Model-based adaptive process control for surface finish improvement in traverse grinding

• A process controller aimed at grinding chatter and waviness marks is developed.
• Sensor-fusion algorithms identify and classify machine and process vibration.
• Grinding wheel speed is automatically adapted basing on the cutting stability map.
• The controller assures residual waviness amplitudes within the requirement of 1 µm.
• Essential self-learning capacities assure the industrial applicability of the solution.

The paper presents a process controller aimed at improving the surface quality generated by traverse grinding, avoiding the surface defects caused by vibrations onset. The innovation provided by the proposed controller consists in suppressing vibration occurrence by means of a model-based and self-learning approach: a monitoring layer classifies occurring problems and a control logic exploits these indications to select the proper mitigation actions. Since wheel-regenerative chatter represents one of the most important problems during traverse grinding in terms of achievable productivity and finishing quality, the main control variable is the wheel velocity. This variable is tuned exploiting an adaptive Speed tuning Map computed by the controller using a heuristic approach and learning methodology. The control can manage also the other sources of vibration by means of proper identification and mitigation strategies. Experimental tests are carried out on a roll grinder to validate the control system. Good performances are achieved after some training tests to allow controller learning.