Journal of Mechanical and Mechanics Engineering

Modifying the Gating System of the Bracket Torque Rod

Balasaheb Koravi — 2026-04-02

Sand casting is one of the most widely used manufacturing processes for producing complex metal components in the automotive and industrial sectors. However, improper gating system design can lead to casting defects such as turbulence, porosity, misruns, and uneven solidification. This study focuses on modifying and optimizing the gating system used in the sand casting of a bracket torque rod to improve casting quality and manufacturing efficiency. The existing gating design was analyzed to identify issues related to turbulent metal flow and improper mold filling. Engineering design principles and casting calculations were applied to redesign the sprue, runner, and gate dimensions to ensure smooth metal flow and controlled solidification. Simulation and analysis were conducted to evaluate the performance of the modified design. The results indicate improved mold filling behavior, reduction in casting defects, and better casting yield compared to the existing gating system. The optimized gating system provides improved reliability and efficiency in the production of automotive casting components.

Design and Development of Deburring Edge Cutter

Mahesh M. Kadam — 2026-03-24

The design and development of a deburring cutter represent an innovative approach to improving the finishing process of machined components. In many manufacturing industries, burrs generated during machining operations affect the dimensional accuracy, surface quality, and safety of the final product. Traditional deburring methods often rely on manual operations, which are time-consuming, inconsistent, and labour-intensive. This study focuses on developing a compact and efficient deburring cutter capable of removing burrs from various machined job parts, particularly circular and cylindrical components. The proposed system emphasises efficiency, accuracy, and operational versatility. Different tool path strategies and cutter geometries are analysed through iterative modelling and experimental testing to achieve optimal performance. The design incorporates a streamlined cutting mechanism that allows smooth operation while maintaining high precision during the deburring process. Lightweight materials and robust tool construction are considered to enhance durability and reduce operator effort. Furthermore, the developed deburring cutter aims to minimise manual intervention and improve productivity in small and medium-scale workshops. By integrating simple yet effective mechanical design principles, the tool provides a cost-effective alternative to conventional deburring techniques. The study highlights the importance of automated and semi-automated finishing solutions in modern manufacturing environments, contributing to improved product quality, reduced processing time, and enhanced operational efficiency.

Dynamic Structural and Modal Analysis of Camshaft Using Finite Element Analysis with Material Optimization

Antra Jitendra Soni — 2026-04-09

This study presents a comprehensive finite element analysis (FEA) of a 4-cylinder compression ignition (CI) engine camshaft based on the TATA Safari Dicor geometry and material data, to evaluate and optimize material selection for superior structural and dynamic performance. Four candidate materials were investigated: grey cast iron and aluminium-silicon carbide (AlSiC) metal matrix composites with 20%, 30%, and 40% SiC reinforcement. The camshaft geometry was modelled in SolidWorks 2023 and analysed in ANSYS Workbench 2023 R1 using Tet10 elements under static structural, pre-stressed modal, and harmonic response analyses. A fixed support at each journal and a concentrated force of 5000 N at the cam nose were applied as boundary conditions. Static analysis revealed maximum von Mises stresses of 21.291 MPa (cast iron), 20.993 MPa (AlSiC 20%), and 21.017 MPa (AlSiC 30% and 40%), all well below respective tensile limits. AlSiC 40% achieved the minimum total deformation of 0.0031311 mm, a 21.0% improvement over cast iron (0.003965 mm). Modal analysis yielded first natural frequencies of 8,725.4 Hz (cast iron), 13,598 Hz (AlSiC 20%), 14,626 Hz (AlSiC 30%), and 15,769 Hz (AlSiC 40%), all providing frequency separation ratios of 87–158 relative to the maximum engine excitation frequency of ~100 Hz. The theoretical frequency ratio from specific stiffness (√(49.82/15.28) = 1.806) matches the computed ratio (1.807) to within 0.06%, confirming simulation validity. Harmonic response analysis confirmed resonance peaks far beyond the operational frequency range for all materials. AlSiC 40% additionally delivers 60.8% weight reduction versus cast iron. These results establish AlSiC 40% as the optimal material, extending the findings of Patra et al., dynamic performance when harmonic response and pre-stressed modal analyses are incorporated into the assessment.

A Hybrid Experimental and Explainable Machine Learning Framework for Predicting Mechanical-Induced Degradation and Reliability in Lithium-Ion Batteries

Ravikant Nanwatkar — 2026-03-31

Li-ion batteries are common in electric vehicles and in energy storage systems, but its performance and reliability are greatly influenced by these mechanical loading factors, which are vibration, compression, and impact. This paper proposes a hybrid experimental, machine learning-based model to examine mechanically induced interdependence between the electrical properties and degradation characteristics of li-ion batteries. Mechanical tests on various battery form factors were controlled in order to produce multi-domain data on voltage response, internal resistance, capacity fade, and temperature variations. They used these datasets to train and test several machine learning models, such as Linear Regression, Support Vector Machine, Random Forest and Gradient Boosting, to learn the nonlinear relationship between mechanical inputs and electrical degradation indicators. The findings prove that ensemble models, and especially Gradient Boosting, have a high prediction accuracy with an R² value of up to 0.94. Explainable AI methods and feature importance analysis were used to discover that mechanical strain and compressional force are the most influential factors that lead to degradation. Moreover, the suggested framework can be used to detect faults in the early stages and achieve trustworthy Remaining Useful Life (RUL) estimates with various loading conditions. This research is useful in the creation of smart battery control, as well as the creation of safer and more durable lithium-ion batteries to be used in mechanically active conditions.