# Region-Based Approach for Machining Time Improvement in Robot Surface Finishing
###### Abstract
The authors in [20] used two multi-objective particle swarm optimization loops, focusing on finding the proper robot base placement and configuration. The first loop minimizes all the robot's constraints, such as singularities, joint limitations, and collisions. Redundancy data were then fed into the second loop to find the optimal robot base placement, such that all trajectories can be followed by continuous motion. In [21], two genetic algorithms were employed to improve the robot tool's initial position in the robotic milling. The first algorithm maximized kinematic and dynamic manipulability, and the second minimized total joint torques. Both optimizations were executed along the milling trajectory. A feasible workpiece placement through multiple constraint violation functions, which are applied and minimized incrementally, was researched in [22]. Much research on workpiece positioning was conducted in the milling section, such as in [23], where, based on milling cutting forces and an elastostatic model of the robot, the optimal workpiece position is searched with the help of genetic and interior-point algorithms. Deburring applications are also considered, such as in [24], where the authors used a genetic algorithm to optimize the workpiece's placement based on the robot's stiffness. Due to the geometric complexity of freeform surface machining, tool orientations typically vary dynamically when using a 5-axis machine tool; because of this, clustering is recognized increasingly as a strategic method for optimizing the machining of large workpieces or those with varied surface curvatures. The authors in [25] aimed to improve CNC machining efficiency by optimizing tool orientations and feed directions. The surface was segmented into optimized sub-regions using K-means clustering, ensuring machinability through going and collision-free orientations. Similar work was performed in [26], where the workpiece was partitioned based on convex, concave, and saddle surface curvature regions and processed further by the Fuzzy-means clustering algorithm. The complexity of robot machining increases significantly when dealing with workpieces with intricate geometries. Traditional CAD/CAM programs often fall short in these scenarios due to the robots' physical limitations and the tasks' complexities. Thus, there is a need for novel approaches that can evaluate and optimize the robot's kinematic capabilities accurately, ensuring that the machining tasks are performed efficiently. Because of the relatively low stiffness of industrial robots, numerous research efforts have focused on enhancing robot stiffness through optimizing robot posture [27; 28]. Here, the authors propose a region-based toolpath generation method for the robotic milling of freeform surfaces to optimize robot stiffness. The method divides a surface into sub-regions with similar robot postures to enhance stiffness, and then generates toolpaths tailored to these regions. The article by [29] developed a robotic belt grinding approach that segments workpieces with complex shapes into regions to simplify the generation of gauge-avoidance toolpaths. It introduced algorithms for both wheel face grinding and wheel edge grinding to analyze and avoid potential gauges, enhancing the robotic grinding process's efficiency and accuracy. Region-based path planning is also used in achieving proper welding layers on complex workpieces [30] and in [31], where partitioning with the help of clustering was used to machine giant complex sculptures. None of the existing robotic region-based approaches incorporate robotic kinematic analysis directly with free-form surface geometry in a task-oriented way. A novel approach to robot kinematic analysis incorporating the constraints imposed by the workpiece surface geometry has been developed to address these challenges [32]. The proposed methodology extends the well-known concept of manipulability, which is used traditionally to assess robot motion capabilities.