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Revista global de tecnología y optimización

Volumen 13, Asunto 9 (2022)

Mini reseña

Smart Drilling Technology Based on Pilotless Appliances for an Overground Iron Mining Company

Goran Klepac

This article examines the current state of research and development of intelligent technologies for underground metal mines in China, where such technologies are being developed for use in the development of mineral resources in a safe, efficient, and environmentally friendly manner. We analyse and summarise the state of research in underground metal mining technology in the United States and abroad, including specific examples of equipment, technology, and applications. We introduce the most recent equipment and technologies for unmanned mining, including intelligent and unmanned control technologies for rock-drilling jumbos, down-the-hole (DTH) drills, underground scrapers, underground mining trucks, and underground charging vehicles, all with independent intellectual property rights. For intelligent and unmanned mining, three basic platforms are used: the positioning and navigation platform, the information-acquisition and communication platform, and the scheduling and control platform.

Mini reseña

Prediction of weld area based on image recognition and machine learning

Milorad Bojic

Modern aluminium alloy welding techniques like laser oscillation welding can successfully reduce weld porosity brought on by the physical and chemical characteristics of aluminium alloy. Since it has a significant impact on the mechanical qualities of welded connections, the weld area is frequently used as an evaluation index of geometric attributes to assess the welding quality. In this paper, a method for predicting the weld area for laser oscillation welding of 6061 aluminium alloy is proposed. The cross-sectional area of the weld is computed using image recognition technology from the metallographic micrographs of welding trials, and the inaccuracy of the recognised weld area is less than 8.8%. Additionally, alternative prediction models for the weld area are created by machine learning methods, such as linear regression, under varied process circumstances.

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