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Selective Laser Melting (SLM) has gained widespread usage in aviation, aerospace, and die manufacturing due to its exceptional capacity for producing intricate metal components of highly complex geometries. Nevertheless, the instability inherent in the SLM process frequently results in irregularities in the quality of the fabricated components. As a result, this hinders the continuous progress and wider acceptance of SLM technology. Addressing these challenges, in-process quality control strategies during SLM operations have emerged as effective remedies for mitigating the quality inconsistencies found in the final components. This study focuses on utilizing optical emission spectroscopy and IR thermography to continuously monitor and analyze the SLM process within the powder bed, with the aim of strengthening process control and minimizing defects.

This procurement specification covers aircraft-quality solid rivets made from a corrosion resistant nickel-copper alloy of the type identified under the Unified Numbering System as UNS N04400 and of 46 ksi minimum shear strength.

This procurement specification covers tubular, blind rivets fabricated from a corrosion resistant nickel-copper alloy of the type identified under the Unified Numbering System as UNS N04405, and of 52 ksi minimum shear strength for self-plugging style rivets.

This procurement specification covers solid rivets and hollow end rivets made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S66286 and of 80 ksi single shear strength at room temperature.

Abstract In recent years, the use of cutting fluids has become crucial in hard metal machining. Traditional non-biodegradable cutting fluids have long dominated various industries for machining. This research presents an innovative approach by suggesting a sustainable alternative: a cutting fluid made from a blend of glycerol (GOL) and distilled water (DW). We conducted a thorough investigation, creating 11 different GOL and DW mixtures in 10% weight increments. These mixtures were rigorously tested through 176 experiments with varying loads and rotational speeds. Using Design-Expert software (DES), we identified the optimal composition to be 70% GOL and 30% DW, with the lowest coefficient of friction (CFN). Building on this promising fluid, we explored further improvements by adding three nanoscale additives: Nano-graphite (GHT), zinc oxide (ZnO), and reduced graphene oxide (RGRO) at different weight percentages (0.06%, 0.08%, 0.1%, and 0.3%).

As a key technology of intelligent transportation system, vehicle type recognition plays an important role in ensuring traffic safety,optimizing traffic management and improving traffic efficiency, which provides strong support for the development of modern society and the intelligent construction of traffic system. Aiming at the problems of large number of parameters, low detection efficiency and poor real-time performance in existing vehicle type recognition algorithms, this paper proposes an improved vehicle type recognition algorithm based on YOLOv5. Firstly, the lightweight network model MobileNet-V3 is used to replace the backbone feature extraction network CSPDarknet53 of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed.

As a key tool to maintain urban cleanliness and improve the road environment, road cleaning vehicles play an important role in improving the quality of life of residents. However, the traditional road cleaning vehicle requires the driver to monitor the situation of road garbage at all times and manually operate the cleaning process, resulting in an increase in the driver 's work intensity. To solve this problem, this paper proposes a road garbage recognition algorithm based on improved YOLOv5, which aims to reduce labor consumption and improve the efficiency of road cleaning. Firstly, the lightweight network MobileNet-V3 is used to replace the backbone feature extraction network of the YOLOv5 model. The number of parameters and computational complexity of the model are greatly reduced by replacing the standard convolution with the deep separable convolution, which enabled the model to have faster reasoning speed while maintaining higher accuracy.

This specification covers a blend of tungsten carbide-cobalt aggregate, a nickel alloy, and a nickel aluminum aggregate in the form of powder.

This procurement specification covers split cotter pins with optional ends (see Figure 1), made from a corrosion resistant steel of the type identified under the Unified Numbering System as UNS S30200.

This procurement specification covers split cotter pins with optional ends (see Figure 1), made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S32100.

This specification covers an aircraft-quality, low-alloy steel in the form of bars, forgings, mechanical tubing, and forging stock.

This specification covers tubular-shaped pins, fabricated from carbon steel, having a full-length longitudinal slot to permit flexure when inserted into a hole.

This specification covers a corrosion- and heat-resistant steel in the form of bars, wire, forgings, mechanical tubing, flash-welded rings, and stock for forging or flash-welded rings.