Direct servo control of positional drivatives for 5-axis CNC machine tools using densely-sampled toolpaths

Machining;CNC;Motion control;Voxel;Computer-aided manufacturing;GPU

Lynn, David Roby ; Kurfess, Thomas Mechanical Engineering Saldana, Christopher Tucker, Tommy Rossignac, Jarek Telenko, Cassandra Helu, Moneer ; Kurfess, Thomas

University:Georgia Institute of Technology

Department:Mechanical Engineering

关键词: Machining;    CNC;    Motion control;    Voxel;    Computer-aided manufacturing;    GPU;   

Others  :  https://smartech.gatech.edu/bitstream/1853/61223/1/LYNN-DISSERTATION-2019.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Ever-increasing quality and complexity requirements for machined parts have led to the development of computer-numerical control (CNC) machine tools with high numbers of servo axes capable of tightly coordinated motion. These machine tools are usually programmed using computer-aided manufacturing software that creates toolpaths for machining surfaces and features selected by a user. Voxel-based computer-aided manufacturing (CAM) software has shown great potential in both creating machining plans for highly complex parts and performing realistic simulations of material removal that would be impractical with current industrial CAM systems. Voxel models allow for the creation of toolpaths that follow the exact surface of a given part on a voxel-by-voxel basis, which enables the recreation of very fine surface details on a machined part. The created toolpaths are translated by the CAM system into a format readable by the machine, known as G-Code, which consists of points and maximum velocities that the machine should follow in order to trace out the desired path. For toolpaths created from a voxel model, this G-Code program consists of many small linear movements for each axis of the machine tool. Specifying toolpaths to the machine in G-Code has a number of limitations: first, many commands are machine specific, which causes compatibility issues between the CAM system and the CNC; second, translating a toolpath into G-Code causes a loss of valuable process control data between the CAM system and the CNC; and third, the use of G-Code forces the CNC to spend valuable compute cycles performing online trajectory planning using a worst-case approach that can prevent the cutting tool from reaching its programmed maximum velocity. Even the most sophisticated CNC machine tool control systems are unable to maintain the programmed tool velocity while machining a toolpath created from a complex voxel model. This causes the machine to not execute the exact toolpath provided by the CAM system, which renders offline simulations of machining and material removal less effective. Much research has focused on finding optimal tool velocities to traverse a path more quickly in order to reduce machining time, but all of these works still rely on G-Code. To overcome the limitations present in G-Code programming, this research develops and evaluates a new solution to offline trajectory planning and control that is enables a CNC machine tool to follow a densely-sampled toolpath (such as one created from a voxel model) at the kinematic limits of each axis. Additionally, the proposed approach will allow for the communication of densely-sampled motion trajectories that would be impossible with standard G-Code. The contributions of this work are as follows: first, a generalized framework and accompanying control system for direct transmission of dense data to and from the machine tool’s servo controllers directly from a voxel-based CAM system is developed; second, a reference implementation of this approach is performed on an open-source CNC platform known as Machinekit; third, near-realtime simulation and analysis capabilities from within the CAM system are developed and discussed; and fourth, the accuracy of motion realizable by the new control system is validated using complex toolpaths created from the CAM system and performance is compared to the standard G-Code programming method.