stainless steel, and titanium. The best anode performance was optimized by using the Taguchi approach with an L16 orthogonal array. the degradation of phenol sonoelectrochemically was investigated with three process parameters: current density (CD) (25, 50, 75, and 100 mA/cm2), time (1, 2, 3, 4 h), and phenol concentration (100, and 200 mg/l). Signal-to-noise (S/N) ratio and analysis of variance (ANOVA) were utilized to examine the impact of each factor. The optimal conditions for phenol removal were 100 mA/cm2, 100 mg/l of phenol, and 4 hours of electrolysis. Under optimal operating conditions, the phenol removal efficiency was 80.99%. The CD was the most influential factor on phenol elimination effectiveness, while the phenol concentration had the least impact.

Enhancing Drilling Performance in Natural Fiber-Reinforced Biocomposites Using Taguchi Method for Sustainable Manufacturing

The current research work focuses on optimizing drilling parameters for Sesbania rostrata fiber-reinforced polycaprolactone (PCL) biocomposites using the Taguchi method and ANOVA to improve hole quality, particularly to minimize delamination. The Taguchi L9 orthogonal array was employed with three factors: spindle speed (300, 750, and 1800 rpm), feed rate (0.05, 0.12, and 0.25 mm/rev), and drill size (4, 8, and 10 mm). The experimental results showed that drill size had the most significant effect on thrust force, with a contribution rate of 99.29%, whereas spindle speed primarily influenced delamination, contributing 67.65%. Optimal parameters were determined to be a spindle speed of 300 rpm, a feed rate of 0.25 mm/rev, and a drill size of 10 mm. These findings are significant for industries using biocomposites, offering sustainable solutions with enhanced machining performance.

An experimental and analytical approach to study the sliding wear performance of epoxy composites with steel wire and glass fiber reinforcement

This study investigates the sliding wear behavior of hybrid epoxy composites reinforced with alternating layers of glass fiber mats and steel wire mesh. The objective is to enhance the wear resistance of glass fiber-epoxy composites with the incorporation of steel wire mesh. Three composite types with different stacking sequences are fabricated using the hand layup technique. Dry sliding wear tests are conducted using a pin-on-disc test rig under various operating conditions, following to ASTM G99 standard. Taguchi analysis with L25 orthogonal array identifies sliding velocity as the most influential factor on wear rate, followed by normal load. Steady-state wear analysis is performed to evaluate the effect of each significant factor independently. Analysis of variance results show sliding velocity significantly affects wear rates, contributing 67.63% for glass-epoxy composite (G11) and more than 75% for glass-steel-epoxy hybrid composites (G7S4 & G4S7). A regression model, based on experimental data, predicts specific wear rates with error margins of 3.17% for glass-epoxy composite and less than 2% for glass-steel-epoxy composites. Additionally, electron microscopy is used to analyze worn surfaces, revealing the primary wear mechanisms.

Impacts of Zirconium Carbide Incorporated Magnesium Alloy Nanocomposite on Wear Properties

In the study, zirconium carbide nanoparticle (ZrC-50 nm) incorporated magnesium alloy (AZ61) nanocomposite was prepared by liquid state stir cast process, and its wear properties were evaluated by different load (U), sliding speed (V), and sliding distance (W). The impacts on wear rate (WR) and coefficient of friction (COF) of AZ61 alloy nanocomposite were studied. With the support of a pin-on-disc wear tester, the dry sliding wear characteristics were measured. Moreover, to find the optimum process parameter on minimum WR and better COF characteristics, the L27 orthogonal array utilized U, V, and W as the input factors, and the WR & COF were treated as output responses. Based on the analysis, load was found to be the most dominant input factor that influences the WR and sliding distance was the factor for fixing the COF.

Pot Skirt Configuration on the UB-03 Biomass Stove: Taguchi Approach Optimization

A biomass stove is a technology that can utilize biomass fuel as an alternative energy source. This stove is considered effective for saving fossil energy because it uses fuels such as wood, waste, and plants so that it can reduce the effects of global warming because it can minimize the emissions it produces. In its technological development, UB-03 is a biomass stove product with compact construction and affordable prices for rural communities. However, there needs to be an increase to produce more efficient performance. In this case, additional configurations in the form of a pot skirt can be used to increase the efficiency of the biomass stove. The pot skirt is a device that focuses the fire’s direction on the load to minimize wasted heat and increase the efficiency of the biomass stove. This study used the Water Boiling Test (WBT) method for experimental testing. In addition, the Taguchi method was used to analyze the data obtained. This study aims to determine the optimum conditions of the biomass stove with the addition of a pot skirt using the Taguchi method with orthogonal array L9 (33) with three factors, namely angle (64°, 65°, 66°), number of holes (9,10,11) and hole diameter (0.8 cm, 1 cm, 1.2 cm). The results of this study indicate that the optimal configuration obtained is by adding a pot skirt at an angle of 65° with 9 holes and a hole diameter of 1 cm, where the highest efficiency value is obtained, namely 21.19%.

