Mechanical micro-cutting is one of the advanced processes for manufacturing of micro-parts. During the micro-cutting process, the thickness of the uncut chip is very close to the tip radius of the tool. The cutting edge is used to cut and extrude the workpiece. In this paper, the experiments and simulations of macro-machining nickel alloy are compared, and the process of micro-cutting nickel alloy is simulated and analyzed. In this study, four cutting edge radii, three cutting speeds, six hot cutting temperatures, and a constant depth of cut are used. The radius of the cutting edge of different sizes is theoretically analyzed and verified by simulation of material flow state, temperature, stress, strain, and cutting force. The results show that the material separation points are very close together at different cutting edge radii. The change in the radius of the cutting edge changed the contact state of the material in the cutting area, which has a large influence on the temperature and cutting force. The effects of different cutting speeds and hot working temperature on the machining process are also discussed.

Inconel 718 alloy has excellent performance in aerospace equipment, but due to its difficult machining characteristics, it requires excessive processing energy consumption and faster tool wear. However, this is difficult to adapt to the current carbon-neutral clean production conditions, thermal-assisted processing technology uses natural gas, a clean energy source, to heat the workpiece and reduce energy consumption during the cutting process. In this study, a dry-cutting study was carried out under thermally assisted machining conditions using TiAlN-coated tools. Thermal-assisted machining by preheating the workpiece material to soften the material locally, and then machining operations such as cutting were conducted. Under the action of heat, the yield strength of the material is reduced, the residual stress during the processing is controlled, the work-hardening phenomenon is reduced, and it is easier to cut. The coating technology is to coat the surface of the tool with a thin layer of inert material to achieve the effect of protecting the tool material. The temperature transfer during the cutting process was analyzed while considering the cutting speed, coating thickness and thermal contact resistance. Through the analysis of the experimental results, there is an inverse correlation between the transfer of cutting heat and the hot working temperature. This is because the increase in temperature reduces the cutting force, weakens the contact between the chip and the tool surface, and reduces the heat transfer. Different coating thicknesses are not positively related to temperature transfer, but the existence of coatings can indeed reduce heat transfer. This study found that the combination of coating technology and thermal-assisted machining technology can improve the cutting process, the specific cutting energy can be reduced by 507 N/mm ² (26.9%), the tool substrate temperature can be reduced by 203 °C (53%), and the tool wear can be reduced by 21.2 × 10 ⁻⁸ mm (19.5%) when the coating thickness is 1 μm.

The development of computer vision and deep learning has promoted agricultural automation. The YOLO series of algorithms are widely used in agricultural fields such as robot fruit picking, but still face challenges such as occlusion and light changes. This study is based on YOLOv5 6.1. The C3 module is lightweight processed based on the 5s model to obtain the C3-L module. In the experiment, the C3 module was replaced with C3-L at the positions of Backbone, Head and Backbone+Head respectively, and the CBAM and CA attention mechanisms were introduced to compare the performances of different models. The results show that the improved C3-L module can reduce resource invocation and graphics card memory usage during training. The stability of replacing the C3 module in the Head part is good. After adding the CBAM attention mechanism, the overall accuracy rate increases by 5%. When the accuracy rate requirement is not high, partially replacing the C3 module in the Backbone can reduce the call to hardware resources and decrease the video memory by 17.4%, which is conducive to operation in mobile hardware. This study provides a reference for the optimization of the YOLO algorithm in agricultural picking scenarios and its transplantation to devices such as microcontrollers.

This study presents a robust approach for identifying irrigation status in wheat fields using specific optical characteristics, validated through multiple deep learning models. Experimental results demonstrate the superior performance of the proposed method in detecting irrigated fields, with all models achieving consistently high accuracy. The optimized lightweight model maintains reliable performance while significantly reducing computational requirements, showing strong potential for practical agricultural monitoring applications. Comprehensive evaluation confirms the method's robustness, with stable detection capability and interpretable error patterns. The findings provide an effective solution for precision agriculture, with future research suggested to explore multi-spectral integration for enhanced performance.

Industries are forced to reduce energy use and carbon emissions while machining difficult to machine alloys like an Inconel 718 due to cost saving and environmental purposes. Introducing heat during machining like thermal-assisted machining (TAM) may improve the machinability of Inconel 718, while research in quantifying its impact on specific energy and carbon emissions is still limited. This study begins by obtaining the temperature properties of Inconel 718, then using finite-element analysis to design a local heating model that softens the material to reduce machining difficulty issues. Machining experiments at a cutting speed of 66 m/min are conducted under various thermal-assisted temperatures and rake angle combinations. The findings reveal that TAM reduces specific cutting energy by 14% to 30% compared to dry cutting. Notably, thermal-assisted processing technology can effectively reduce carbon emissions, and at a −5° rake angle and 500 °C, it can reduce 99.06 μg, but the increase in rake angle will counteract this effect. When using TAM, the chip morphology will change from serrated chips to smooth continuous chips as the heat-assisted temperature increases, and the increase in rake angle will resist this change. These results indicate that the combination of thermal-assisted technology and the selection of the rake angle of the cutting tool can reduce carbon emissions and achieve dual sustainability benefits in terms of economy and environment. This provides a promising approach for improving energy efficiency and supporting low-carbon processing strategies for aerospace high-temperature alloys.