Journal of Advanced Research in Mechanical Engineering and Technology

A Comparative Study of the Physio-Mechanical Properties of Iron Fillings and Mild Steel Chips in Reinforced Particleboard

2026-01-22T05:47:14+00:00

This study compared the physio-mechanical characteristics of mild steel chips and iron fillings on sawdust-produced particleboard to regular particleboard made from sawdust alone, using identical production conditions. Particle board was made using 1.18mm sawdust, 3mm mild steel chips, and iron filler with diameters of 0.15mm, 0.425mm, 0.6mm, 1.18mm, and 2.0mm. 70g of sawdust, 40g of iron filings, and 40g of mild steel chips were used in the production process. 50 ml urea formaldehyde was used as a binder. The atomic absorption spectroscopy was determined for iron filings and mild steel chips. Particleboards were produced at a temperature of 160°C and a pressure of 20 tonnes for 15 minutes. The mechanical property tested indicates that particleboard containing iron filings has a lower MOR because the size of the iron filings in the particleboard reduces as the MOR increases. The particle size of iron filling with the value of 2.0 mm had the least MOR of 3.99 MPa/m² in iron filling samples. The MOR of mild steel chips was higher compared to all the samples analysed. The MOE of particleboard produced from iron filings increases as the particle sizes of iron filings increase. The samples containing iron filings alone showed the highest MOE of 244.89 MPa/m² for 2.0 mm particle board, while the ones containing mild steel chips had the highest MOE of 282.82 MPa/m² across all samples, suggesting their strength as reinforcement. The rate of water absorption and thickness of swell of particleboard produced from iron filings increases as the size of the iron filings increases.

Numerical Modelling Approaches in Analysis and Design of Underground Coal Mines: A Review

2026-01-22T07:01:14+00:00

Underground coal mining has remained a prolific technique in advancement of human civilization by catering to the exponentially increasing energy demands. From time and on, underground mines have undergone different disasters leading to huge loss of life and assets. Subsidence or roof collapse in mines is one of the prime destructive hazards faced in the mines. Repetitive blasting and seismic induced vibration endure to be the main causes ofsubsidence in mines. This paper reviews the development made in the numerical methods and their reliability with respect to experimental techniques in addressing hazards encountered by underground coal mines. Preliminary focus of study has been laid upon seismicity induced subsidence in mines. Analysis based on finite element method, finite difference method and discrete element method has been appraised during the study. Furthermore, potential of coupled numerical model involving meshfree analysis technique and disturbed state concept has been highlighted in analysis of crack propagation and material heterogeneity analysis in underground coal mines that ultimately leads to subsidence in mines.

Root Crack Incipient Gear fault detection using Machine Learning Algorithms

2026-01-22T07:42:40+00:00

Rapid advancements in sensors and computing technology have motivated stupendous progress in evolving the next generation of Industry 4.0, where machines really speak about their health state in real time. Low-speed machinery, such as the churn gearbox in a chemical plant and the wind turbine main bearing, is considered to be the most difficult to analyse for incipient faults because these faults remain hidden in strong environmental noise during machine operation. Early detection of these faults at the incipient stage is paramountto increasing the overall availability of the whole plant and machinery. Vibration sensor-based fault diagnosis techniques were found to be the most popular among researchers working in the condition monitoring field as well as the most adopted framework in industries. However, acoustic analysis, a non-contact type of sensing technique, possesses huge potential in the fault diagnosis of rotating machinery. This article describesthe framework for fault detection of a slow-speed gearbox working under variable speeds and loads using data fusion from vibration and acoustic signals at the statistical feature level. Maximal Overlap Discrete Wavelet Transform (MODWT) is used as the signal processing technique to remove the noise from the vibro-acoustic signals and extract the fault-related information. Feature monotonicity of the extracted features from MODWT-processed signals is considered as the selection criteria for selecting vibro-acoustic features containing maximum fault-related information. A performance comparison of sensor data fusion over vibration and acoustic sensors when used alone is also presented. To validate the performance of the proposed framework, five different artificial intelligence (AI) models have been used, and the results have been compared.