Journal of Computers, Mechanical and Management

Optimizing Abrasive Water Jet Machining for Enhanced Machining of 316 Stainless Steel

2023-07-12T20:23:21+03:00

Abrasive Water Jet Machining (AWJM) is a non-traditional machining process renowned for its versatility and ability to cut a wide range of materials precisely. This research article presents an in-depth analysis of the optimization of AWJM parameters for machining 316 stainless steel, aiming to enhance surface quality and machining efficiency. Through a comprehensive experimental setup, the study explores the effects of varying the speed, standoff distance (SOD), and flow rate on the surface roughness (Ra) of the machined workpiece. The Taguchi method's L9 orthogonal array is employed to design the experiments, and a signal-to-noise (S/N) ratio analysis, alongside an analysis of variance (ANOVA), is utilized to discern the most significant machining parameters. Response tables for S/N ratios and means are created to summarize the effects, and main effects plots are generated to visualize trends in the data. Furthermore, a regression model is developed to correlate the machining parameters with the surface roughness, which is validated by a high coefficient of determination. Residual plots and diagnostics for unusual observations are utilized to ensure the robustness of the model. The study concludes that SOD is the most influential parameter, followed by speed and flow rate. The optimization results provide a quantitative understanding that can significantly contribute to the industrial application of AWJM for 316 stainless steel, ensuring optimal surface integrity and operational cost-effectiveness. The findings of this research offer pivotal insights for manufacturing industries that seek to integrate AWJM into their production processes.

Effects of Inflation, Ten-Year Bond Yield Rate, and VIX Index on the Stock Prices of Banks Across All Three Market Capitalizations in India

2023-12-04T12:46:54+03:00

This study investigates the impact of critical economic factors—namely, inflation, the 10-year bond yield rate, and the VIX index—on the stock prices of banks operating across different market capitalization segments in India. Through a comprehensive regression analysis framework, this research quantifies the relationships between these economic factors and bank stock prices while accounting for potential variances across large-cap, mid-cap, and small-cap banks. Utilizing data from the past five years, this analysis not only provides a nuanced understanding of how these macroeconomic indicators influence bank stock prices but also explores the specific effects on banks of varying market capitalizations. The findings reveal that small-cap companies are predominantly influenced by internal management decisions and capital allocation, whereas the consumer price index significantly predicts and reflects stock price behavior. Conversely, the bond yield rate and VIX index show minimal impact on stock prices. This study offers valuable insights for investors, policymakers, and financial institutions, aiding in the development of informed investment strategies and risk management practices.

Conceding Sentiment Prognosis on Twitter Data

2023-12-04T12:13:45+03:00

Twitter is the biggest micro-blogging website that gives people a platform to share their opinions about any new happenings around the world. The size of tweets is generally short which makes it very suitable for opinion mining. The key focus of the paper is to analyze the feelings and ideas. In this paper, analysis is done on the classification of tweets on a particular keyword. The tweets related to the given keyword are collected, analyzed, and the result is generated in the form of percentage of positive, neutral and negative sentiments, which gives us a sense of overall sentiment of the keyword. Further, Classification is done using supervised learning algorithms and the best among these will be found by calculating the accuracy of each.

Analyzing π through Monte Carlo Simulations

2023-11-20T11:36:18+03:00

This study investigates the estimation of π using the Monte Carlo Simulation Method, comparing the results with experimental values. To determine π's experimental value, a unit circle (z=1) centered at the origin within a square bounded by points (0,0), (1,0), (1,1), and (0,1) is considered, where an infinite number of points exist within both the circle and the square. Points with z≤1 are within or on the circle's arc, and those with z>1 are outside the arc but within the square. By selecting hundreds or thousands of random number pairs, their positions relative to the arc and square are determined. With N representing the total number of points considered and n the number of points within or on the arc, the experimental value of π is calculated as π=4n/N. This formula indicates that a larger sample size, N, results in a π value closer to its true value. Furthermore, non-parametric hypothesis testing, such as Friedman's Test, is applied to a Monte Carlo Simulation distribution of 20 triplets of random numbers to evaluate their distribution on a semicircle, followed by a Chi-Square Test for goodness of fit. This comprehensive methodology elucidates various insights into the distribution and impact of random number triplets and their conformity to the expected goodness of fit.

