A dynamic threshold-based fuzzy adaptive control algorithm for hard sphere grinding

Abstract

Grinding wheel wearing fast and metal adhering were severe in hard sphere grinding, which led to wheel overload and clogging. If a fixed-feed grinding was used, the normal pressure between the workpiece and the grinding wheel increased rapidly. Once the grinding load on the grinding wheel was greater than the strength of the retaining bond bridges, a large amount of grains dropped out, which can even damage the wheel. This led to the sphere surface to be scratched. In this study, a dynamic threshold-based fuzzy adaptive control algorithm (DTbFACA) is proposed for hard sphere grinding to avoid scratches on the workpiece. The grinding force was indirectly obtained by measuring the motorized spindle current which was used as a feedback to control hard sphere grinding process. The current threshold in DTbFACA was obtained and online-rectified automatically. The depth of cut and the cup wheel swing speed that affect the motorized spindle current was online-adjusted by fuzzy algorithm. The experimental results indicated that DTbFACA can avoid scratches on the workpiece without sacrificing the sphere form error and grinding efficiency. DTbFACA has been implemented on MD6050 sphere grinding machine tool in production.

Simulative investigation of hybrid force and position control for electromechanical feed axes in production machines

“…Tsai et al 8 proposed a fuzzy adaptive control strategy to the problem of cutting force control in high‐speed milling operations. A dynamic threshold‐based fuzzy adaptive control algorithm for hard‐sphere grinding processes was introduced by Li et al 9 Hu et al 10 presented an adaptive control algorithm based on fuzzy logic and embedded the controller in the software computerized numerical control (CNC) kernel to improve the real‐time performance of machining process control. The combination of artificial techniques with adaptive force control in milling operations was discussed by Zuperl et al 11 The feed rate is adjusted on‐line with a feed‐forward neural control scheme to maintain a constant cutting force despite the variations in cutting conditions.…”

Constant Cutting Force Control for CNC Machining Using Dynamic Characteristic-Based Fuzzy Controller

“…The fuzzy rules for the control of the wire electrical discharge machining process were formulated and incorporated with pulse trains analysis and experience to control cutting force and power consumption in [15], and conventional control method was presented to adjust the feed rate, control the spindle load, and optimize the machining process online in [16]. A dynamic threshold-based fuzzy adaptive control algorithm was proposed to online-adjust the cut depth and cup wheel swing speed that affect the motorized spindle current for avoiding scratches on the work piece in hard sphere grinding in [17]. However, the creation of the rules base was taken from the expert operator and the experience data cannot reflect the actual machining process.…”

Material removal monitoring in precision cylindrical plunge grinding using acoustic emission signal

“…3 Lee 4 proposed a control-oriented model for the cylindrical grinding process to estimate the workpiece qualities such as surface roughness and actual part size with the measurement of grinding power. To avoid scratches on the workpiece in hard sphere grinding, Li et al 5 proposed a dynamic threshold-based fuzzy adaptive control algorithm by measuring the motorized spindle current and verified that the algorithm can avoid scratches on the workpiece without sacrificing the form error and grinding efficiency. Morgan et al 6 proposed a fully integrated intelligent grinding system for adaptive controlled cycle optimization, thermal damage avoidance, dressing interval optimization and data retention.…”