Process Control System For WEDM Of Variable Workpiece Height

As one of the non-traditional machining approaches, wire electrical dischargemachining(WEDM) plays an important role in the manufacture of molds, shaped elec-trodes, hard-to-cut materials and precise parts with intricate shapes. With the devel-opment of technologies, the requirements for quality and precision of WEDM arebecoming more demanding. When machining workpieces with a variable height, wirebreakage and low machining efficiency are the two major issues. When the workpieceheight along a machining path changes from high to low, the discharge energy willbe concentrated on a short range of the wire. As a result, wire breakage is likely tooccur. On the contrary, when the workpiece height varies from low to high, the densi-ty of discharge along the wire becomes low, thus resulting in a low material removalrate(MRR).In order to avoid wire breakage and improve machining efficiency when machin-ing workpieces with variable heights by WEDM, this dissertation investigates severaltopics which are crucial for variable height machining by WEDM, those include of-fline and online workpiece height estimation, extraction of workpiece heights from3D models, modelling of WEDM processes, as well as model predictive control forWEDM processes.In this dissertation, a CNC (computer numerical control) system for a WEDMwas developed based on Linux operating system. The RTAI kernel is attached to theLinux to meet the real time requirement for CNC systems. The CNC system consists offive modules, which include GUI module, code interpreter module, task managementmodule, motion control module and I/O control module. The control signals generatedby the motion control module drive the machine through a multi-function I/O card.After analyzing the discharge current and voltage wave forms collected during the machining，on the basis of Farad’s induction theory, a discharge frequency monitorsystem was developed to detect the discharge frequency.For variable height WEDM, the workpiece height must be provided to CNC sys-tem, so that appropriate machining parameters can then be determined according toit. In this dissertation, depending on the availability of a3D model, two cases areprocessed separately. If there is no3D model of a workpiece, a black box methodis adopted to build up a workpiece height estimation model. When the3D model isavailable, a white box method is applied to extract the workpiece height informationalone the machining path from the3D model. The workpiece height data is finally fedinto the CNC system.Due to its good performance in modelling nonlinear systems, support vector ma-chine (SVM) is used to build up a workpiece height estimation model with the ma-chining data collected. This is the so-called black box method. The input and outputof the model are chosen from those machining parameters which have no noticeableimpacts on the surface quality. The output variable is the workpiece height that is tobe estimated, and the input variables are a set of machining parameters, discharge fre-quency and feed rate. The estimated workpiece height will be then used as a referencefor appropriately determining a set of machining parameters.When the3D model of a workpiece is available, the workpiece height extractedfrom the3D model can be directly applied to the CNC system. This is the so-calledwhite box method. As the range of workpiece heights becomes larger, the machiningdata collected is increasing. Not only can this data be used to control the machiningprocess in progress, but also can be used to update the workpiece height estimationmodel online. By learning the new dynamics which have not been excited before, theheight estimation model can be refined, thus improving its prediction accuracy. Theupdate of the workpiece height estimation model was achieved by using the on lineleast square support vector machine (LS-SVM). When a new data pair is available, ajudgment must be made whether the new data pair should be selected as a new basicvector (BV) based on the projecting method. If the new data pair is selected as a newBV, the old BV which has the least significant impact on the model should be removedfrom the BV set. By keeping the number of BVs within a predefined value, this online algorithm can not only reduce the computational load and the requirement for memoryspace, but also overcome the non-sparsity of LS-SVM.No matter how the workpiece height is obtained either by the black box methodor by the white box method, the workpiece height will be used to determine an appro-priate set of machining parameters so as to avoid wire breakage and improve MRR.A model predictive controller is designed in such a way that the reference dischargefrequency will be followed to maintain a stable machining. The optimal control inputsare then generated by solving an online optimization problem.