In this paper, biodiesel was used as an alternative fuel to investigate the combustion and emission characteristics of a four-stroke diesel engine, in terms of cylinder pressure, heat release rate, cylinder temperature, brake thermal efficiency, brake specific fuel consumption, nitrogen oxide, soot, carbon monoxide, and hydrocarbon. Firstly, a diesel engine cylinder model was developed by AVL-Fire software coupled with CHEMKIN code to simulate the injection and combustion of biodiesel with a kinetic mechanism with 106 species and 263 reactions. Then, the simulation model was validated by experimental results under 100% and 50% load conditions and used to simulate the combustion process of a diesel engine fueled with pure diesel, biodiesel, and biodiesel–diesel blends with 10%, 20%, 30% biodiesel by volume, respectively. The results showed that the brake specific fuel consumption increased with the increase of mixed biodiesel ratio. The brake specific fuel consumptions of B10, B20 and B30 increased by 1.1%, 2.3% and 3.3%, respectively, compared with that of D100. The combustion and emission characteristics of the diesel engine are improved. Therefore, biodiesel can be used as an alternative fuel for the diesel engine. The diesel–biodiesel fuel can improve the combustion and emission characteristics of the diesel engine.

Nickel-based superalloys have been widely used in the aerospace, petrochemical, and marine fields and others because of their good oxidation resistance, corrosion resistance, stability, and reliability at various temperatures. However, as a nickel-based superalloy is a kind of processed material, in the cutting process a large amount of cutting heat is generated due to the interaction between the tool and the workpiece. At the same time, the low thermal conductivity of the workpiece causes a large amount of cutting heat to accumulate at the contact point, resulting in serious tool wear, reduced tool life, frequent tool changes, and other problems, which increase the production cost of the enterprise. This paper introduces the tool wear mechanisms (abrasive wear, adhesive wear, plastic deformation, chemical wear, etc.) in the machining process of nickel-based superalloys and summarizes the research status of failure mechanisms, tool wear optimization, etc. Based on a review of the existing research, it was found that the purpose of adding tool coatings, optimizing tool materials and cutting parameters, or improving the cutting environment is to control the heat during the processing of nickel-based superalloys to improve the tool environment and prolong the service life. The development prospects of tool wear prevention measures in the field of nickel-based alloy machining are also described.

In order to improve the accuracy of numerical simulation, a new heat transfer model is developed by using a modular approach in the Anstalt für Verbrennungskraftmaschinen (AVL)-Boost software. The improved heat transfer model mainly considers the effects of the swirl and boiling heat transfer inside the engine. In addition, a chemical kinetics mechanism including 475 reactions and 134 species is employed to predict the combustion of diesel engines fueled with biodiesel. The result shows that the boiling heat transfer will occur, especially in the high-temperature area. Analysis shows that the improved model is reliable and its precision is increased. Finally, the perturbation method is employed to investigate the relatively important inputs as the complex nonlinear function with a lot of output data and input data produced by the improved model. The relative effects of different parameters such as EGR, injection mass, injection timing, compression ratio, inlet air pressure, fuel injection pressure, exhaust pressure and inlet air temperature on performance and emission characteristics are compared. The eight parameters are investigated on four outputs of brake power, Brake Specific Fuel Consumption (BSFC), NO_x and HC. The injected fuel mass plays an important role in emissions and performance. The EGR, compression ratio and inlet air pressure have a great effect on the HC and NO_x emission.

To improve the combustion and emission characteristics of diesel engines, methanol-diesel fuels with different mixing ratios (DM0, DM10, DM20, DM30, and DM40) were used to investigate the effects of methanol addition on the combustion and emission of a four-stroke diesel engine in terms of cylinder pressure, brake power, brake-specific fuel consumption, and nitrogen oxides, soot, and carbon monoxide emissions. Firstly, an improved entire diesel engine model was developed using AVL-BOOST software and validated by the experimental results. The results showed that the increase of methanol content in the fuel mixture had a negative impact on the performance characteristic of the diesel engine, but significantly improved the emission characteristic of the diesel engine. With the methanol ratio in the mixed fuel increased to 10%, 20%, 30%, and 40%, the cylinder pressure of the engine increased by 0.89%, 1.48%, 2.29%, and 3.17%, respectively. However, the power decreased by 3.76%, 6.74%, 11.35%, and 15.45%, the torque decreased by 3.76%, 6.74%, 11.35%, and 15.45%, respectively, and the brake specific fuel consumption increased by 3.77%, 6.92%, 12.33%, and 17.61%, respectively. In addition, with the methanol ratio in the mixed fuel increased to 10%, 20%, 30%, and 40%, the carbon monoxide emission decreased by 21.32%, 39.04%, 49.81%, and 56.59% and the soot emission decreased by 0.25%, 8.69%, 16.81%, and 25.28%, respectively. Therefore, the addition of methanol to the fuel can improve the combustion and emission characteristics of the engine.

Graphite/polymer composites are brittle materials that are prone to producing cracks and concavities on machined surfaces, and their surface quality shows greater randomness. This work aims to overcome the large fluctuations in the machined surface quality of graphite/polymer composites, realize the prediction of machined surface roughness under different machining conditions and optimize the process parameters. A graphite/polymer composite material was cut orthogonally using different machining parameters, and the machined surface roughness of the cut samples was measured by a noncontact surface profiler to obtain training samples for Artificial Neural Network (ANN). In this study, a trained radial basis function neural network was used to predict the machined surface roughness, and the prediction accuracy was more than 93%. A Genetic Algorithm (GA) was used to optimize the established ANN, and then grey relational analysis was used to compare the accuracy of the GA optimization results. The ANN prediction after GA optimization showed that the lowest machined surface roughness of the graphite/polymer composites was 1.81 μm, and the corresponding optimal cutting speed, cutting depth, tool rake angle, and rounded edge radius were 11.2 m/min, 0.1 mm, 6.85°, and 11.16 μm, respectively. A verification experiment showed that the lowest machined surface roughness was obtained when the above process parameters were selected, which was only 1.95 μm, and the prediction error of the ANN was approximately 7%. The combination of a GA and an ANN can accurately predict the surface roughness of graphite/polymer composite materials and optimize the process parameters.

During the Keyhole Tungsten Inert Gas (K-TIG) welding process, a significant amount of information related to the weld quality can be obtained from the weld pool and the keyhole of the topside molten pool image, which provides a vital basis for the control of welding quality. However, the topside molten pool image has the unstable characteristic of strong arc light, which leads to difficulty in contour extraction. The existing image segmentation algorithms cannot satisfy the requirements for accuracy, timing, and robustness. Aiming at these problems, a real-time recognition method, based on improved DeepLabV3+, for identifying the molten pool more accurately and effectively was proposed in this paper. First, MobileNetV2 was selected as the feature extraction network with which to improve detection efficiency. Then, the atrous rates of atrous convolution layers were optimized to reduce the receptive field and balance the sensitivity of the model to molten pools of different scales. Finally, the convolutional block attention module (CBAM) was introduced to improve the segmentation accuracy of the model. The experimental results verified that the proposed model had a fast segmentation speed and higher segmentation accuracy, with an average intersection ratio of 89.89% and an inference speed of 103 frames per second. Furthermore, the trained model was deployed in a real-time system and achieved a real-time performance of up to 28 frames per second, thus meeting the real-time and accuracy requirements of the K-TIG molten pool monitoring system.