Applied Science and Engineering Journal for Advanced Research
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Advanced Process Parameter Optimization for Compressive Strength of FDM-Printed PETG Using GA-ANFIS
No full textThis study investigates the optimization of process parameters to enhance the compressive strength of polyethylene terephthalate glycol (PETG) parts manufactured using Fused Deposition Modeling (FDM). Compression test specimens were fabricated following ASTM D695 standards, with nozzle temperature, infill density, layer height, and printing speed selected as the key input variables. A three-level face-centered central composite design (FCCD) was employed to systematically evaluate their individual and interactive effects on ultimate compressive strength (UCS). Experimental testing revealed that higher infill density and reduced layer height significantly improved compressive performance, with UCS reaching 106.25 MPa under baseline conditions. To further optimize results, a hybrid Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) framework was implemented, enabling accurate prediction and intelligent optimization of compressive strength. The optimized parameters—224.25 °C nozzle temperature, 88% infill density, 0.15 mm layer height, and 55 mm/s print speed—yielded a maximum UCS of 148.53 MPa, representing a 39.78% improvement over baseline results. The findings demonstrate that intelligent hybrid optimization provides a robust approach for tailoring FDM process parameters, thereby enhancing the structural reliability of PETG components for engineering applications
Optimizing Wear Resistance in Composite Materials: Surface Response Methodology
Get PDFIn This research aims to use Response Surface Methodology (RSM) in determining the settings that give the highest wear resistance of the developed (Cu-W) composites by varying reinforcement percentage at 20-40%, temperature at 160-200°C and mechanical load at 80-100N. A regression analysis was carried out to determine the parameters that affect wear rate and indicated that temperature and Cu-W percentage are significant factors affecting the wear rate with p total values 0.017 is significant. Optimization result suggested that at 30% Cu, 200°C and 80N load has the highest desirability of 1.00 with the best wear rate is 3.498 mm3/Nm. Contour and surface plots were used to further elaborate on synergy between the factors. The results of this study will be valuable for creating the adequate Cu-W composites composition that has decent durability in the applications that require the use of electric contacts and tools for high temperatures, in which wear is of great importance. This, therefore, shows how RSM is effective in optimizing the material with minimum experimental runs
African Oil Bean Seed Oil Biodiesel Optimization Production via the Technique of Response Surface Methodology-Genetic Algorithm (RSM-GA) and RSM
Get PDFThis article focuses on optimized production of biodiesel from African Oil Bean Seed Oil, an indigenous African tropical tree of the leguminosea family, using response surface methodology (RSM) and response surface methodology-genetic algorithm (RSM-GA). Transeterification method was adopted using sodium hydroxide (NaOH) catalyst and methanol (alcohol). The extracted oil was pre-treated due to its high free fatty acid FFA contentFrom the research findings, the physiochemical properties of AOBSO are within ASTM ranges. The process parameters investigated were agitation speed, methanol/oil molar ratio, reaction time, reaction temperature, and catalyst concentration. RSM and RSM-GA gave nearly identical optimal results, with RSM-GA producing the better yield. Agitation speed of 225 rpm, methanol/oil molar ratio of 6.2:1, reaction time of 60 minutes, reaction temperature of 60oC and catalyst concentration of 0.775%wt were therefore the optimal parameters for RSM-GA.
The yield of methyl esters (FAAE) under these optimal process parameters was 99.75%
Comparative Analysis of Experimental -Based Wear Rate Investigation of Different Coatings on Nitrided AISI H13 Tool Steel
Get PDFThe research investigates the tribological behavior of Titanium Carbide (TiC) and Chromium Nitride (CrN) and Aluminum Titanium Nitride (AlTiN) coatings used on gas-nitrided AISI H13 tool steel when operating under multiple conditions. The Taguchi L9 orthogonal design evaluated how coating type together with temperature (40–50 °C) and load (5–15 N) affect wear rate measurements. A tribometer tester performed the wear tests and were determined by applying the circular segment method to assess the cross-sectional area of the tracks formed during testing.. The multiple linear regression prediction model for wear rate performance exhibited an error margin of less than 10% throughout every experimental trial. The statistical results from analysis of variance (ANOVA) showed coating type to be the main contributor to wear variation (p = 0.002). Within the set of tested coatings AlTiN established the highest degree of wear resistance during optimized conditions. The verification tests confirmed the accurate forecasting capabilities of the predictive model for regression while showing that duplex surface modifications work properly. Results show that using AlTiN-coated nitrided AISI H13 steel makes it possible to deploy these tools in demanding high-temperature applications which need exceptional wear protection
Structural Simulation Analysis of a Hybridized Composite Pulley using SolidWorks Simulation Technique
Get PDFThis study explores the simulation and finite element analysis of a pulley hybridized composite reinforced (with silica sand and soda-lime glass) A36 grade steel and cast iron steel. SolidWorks simulation techniques were used in this study for the numerical analysis of stress-strain distribution and deformation under applied torque. A simulation study was carried out under the three different materials. The hybridized pulley had the highest strain and displacement of 5.63×10⁻² and 1.258×10⁻² for cast iron but the least weight of 1.8659N for the hybridized pulley. The material of choice based on displacement was cast iron pulley material, but based on weight and operational sufficiency, the hybridized pulley material can also be chosen because there were no large deviations from the strain and displacement analysis
LLM for Financial Services: Risk Analysis and Fraud Detection