Chemical characterization of hibiscus rosa-sinensis plant fibers facilitated through design of experiments and artificial neural network hybrid approach

The integration of natural fibers into Fiber Reinforced Polymers (FRPs) has emerged as a promising avenue for sustainable and high-performance composite materials. Natural fibers, derived from plants, offer notable advantages such as renewability, low cost, and environmental friendliness. Among these natural fibers, Hibiscus Rosa-Sinensis (HRS) plant fibers have gained significant attention owing to their widespread availability and unique mechanical properties. In this study, HRS fibers were chemically treated using Sodium Hydroxide (NaOH), Potassium Permanganate (KMnO4), and Acetic Acid (CH3COOH) at different weight percentages (3, 4, 5 Wt.%) and solutionizing times (1, 2, 3 h) based on Taguchi’s L27 orthogonal array. The fibers, extracted from epidermis of the stems, underwent cleaning and chemical treatment after water retting. The crystallinity index, determined via X-ray Diffraction (XRD), indicated a maximum value of 65.77%. Thermo-gravimetric analysis (TGA) exhibited a degradation temperature of 365.24 °C and a material loss of 63.11%. Potassium Permanganate treatment at 4 Wt.% and 3 h of solutionizing time has yielded the best results. Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) has been successfully applied to accurately predict the output physical characteristics of chemically treated HRS fibers using experimental data. The results are in close alignment with the literature. Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) analyses have provided valuable insights into the microstructure and constituents of the chemically treated HRS fibers. This research emphasises on the effectiveness of the chemical treatment process in enhancing the properties of HRS plant fibers for potential composite applications.

Parametric investigation on surface responses of TIG cladded medium carbon steel substrate with TiO2–SiC powder

The present research work consists of surface modification of a medium-carbon steel substrate with TiO2–SiC powder of the same weight percentage. A Taguchi L9 orthogonal array was used for the design of experiments. Different input process parameters, such as current (A), scan speed (mm/s) and gas flow rate (L/min), have played a significant role in controlling geometrical surface responses. The relationship between different input parameters and response surfaces (clad height, clad width) was plotted and discussed. Diagrams have been utilized to demonstrate the combined impacts of any two process factors on clad height and clad width, such as TIG current–scan speed, TIG current–gas flow rate and gas flow rate–scan speed. Experimental observations revealed that cladding input parameters have a significant influence on response surfaces. TIG current has the maximum impact on clad height. Observations showed that with the increment of current up to a certain amount clad height increased. It was also observed that with an increment of current, clad width increases. Observations revealed that as the gas flow rate rises, clad height decreases but clad width increases. It was further noted that with an increase in scan speed, clad height and clad width follow a downward trend.

Pattern optimization for 3D printing of composite filament materials with natural fibers and polylactic acid

Composite fiber materials developed through Fused Deposit Modeling (FDM) in high demand in fabricating automotive, machinery components such as bearings, sleeves, bumpers, windshields, etc. In this article, a new composite filament with natural fiber (Flax) and polylactic acid was prepared and their mechanical properties were analyzed. Taguchi approaches L27 orthogonal array was used to optimize the process parameters layer thickness (0.15, 0.25, 0.35 mm) for 0.4 mm nozzle with filling structure (L1—lines, L2—triangles, L3—octet), speed of nozzle movement (80,100,120 mm/s) and occupancy ratios (20%, 40%, 60%) were chosen as process parameters. Tensile and Impacts test have been carried with American Society of Testing and Materials Standard (ASTM D638 and ASTM D256) to evaluate the tensile strength, percentage of elongation and impacts strength of the fabricated specimens. The results emphasized that layer thickness significant influences the tensile characteristics as compared to better process parameters. The occupancy ratio at 20% and 40% with triangle and octet filling structure, nozzle speed of 100 mm/s at constant extruder temperature of 200°C have been found to optimize 3D printing parameters in this work. They were calculated as tensile strength 61.13 MPa, percentage of elongation is 4.43% and Izod impact test is 12.77 kJ/m2.