Non-Destructive Health Monitoring Techniques for Composite Materials Used in Aerospace Industry

2024-02-29T15:29:08+03:00

Composite structures and materials are improving in terms of cost-effectiveness, product efficiency, and the creation of better specific properties. The aerospace industry is using composite materials extensively. Therefore, reliable non-destructive testing of composites is crucial to lowering maintenance costs and safety issues. Various non-destructive testing techniques have been developed based on distinct concepts to ensure quality throughout a composite product's entire lifecycle. These include Ultrasonic Testing, Acoustic Emission, Eddy Current Testing, Shearographic Testing, infrared thermography, and X-ray radiography. The most well-known non-destructive testing methods, guiding principles, instruments, and facilities for composite defect detection and damage evolution are reviewed in this paper. This paper will review the current state of the art in the area and emphasize the Success and challenges of various Non-Destructive Testing techniques for assessing the integrity of crucial aerospace composites. The study reviewed all Non-Destructive Testing methods, finding that infrared thermography and Ultrasonic Testing are the flexible and affordable options, having been widely applied in both the academic research and industrial sectors. Although rarely successful in providing a comprehensive diagnostic of structural integrity, each non-destructive testing method has its potential. Future research and development in non-destructive testing methods for composites will focus on Smart inspection systems with high levels of precision and data processing speed.

Investigating the Impact of Implant Diameter and Pitch on Stress Distribution in Osseointegrated Implants and Bone Structure

2024-02-29T14:53:22+03:00

Dental implants have revolutionized dentistry by offering a durable and aesthetic solution for tooth replacement. Understanding the biomechanical behavior of dental implants and their interaction with surrounding bone tissue is crucial for optimizing treatment outcomes. This study investigates the stress distribution within dental implants and bone structure using finite element analysis to enhance implant design and biomechanical performance. The methodology involved designing and modeling bone structure, comprising cortical and cancellous bone, along with various implant configurations. Finite element analysis, conducted using Ansys Workbench 2023 R1, allowed for a comprehensive evaluation of stress distribution under axial and oblique loading conditions. Results revealed significant stress distribution differences across implant designs and loading scenarios. Notably, under axial loading, implants with V-thread design (0.75 mm pitch, 3.5 mm diameter) exhibited lower von Mises stress in cancellous bone (4.23E+06 Pa) compared to those with V-thread design (0.75 mm pitch, 4.5 mm diameter) (4.63E+06 Pa). Similarly, buccolingual loading resulted in higher shear stress in cortical bone for implants with V-thread design (0.75 mm pitch, 4.5 mm diameter) (9.61E+06 Pa) compared to those with V-thread design (0.5 mm pitch, 3.5 mm diameter) (8.29E+06 Pa). These findings underscore the importance of implant design parameters in influencing stress distribution and biomechanical performance. By optimizing implant dimensions and thread type, clinicians can minimize the risk of biomechanical complications and enhance implant stability, ultimately improving treatment outcomes for patients undergoing dental implant procedures.

AI-Driven Decision Support System Innovations to Empower Higher Education Administration

2024-02-29T15:22:42+03:00

This study explores the utilization, perceptions, and impacts of Decision Support Systems (DSS) in higher education administration. With a focus on DSS, a cross-sectional survey was conducted among higher education administrators from various institutions. The findings underscore the essential role of DSS in higher education administration, with administrators reporting significant utilization and praising their effectiveness and user-friendliness. The study reveals the positive influence of DSS on strategic planning, enrollment management, resource allocation, and student success initiatives. Moreover, it demonstrates the association between DSS usage and favorable outcomes, including increased efficiency and perceived positive consequences. However, persistent challenges such as data quality issues, privacy concerns, and resistance to change highlight the need for improved data management strategies, ethical considerations, and change management approaches. These findings contribute to the ongoing discourse on the transformative potential of DSS in higher education administration and provide valuable insights for businesses seeking to enhance decision-making, resource allocation, and data-driven initiatives. The innovative integration of AI in DSS for higher education administration represents a paradigm shift in decision-making processes, offering unprecedented opportunities for improvement and innovation.