In this article, we present a new path planning algorithm based on the rapidly exploring random tree-fixed node (RRT*FN) algorithm for manipulators. It addresses the problem that RRT*FN complex environment processing is not fast enough to meet real-time requirements, and RRT*FN is basically impossible to search for an effective manipulator path in a narrow-channel environment. In the new path planning algorithm, a heuristic sampling method is adopted and the leaf nodes outside the ellipsoids are preferentially deleted to address the node removal problem, resulting in better search paths and faster search speeds. When the nodes are expanded, new nodes are generated due to goal gravity and random point gravity, with the weight values dynamically adjusted via dichotomy, and the new nodes are expanded to the goal point twice to more rapidly obtain a tree extension direction that is closer to the goal point. For boundary points, the issues of narrow channels and stepped obstacles can be effectively solved by extending the local environment sampling boundaries. To optimize the paths, the redundant intermediate nodes are simplified based on the triangle inequality. Simulation analyses show that the proposed planner can adapt to a variety of scenarios in real time while satisfying optimal path conditions.

Establishing and controlling the prediction model of a machined surface quality is known as the basis for sustainable manufacturing. An ensemble learning algorithm—the gradient boosting regression tree—is incorporated into the surface roughness modeling. In order to address the problem of a high time cost and tendency to fall into a local optimum solution when the grid search and conjugate gradient method is adopted to obtain the super-parameters of the ensemble learning algorithm, a genetic algorithm is employed to search for the optimal super-parameters in the training process, and a genetic-gradient boosting regression tree (GA-GBRT) algorithm is developed. A fitting goodness of fit is taken as the fitness function value of the genetic algorithm and combined with k-fold cross-validation, as such, the initial model parameters of the gradient boosting regression tree are optimized. Compared to the optimized artificial neural network (ANN) and support vector regression (SVR) and combined with the cutting experiment of 304 stainless steel with a micro-groove tool, a genetic algorithm multi-objective optimization model with the highest cutting efficiency and a supreme surface quality was constructed by applying the GA-GBRT model. The response relationship reveals the non-linear interaction that occurs between the cutting parameters and the surface roughness of 304 stainless steel that is machined by the micro-groove tool. As indicated by the results obtained from the multi-objective optimization, the cutting efficiency can be enhanced by increasing the cutting speed and depth within a small range of surface quality variations. The GA-GBRT model is validated to be reliable in making a prediction of the surface roughness and optimizing the cutting parameters with turning and milling data.

Tool-chip friction increases cutting temperature, aggravates tool wear, and shortens the service life of cutting tools. A micro-groove design of the rake face can improve the wear performance of the tool. In this study, we used the finite element simulation “Deform” to obtain the temperature field distribution of the tool rake face. The size of the micro-groove was determined by selecting a suitable temperature field combined with the characteristics of tool–chip flow in the cutting process, and the tool was prepared using powder metallurgy. The three-direction cutting forces and tool tip temperature were obtained by a cutting test. Compared with the original turning tool, the cutting force and cutting temperature of the micro-groove tool were reduced by more than 20%, the friction coefficient was reduced by more than 14%, the sliding energy was reduced and the shear energy was greatly decreased. According to the analysis of tool wear by SEM (scanning electron microscope) and EDS (energy dispersive X-ray spectroscopy), the crater wear, adhesive wear and oxidation wear of the micro-groove tool were lower than those of the original turning tool. In particular, the change in the crater wear area on the rake face of the original tool and the micro-groove tool was consistent with the cutting temperature and the wear width of the flank face. On the whole, the crater wear area and the change rate of the crater wear area of the micro-groove tool were smaller. Due to the proper microgroove structure of the rake face, the tool-chip contact area decreased, and the second rake angle of the tool became larger. Hence, the tool-chip friction, cutting forces, cutting energy consumption were reduced, tool wear was improved, and the service life of the micro-groove tool was five times longer than that of the original tool.