Enhancing textile wastewater treatment for subsequent biological processes by integrating electrooxidation and electrocoagulation

ABSTRACT The presence of refractory contaminants in textile wastewater is one of the major concerns while handling them with the biological processes at common effluent treatment. Electro-oxidation (EO) as a standalone process is an insufficient treatment method for the abolition of inorganic contaminants (carbon and non-carbon). By incorporating electrocoagulation (EC) as an associated treatment method after EO, removal of such contaminants becomes easy, which not only makes the treated wastewater fit for biological remediation but also reduces load on biological units. The removal of non-carbonic impurities was assessed in terms of improvement in the chemical oxygen demand (COD) post EC. L25 orthogonal array of experiments was obtained using the Taguchi method. From the S/N ratio plot, the optimal process combination was obtained as, EO with current density = 25 mA/cm2, electrolysis time = 50 min followed by EC with current density = 18 mA/cm2, speed of rotation = 50 rpm and electrolysis time = 40 min. The enhancement in COD and total organic carbon removal efficiencies after EC were 65.11 and 63.57%, respectively, over EO. The biodegradability index also improved from an initial value of 0.098–0.737 post-hybrid treatment. Inorganic carbon reduced from a value of 36.37 mg/L after EO to 0.1 mg/L post EC.

Optimization of Hammer-Disc Mill Parameters During Producing Gluco-mannan Flour Using Taguchi Method

Porang tubers contain glucomannan, which has health benefits for the human body. However, glucomannan also contains calcium oxalate, which is toxic. A Hammer-Disc mill (HDM) machine was used to separate glucomannan and calcium oxalate, categorized as a mechanical process. This study aims to optimize the parameters of the Hammer-Disc Mill machine for producing glucomannan flour by using the Taguchi Method. The selected parameters are the mass of the porang chip, motor rotation speed, and the distance of the hammer blades. The Taguchi Method was used to design an experiment using the Orthogonal Array L9 (3 factors, 3 levels). In this study, statistical analysis was carried out using Analysis of Variance (ANOVA) to determine each parameter's effect on glucomannan production. The glucomannan granules were taken using a screener of 60-80 mesh. The results show that the input mass parameter of porang chips significantly affects the glucomannan produced, which, F calculating 10,91 was more than f Table 5,41. The contribution of mass input of porang chips in percentage is 85.36%. Based on the results of the best response, the optimal condition for the production of glucomannan flour is to use a mass of 1 kg of porang chip input (level 3), motor rotation speed of 3000 rpm (level 3), and hammer spacing of 1 cm (level 3). By optimizing this parameter it is expected to increase the yield of glucomannan produced by the Hammer-Disc Mill machine.

Arsenic and cadmium ions remediation from aqueous solution using plant-derived iron NPs

ABSTRACT Water is essential for life, ecosystems, and economic growth. Water availability and quality are essential for health, agriculture, industry, making water management essential to sustainable development. Industrial waste such toxic chemicals, dyes and heavy metals pollutes water supplies today. Arsenic (As) and Cadmium (Cd) will be removed from water using plant-derived iron NPs in this experiment. NPs were synthesised from Ocimum tenuiflorum, Margosa, and Helianthus annuus. The percentage of arsenic and cadmium biosorption determined this method’s efficacy. To maximise heavy metal biosorption (%) from aqueous solutions, the Taguchi L16 orthogonal array (4 factors at 4 levels) was used to statistically optimise process parameters like Plant NP quantity, pH values, exposure time, and initial arsenic and cadmium concentrations. Maximum As and Cd biosorption (%) was 99.92% and 98.95% under optimum circumstances.

Optimization of autothermal reforming of ultrasonic methanol sprays over Cu/ZnO catalyst for hydrogen production via a statistical approach

