Vol 13 Issue 1 April 2025-September 2025

Abstract: Efficiency, speed, and precision are essential in modern manufacturing. AI Maglev Conveyor system, combining magnetic levitation (maglev) technology with artificial intelligence (AI), revolutionizes automated production processes. This system reduces maintenance costs and downtime by eliminating friction, enhancing operational efficiency. It transports goods swiftly with minimal energy consumption, optimizing resource use and supporting sustainability. AI integration enables real-time monitoring and adaptive control, allowing businesses to respond to production demand fluctuations and streamline supply chain operations.

The AI Maglev Conveyor offers smooth, silent operation, accommodating diverse product types and sizes for flexible manufacturing without extensive reconfiguration. AI algorithms optimize routing, reduce cycle times, and improve throughput, creating an agile production line adaptable to market changes.

This applied research paper introduces the Maglev Conveyor system, featuring an electromagnetic controller and multiple movers to enhance automation. It offers cost savings as an alternative to setups using six-axis robots or linear motors, with precise adjustments for robotic arm loading. Operating at high speeds minimizes treatment time for delicate components while maintaining precision. Its adaptable design accommodates various materials, facilitating integration of processing stations alongside electronic product assembly. Positioned between linear-axis and robotic systems in cost, the Maglev Conveyor is ideal for flat parts requiring minimal travel, transforming production efficiency across industries. It explores its technical advantages, flexibility, cost reductions, and overall benefits.

Keywords: Magnetic Levitation, Maglev, AI, Automation, Robot, Manufacturing, Production, Conveyor.

Title: AI Magnetic Levitation (Maglev) Conveyor for Automated Assembly Production

Author: Ray Wai Man Kong

International Journal of Mechanical and Industrial Technology      

ISSN 2348-7593 (Online)

Vol. 13, Issue 1, April 2025 - September 2025

Page No: 19-30

Research Publish Journals

Website: www.researchpublish.com

Published Date: 05-June-2025

Abstract: High-rise buildings minimize the use of high-density construction materials to reduce the weight of the building. One of construction parts that can be employed to reduce weight in buildings is a partition wall. Partition walls compartmentalize the building and define the spaces within it and are traditionally made of concrete masonry. Because of the high weight of concrete, there is need for a sustainable light weight alternative material.  In this work, raffia palm fibre-groundnut shell powder/fine clay filler reinforced epoxy composite has been developed for application in interior partition walls of buildings. The composite was produced using hand lay-up lamination technique. Mechanical properties such as tensile strength, flexural strength, compressive and impact resistance which are critical for materials used in the partition walls were evaluated. The results indicate; the tensile strength of composite is in the range 7.056-17.845 MPa. The Young’s modulus of composite is in the range of 67.18-220.04 MPa. The modulus of elasticity (MOE) values ranged from 2343.75-3906.25 MPa. The modulus of rupture (MOR) values ranged from 25.78-70.31 MPa. Compressive strength of composite ranged from 9.4-29.0 MPa. The impact strength values ranged from 0.70-3.60 J/mm² in the composites. The study concluded that, based on the results obtained, the composite has sufficient mechanical properties suitable as an alternative construction material for replacement of concrete masonry in partition wall applications. 

Keywords: Partition wall, Concrete masonry, Composite, Fine clay and Hand lay-up.

Title: Evaluation of Mechanical Properties of Raffia Palm Fibre-Groundnut Shell Powder/Fine Clay Filler Reinforced Epoxy Composite for Partition Wall Applications

Author: Bam S. Aondover, Nyior G. Bem, Tile E. Sesugh

International Journal of Mechanical and Industrial Technology      

ISSN 2348-7593 (Online)

Vol. 13, Issue 1, April 2025 - September 2025

Page No: 10-18

Research Publish Journals

Website: www.researchpublish.com

Published Date: 13-May-2025

Abstract: The garment industry is experiencing a transformative shift by integrating intelligent learning technologies, including machine learning (ML) and deep learning. This applied research with case study examines the application of Convolutional Neural Networks (CNNs) for automating quality control in garment manufacturing, specifically focusing on the detection of sewing lines in captured images. Manufacturers can enhance efficiency, improve product quality, and reduce operational costs by utilizing advanced data analytics and image processing techniques. The CNN model is trained to identify unique features of sewing lines, allowing for real-time comparisons between machine outputs and standard benchmarks. This intelligent learning approach not only streamlines the inspection process but also enables predictive maintenance and data-driven decision-making, fostering adaptability to market demands. The findings highlight the potential of AI-driven solutions to replace manual inspections, ultimately driving innovation and sustainability in garment production. As the industry evolves, embracing intelligent learning technologies will be crucial for manufacturers seeking to maintain a competitive edge in an increasingly dynamic marketplace. This research underscores the importance of preparing high-quality training data to optimize CNN performance and ensure effective garment quality control.

Keywords: Artificial Intelligence, AI, Automation, Convolutional Neural Networks, Machine Learning, Garment Manufacturing, Quality Control.

Title: AI Intelligent learning for Manufacturing Automation

Author: Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong

International Journal of Mechanical and Industrial Technology      

ISSN 2348-7593 (Online)

Vol. 13, Issue 1, April 2025 - September 2025

Page No: 1-9

Research Publish Journals

Website: www.researchpublish.com

Published Date: 05-April-2025