Abstract
This paper presents a dynamic characteristic-based fuzzy adaptive control algorithm (DCbFACA) to avoid the influence of cutting force changing rapidly on the machining stability and precision. The cutting force is indirectly obtained in real time by monitoring and extraction of the motorized spindle current, the feed speed is fuzzy adjusted online, and the current was used as a feedback to control cutting force and maintain the machining process stable. Different from the traditional fuzzy control methods using the experience-based control rules, and according to the complex nonlinear characteristics of CNC machining, the power bond graph method is implemented to describe the dynamic characteristics of process, and then the appropriate variation relations are achieved between current and feed speed, and the control rules are optimized and established based on it. The numerical results indicated that DCbFACA can make the CNC machining process more stable and improve the machining precision.
Highlights
The cutting force is considered as the main influence factors in the machining process due to its direct influence on the machining state and the role it plays in the machining accuracy as well as the lifespan of the cutting tools
This paper presents a dynamic characteristic-based fuzzy adaptive control algorithm for overcoming the defect of the experience-based control rule and stabling the machining process
When the current is mutating, it can be adjusted to the reference value at 30 s early based on the optimized control rules and small fluctuation
Summary
The cutting force is considered as the main influence factors in the machining process due to its direct influence on the machining state and the role it plays in the machining accuracy as well as the lifespan of the cutting tools. It can be concluded that the adaptive cutting force control based on the fuzzy logic algorithm is the direction of research and development; the application of fuzzy logic control still needs considerable effort to identify the appropriate membership functions and fuzzy rules, when the system is complicated or rapidly changing. 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]. The design of the fuzzy controller presents difficulties in finding control rules and selecting an appropriate membership function To solve this problem, a grey-theory algorithm was introduced into the turning fuzzy control to predict the output error of the system and the error change for eliminating these difficulties in [13]. The feed speed changes as the system adjustment parameters, the current as decision parameters to compose the feedback control loop of the feed-drive system, which to realize the cutting force adaptive control
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