Autothermal reforming (ATR) is a highly efficient process for hydrogen production. This study uses the Taguchi method to optimize the hydrogen production from methanol via the ATR process. The Cu/ZnO particle is used as the catalyst, and an ultrasonic spray system is used to generate aqueous methanol sprays for ATR. A Taguchi L9 orthogonal array is used to assess the effects of preheating temperature, oxygen/carbon (O2/C) ratio, steam/carbon (S/C) ratio, and gas hourly space velocity (GHSV) on H2 yield. The results show that the O2/C ratio has the most significant influence on the objective function, H2 yield, followed by preheating temperature, S/C ratio, and GHSV, with the most negligible impact. The optimal conditions for maximizing the H2 yield from signal/noise ratio analysis are a preheating temperature of 350 °C, an O2/C ratio of 0.65, an S/C ratio of 1.5, and a GHSV of 7000 h−1. The analysis of variance (ANOVA) indicates that the model has a good fit with a high R2 value (=0.9735). The contribution to H2 yield is 40.22 % by the O2/C ratio, 37.80 % by the preheating temperature,19.34 % by the S/C ratio, and 2.54 % by the GHSV. The H2 yields from ATR, steam reforming, and partial oxidation are also compared. The results indicate that ATR is the optimal choice for producing hydrogen from methanol while maintaining the proper reaction temperature range without generating extra energy.

Machine learning and Taguchi techniques for predicting wear mechanisms of Ni–Cu alloy composites

Nickel–Copper alloys hybrid composite was formed in an induction furnace set up on a sand substrate. With different percentages of Al2O3 (3, 6, 9 and 12 wt%) and TiO2 (constant 9 wt%) reinforcements, the goal is to examine the wear behavior and friction coefficient of Ni–TiO2–Al2O3. The factors considered for the wear analysis were sliding distance (1500, 1000, and 500 m), applied load (25, 50, and 75 N), and sliding velocity (1.46, 2.93, and 4.39 m/s). The pin-on-disc equipment is utilized to perform different wear tests are carried out using in accordance with the Taguchi L27 orthogonal array. The machine learning used to correlate between actual and anticipated values for both metrics is strong, with a reasonable error margin. The Mean Squared Error (MSE) for the wear rate was 0.1025 (10.25%) in the Linear Regression model and 0.2390 (23.89%) in the Random Forest model. Regression analysis determined the impact of several parameters on wear rate, whilst machine learning approaches expanded the evaluation of wear rate and coefficient of friction beyond experimental data. The findings demonstrate the effectiveness of combining Taguchi methods with machine learning to accurately anticipate wear mechanisms in Ni–Cu alloy composites.

Investigation of mechanical morphological structural and electrochemical properties of PVD TiAlN coating: A detail experimental and its correlation with an analytical approach using the least square method

In this experimental investigation, a Physical Vapor Deposition (PVD) process was employed to deposit TiAlN coating onto a Si substrate. The nitrogen flow rate, bias voltage, and substrate-to-target distance were selected as input parameters, each with three different levels. The design of these input parameters was structured according to Taguchi's L9 Orthogonal Array (OA). Following deposition, the mechanical, microstructural, structural, and electrochemical properties of the TiAlN coating were meticulously characterized and analyzed to discern the influence of the selected parameters on its various properties. Microstructural analysis revealed a homogeneous structure throughout the film. Additionally, the mechanical properties of the film exhibited notable performance under the specified parameters. However, it was observed that no consistent trend could be identified across different properties concerning the applied parameters. To elucidate the complex relationships among these variables, the Least Squares Method (LSM) regression analysis technique was employed. This analytical approach facilitated the establishment of correlations among the diverse parameters, enhancing the understanding of their collective impact on the TiAlN coating properties. The understanding of analytical results will be useful for predicting the values between the two extremities to measure the performance parameters where the experimental results are not available.

Impact of Robot Programming Methods on Servo Drive Temperature

There are various motion commands used for programming ABB Industrial Robot & each command is used for a specific application. Also, there are various parameters in the motion command such as Velocity & Zone Error. Setting different values normally alters the Robot performance and eventually the servo drive temperature. This paper has made a case study on the temperature of Servo Drive with respect to the Programming Method. Programming is done using different move commands, Velocity values & Zone Error values to move the Robot End Effector between two fixed locations. The impact of these parameters on the temperature rise of Servo Drive is analyzed. Valid results are arrived using Taguchi L9 Orthogonal array. The optimal results obtained from the Taguchi analysis is checked with the real ABB Robot available at Robotics lab of VSVN Polytechnic College Key Words: ABB Robot RAPID Program – Velocity Data – Zone Error Data – Taguchi L9 Orthogonal Array.

Optimizing Nd: YAG laser cutting of carbon fiber reinforcing polymer with newly developed resin using Taguchi-GRA approach and machine learning integration

PurposeThe investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.Design/methodology/approachThe study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.FindingsThe work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.Originality/valueThe novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.