Get PDFThe financial service industry is increasingly suspected by risk management and complicated frauds, because of traditional methods, such as rules based on rules, becomes become Not enough to combat evolutionary threats. This study discovers the potential of large language models (LLM), including GPT-3 and Finbert, to improve risk analysis and fraud detection in the financial sector. LLM, capable of processing structured and non -structured data, provides improvement in detecting models and abnormalities between trading newspapers, customer interaction and talent reports main. A quantitative comparative comparative research design, financial data analysis can access the public and compare LLM performance with traditional systems. Main performance measures - Prediction Accuracy, False Positive Rate, Processing Time, and Fraud Detection Rate- are used to evaluate the effectiveness of the models. The results show the significant potential of LLM to improve financial risk management and detect fraud, provide an effective, accurate and developed approach to modern financial institutions
Alkali Interaction with Expansive and Non Expansive Soils
Get PDFThis research examines the interaction of alkali contaminants with both expansive and non-expansive soils, specifically black cotton soil and red soil, while assessing the effectiveness of sulphur and gypsum in restoring soil pH for geotechnical stability. Alkaline contamination, commonly caused by industrial effluents, construction activities, and agricultural practices, alters soil behavior by influencing its strength, swelling-shrinkage properties, and overall suitability for construction and infrastructure development. Laboratory experiments are conducted to evaluate the impact of alkalinity on key soil properties, including Atterberg limits, swelling potential, shear strength, permeability, and consolidation characteristics.
Expansive soils, such as black cotton soil, exhibit significant volume changes with moisture fluctuations, whereas non-expansive soils like red soil respond differently to alkali exposure. The presence of alkali contaminants can lead to reduced cohesion, increased dispersibility, and diminished bearing capacity, which pose risks to foundations, pavements, and embankments. This study investigates the potential of sulphur and gypsum as chemical stabilizers to counteract the negative effects of alkalinity. Controlled soil treatment trials are conducted to systematically assess variations in soil pH, structural integrity, and overall engineering performance.
The results offer valuable insights into the geotechnical consequences of alkaline contamination and the effectiveness of remediation techniques in stabilizing affected soils. By addressing the challenges posed by contaminated soils in civil engineering applications, this research contributes to sustainable ground improvement strategies, enhancing the durability and safety of infrastructure projects in impacted areas
Leveraging Microservices and Serverless Architectures for Enhanced Enterprise Agility
Get PDFThis study investigates the impact of serverless and microservice architectures on the agility, scalability, and cost efficiency of an enterprise. Modern digital enterprises can no longer rely on traditional monolithic architectures due to constraints on deployment velocity, flexibility, and scalability. The research underlines the far-reaching benefits of microservices in modularity, resource consumption, and development cycle time optimization, while also noting the benefits of serverless computing in infrastructure expenditure, auto-scaling capabilities, and performance enhancement. Moreover, the integration of services was analyzed with a focus on security hygiene through policy enforcement, authentication, and workload distribution techniques. It is indisputable that the shift towards microservices and serverless structures provides enterprises with the ability to rapidly achieve innovations, operational agility, and scalability. This study has proven that the adoption of cloud-native architectures is imperative for enterprise modernization and attaining competitiveness within the ever-evolving realm of information technologies
Fault Detection in Underground Cable by using Arduino
Get PDFThis paper presents the system to ascertain the length of faulty cable in kilometres by employing NANO ARDUINO outfit. In numerous Municipal fields, the underground cable method is employed. When faults or abnormalities occur due to any reason, it is difficult to rectify the quandary because of not perceiving the exact location of the fault. The proposed method is to localize the exact location or spot of the fault. The system employs the standard principle of Ohm’s Law i.e. the current changes depending upon the fault distance, where small voltage is employed at feeder end over series resistors. Consequently the voltage across the resistors alters which is calibrated in distance after feeding the data to inherent ADC of NANO ARDUINO outfit, and displayed on digital seven segment display. The hardware system is designed in such manner the series resistors, which are depicting cable length in kilometres, and short circuit fault formation is done with the switches at each comprehend kilometre to verify the correctness of the design. Hereafter, the work will be amplified with the application of capacitor in an AC system for finding the impedance, which will localize the open circuit in underground cable
Beef Quality Classification and Logistics Patterns over Local, Regional, and National Supply Chains in South Korea
Get PDFIn this paper, we intend to examine the impact of several biological factors such as cattle types, gender, and weights on beef quality using data sets obtained from the digital beef traceability system in South Korea. In addition, we also like to identify several cities or slaughter houses that produce the highest quality of beef to validate whether best places for Hanwoo in our analysis are consistent with cities constantly recommended from Google query and many Koreans. Then we calibrate a machine learning model to identify cattle that are most likely to produce the highest-grade beef. According to our calibrated decision tree (DT) model, neutralized male Hanwoo with a certain weight range is most likely to produce high grade meat and our DT model performs at least twice better than a random model in terms of correctly identifying positive samples when top 20% and 40 % of cattle were chosen for a prediction task. Finally, we like to explore different types of beef supply chains in the current digital traceability system and profile them based on their distinct geographical coverages and beef consumption patterns