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The processing quality of the block hole system affects the working performance of the marine diesel engine block directly. Choosing an appropriate combination of process parameters is a prerequisite to improving the accuracy of the block hole system. Uncertain fluctuations of process parameters during the machining process would affect the process reliability of the block hole system, resulting in an ultra-poor accuracy. For this reason, the RBF method is used to establish the relationship between the verticality of the cylinder hole and process parameters, including cutting speed, depth of cut, and feed rate. The minimum cylinder hole verticality is taken as the goal and the process reliability constraints of the cylinder hole are set based on Monte Carlo, a reliability optimization model of processing parameters for cylinder hole is established in this paper. Meanwhile, an improved particle swarm algorithm was designed to solve the model, and eventually, the global optimal combination of process parameters for the cylinder hole processing of the diesel engine block in the reliability stable region was obtained.

Instantaneous undeformed chip thickness (IUCT) plays a critical role in optimizing and monitoring of five-axis milling processes, as it determines machining precision and tool life by affecting cutting forces. Analytical and numerical methods have been widely used for IUCT calculation. However, these methods have some deficiencies, such as solving the unsolvable transcendental equations, modeling the complex cutting edge trajectory, and poor adaptability for five-axis milling. Therefore, a Boolean method which consists of subtraction and intersection operations is developed. First, the blank for the current cutting edge is obtained by the Boolean subtraction operation between the workpiece and the swept volume that produced by the front cutting edges. Second, the Boolean intersection operation is executed between the blank and the auxiliary edge entity (AEE) that corresponding to the tool edge. Then, the IUCT of each cutting edge or each cutting point can be deduced based on the Boolean result with the known blank geometry, tool geometry, tool path, and machining parameters. The method is verified by milling force experiments. It is an efficient, accurate, and general method because it does not need to establish complex mathematical or numerical models. The calculated IUCT will promote the optimizing and monitoring of five-axis milling processes.

The residual stresses (RS) generated during the thermal processing processes such as casting and heat treatment is one of the fundamental causes of irreversible deformation of diesel engine bodies (DEBs). However, previous studies only focused on the RS generated from casting or the effect of annealing heat treatment on the RS in castings unilaterally, and the analytical results were far from the actual conditions. Therefore, this paper proposes a comprehensive research method to consider the whole dynamic evolution of the casting from the RS generated by casting to the stress relief by heat treatment. Numerical simulation of DEBs casting process is carried out by PROCAST, and the results of casting RS obtained from the simulation are imported into ABAQUS, and then numerical simulation of annealing heat treatment is carried out for the DEB casting model with casting RS, so as to realize the analysis of dynamic evolution of RS from casting to heat treatment.

A high performance composite grinding wheel with brazed diamond sheets was developed for rail grinding. The rotation stress formula of the composite grinding wheel is deduced and analyzed, and the cutting off effect of the brazing sheet on the resin in the composite grinding wheel is analyzed. The finite element simulation analysis of the rotation performance of the composite grinding wheel is carried out when the brazing sheet is in the middle of the grinding wheel or one end of the brazing sheet is flush with the grinding end of the grinding wheel. The corresponding composite grinding wheel is made to carry on the rotation test, which is compared with the simulation analysis results. The results show that for the O260-size rail grinding composite grinding wheel, when using 15 mm wide and 55 mm long brazing sheets, the optimal placement angle is 55°, followed by 60° and 45°. The analysis of the noise detection results of the rotary testing machine shows that the resonance speed of the rail grinding wheel is between 4600–4800 rpm, and the rotary noise of the composite grinding wheel with brazing sheet is lower than that of the resin grinding wheel.

Camshaft is a key part of marine diesel engines. However, the large residual stresses resulting from the induction hardening to which the cams are submitted may lead to cracks on their contacting surfaces. These cracks have great influence in the service life of the shaft. In order to find the relationship between the induction hardening process parameters and the residual stress field, the process was simulated by the finite element method, and the SYSWELD and ANSYS software were used. The temperature distribution during hardening process and stress field of the camshaft after the process were obtained and analyzed. The thickness of the hardening layer was calculated. The simulation results were validated by residual stress measurement experiments and showed a good agreement with the practice. The method reported on the present paper provides an efficient way to analyze the camshaft induction hardening process and avoid the surface failure of the cams.

Applying complex groove with variable helical angle, rake angle, core radius or groove width along tool axis is an essential way to improve the cutting performance of solid end mills. However, the usage of complex groove is limited due to the lack of an effective way to calculate wheel path for its machining, because the previous envelope theory or trial-and-error method that used for simple constant groove can no longer be used for complex groove. In this paper, a general model is established to calculate the wheel path for complex groove machining based on a mathematical optimization model, which have three constraints and one objective. First, the complex groove is defined by four curves, namely the edge curve, rake curve, core curve and flank curve, so that the variable helical angle, rake angle, core radius and groove width can be expressed by mathematical formulas. Second, the grinding wheel path is expressed as a series of its position vectors, including three location parameters and three posture parameters. Then, these vectors are calculated by searching the minimum machining error of groove width with three constraints: contact constraint to meet the rake and core radius requirements, groove width constraint to prevent the overcut with the adjacent edge and non-interfere constraint to prohibit the interfere with the current edge. Finally, the accuracy and efficiency of the model are validated by five experiments with different designed complex grooves.

Groove is a key structure of high-performance integral cutting tools. It has to be manufactured by 5-axis grinding machine due to its complex spatial geometry and hard materials. The crucial manufacturing parameters (CMP) are grinding wheel positions and geometries. However, it is a challenging problem to solve the CMP for the designed groove. The traditional trial-and-error or analytical methods have defects such as time-consuming, limited-applying and low accuracy. In this study, the problem is translated into a multiple output regression model of groove manufacture (MORGM) based on the big data technology and AI algorithms. The input are 34 groove geometry features and the output are 5 CMP. Firstly, two groove machining big data sets with different range are established, each of which is includes 46656 records. They are used as data resource for MORGM. Secondly, 7 AI algorithms, including linear regression, k nearest-neighbor regression, decision trees, random forest regression, support vector regression and ANN algorithms are discussed to build the model. Then, 28 experiments are carried out to test the big data set and algorithms. Finally, the best MORGM is built by ANN algorithm and the big data set with a larger range. The results show that CMP can be calculated accurately and conveniently by the built MORGM.