Optimizing the Die-Sink EDM Machinability of AISI 316L Using Ti-6Al-4V-SiCp Electrodes: A Computational Approach

Die-sink electric discharge machining (EDM) is essential for shaping complex geometries in hard-to-machine materials. This study aimed to optimize key input parameters, such as the discharge current, gap voltage, pulse-on time, and pulse-off time, to enhance the EDM performance by maximizing the material removal rate while minimizing the surface roughness, residual stress, microhardness, and recast layer thickness. AISI 316L stainless steel was chosen due to its industrial relevance and machining challenges, while a Ti-6Al-4V-SiCp composite electrode was selected for its thermal resistance and low wear. Using Taguchi’s L27 orthogonal array, this study minimized the trial numbers, with analysis of the variance-quantifying parameter contributions. The results showed a maximum material removal rate of 0.405 g/min and minimal values for the surface roughness (1.95 µm), residual stress (1063.74 MPa), microhardness (244.8 Hv), and recast layer thickness (0.47 µm). A second-order model, developed through a response surface methodology, and a feed-forward artificial neural network enhanced the prediction accuracy. Multi-response optimization using desirability function analysis yielded an optimal set of conditions: discharge current of 5.78 amperes, gap voltage of 90 volts, pulse-on time of 100 microseconds, and pulse-off time of 15 microseconds. This setup achieved a material removal rate of 0.13 g/min, with reduced surface roughness (2.46 µm), residual stress (1518.46 MPa), microhardness (259.01 Hv), and recast layer thickness (0.87 µm). Scanning electron microscopy further analyzed the surface morphology and recast layer characteristics, providing insights into the material behavior under EDM. These findings enhance the understanding and optimization of the EDM processes for challenging materials, offering valuable guidance for future research and industrial use.

Characterization and optimization of TIG welding parameters for AISI 304 and AISI 316 stainless steel dissimilar joints

This study explores the impact of tungsten inert gas (TIG) welding parameters on the mechanical properties and microstructure of dissimilar welds between AISI 304 and AISI 316 austenitic stainless steels. Given the growing industrial demand for these materials, the research focuses on optimizing welding current, shielding gas flow rate, and voltage to enhance tensile strength, hardness, and impact toughness. Using the L9 orthogonal array based on Taguchi’s methodology, the experiments revealed that Relatively highs currents and voltages significantly improved the ultimate tensile strength (up to 673.67 MPa) and impact energy absorption (up to 36.5 J). Microstructural analysis indicated refined grain structures in the heat-affected zones, with pronounced grain growth in AISI 316 due to its thermal sensitivity. The micro-hardness analysis revealed that the highest hardness occurred in the HAZ of AISI 304, with samples 5 and 6 exhibiting the optimal hardness profile, reflecting the most favorable welding parameters. The study concludes that a current range of 80-90 A and a voltage range of 10-11 V and a shielding gas flow rate of 12-16 L/min provide optimal welding conditions, offering robust guidelines for industrial applications requiring high-performance dissimilar welds.

Impact of graphite on tribo‐mechanical, structural, and thermal behaviors of polyoxymethylene copolymer/glass fiber hybrid composites via Taguchi optimization

AbstractUnique hybrid thermoplastic composites based on polyoxymethylene copolymer (POM C), 10 wt.% glass fiber (GF) and graphite (Grt) filler at 1, 3, and 5 wt.% were developed using injection molding technique. According to the Taguchi L16 orthogonal array, present experiments were carried out with the aim of determining the coefficient of friction (COF) and specific wear rate (SWR) under a range of loads (namely, 5, 10, 20, and 30 N), sliding speeds (namely, 200, 400, 600, and 800 rpm), and run times (namely, 15, 30, 45, and 60 min) with varying wt.% of Grt (namely, 1, 3, and 5 wt.%) throughout the experiment. Analysis of variance (ANOVA) was used to evaluate the most significant factors that affect the output functions (viz., COF and SWR). The findings demonstrated that POM C/10GF composites' tribo‐mechanical, structural, and thermal properties were considerably improved upon by including Grt. Various microscopical methods were also employed to study the wear mechanisms of the composites and the surface morphology of the worn samples. The POM C/10GF with a 3 wt.% of Grt exhibited superior tribological properties owing to its enhanced interfacial bonding characteristics, resulting to increased wear resistance.Highlights Injection molded POM C/10GF hybrid composites with 1, 3, and 5 wt.% graphite (Grt) Structural, thermal, chemical, tribo‐mechanical and microstructural studies Design of experiment with variable loads, times, speeds, and Grt compositions COF and specific wear resistance as response Optimization of hybrid composite composition using Taguchi L16 orthogonal array