Laser technology plays a crucial role in human lives. They are used signifi cantly in the scientifi c area, military, medical, manufacturing and even in the entertainment industry. Their usage is critical due to the advantages over many other technologies including precision and accuracy, fast and high efficiency, productivity, reproducibility, fl exibility, easy to automate, less energy consumption, more environmentally friendly which in turns more cost-effi cient. This paper provides a review of laser technology focusing on the latest technology of blue diode laser in manufacturing.

This research aims to study the mechanical properties and microstructure of micro-alloy from arc-welding process. The study provides results comparing non-welded micro-alloy with micro-alloy welding by E8018 and E7018 welding electrodes. The results come from mechanical tests and microstructure examinations. The mechanical tests include the Brinell hardness test, impact test and tensile test. Microstructure, chemical components and Grain size are analyzed by 1) examination with a scanning electron microscope for microstructure and Grain size, 2) X-ray signal detection for chemical components. After running examinations and analyzing the results following mechanical procedure, the research shows that microalloy welded by ferrite-based E7018 electrode provided the highest factor for impact test, while welding by pearlite- based E8018 electrode provided the best result in both tensile and hardness tests.

Large amounts of rice husk char (RHC) and rice husk ash (RHA) are generated in rice husk-based power plants, and development of an effective recycling method for the RHC and the RHA wastes would be desirable. The main components of RHC were carbon (37.2 mass%) and silica (47.72 mass%), while that of RHA was silica (88.59 mass%). Both RHC and RHA had a reticulate porous structure with specifi c surface areas of 207.0 and 7.9 m²/g, respectively. Their ammonia recovery capacities were evaluated by adsorption capacities of ammonia in gas phase (50 ppm) and its desorption capacities in water at 25°C and ambient pressure. The adsorption capacity of ammonia in gas phase was higher for the RHC than for the RHA.

Mining methods selection (MMS) is one of the most critical and complex decision-making tasks in mine planning. The selection of underground mining methods is considered to be the most problematic due to the complexity associated with the orebody geometry, geology, and geotechnical properties. This study integrated artificial intelligence and machine learning in the MMS process by introducing the recommendation systems  (RS) approach in MMS through the nonnegative matrix factorization (NMF) algorithm. As such, the weighted nonnegative matrix factorization (WNMF) algorithm is applied to build a model for underground MMS. The study's input dataset is based on thirty mining projects' historical data. In the experiments, we evaluate the capability of the WNMF to predict underground mining methods using five input variables: ore strength, host-rock strength, orebody thickness, shape, and dip. The results show that the WNMF model achieved an average prediction accuracy of 67.5%, considered reasonable and realistic. Further findings reveal that the WNMF model is sensitive to the imbalanced class dataset used in the experiments, thus, suggesting the need to improve the dataset's quality. These results reveal the model's effectiveness in predicting underground mining methods; therefore, with continuous improvement, the WNMF model can be effectively applied in underground MMS.