Study Of Prediction Modeling Method For Flexible Workpiece Path Processing Deformation Compensation

Flexible workpiece is common processing material in textile manufacturing, aerospacemanufacturing and other industries are widely used, but because of its poor rigidity, in theprocess, the workpiece is easy to have a greater tensile or compressive deformation, whichmakes process control becomes more difficult. To improve the flexible workpiece of the pathprocessing accuracy, speed and reliability targets, the study includes: the decision-makingknowledge extraction of workpiece's deformation, nonlinear modeling of distortioncompensation for processing path, feedback control of processing path through onlinemeasurement and embedded core algorithm development theory and methods, which promotethe advanced manufacturing science, intelligent measurement and control the developmentand application of science, with important academic value and practical significance. Thework was supported by combination project of Guangdong province and the Educationministry of China (2011B090400468), Guangzhou municipal science and technology keyproject (2007Z2-D3161) and Guangdong provincial natural science foundation (05001838)funding.Based on the analysis and processing of flexible workpiece path processing controlevaluation, to improve the machining accuracy and system performance of flexible workpiecepath processing, distortion compensation control of processing, feedback control ofprocessing path through online measurement, adaptability and intelligent of process controlmodel are the key technologies for studying. And discussing the domestic and internationalresearch of flexible workpiece path processing distortion compensation control technologythrough the following three aspects, which is MIMO modeling of flexible workpiece pathprocessing, flexible workpiece path processing measurements by machine vision, intelligentcontrol systems hardware and software co-design with hardware acceleration, then to todetermine the study content of the paper, the main thesis includes:⑴Carried out the mechanical analysis of flexible workpiece path processing deformation,pointed out the factor of flexible workpiece path processing deformation are very complicated,deformation decision-making knowledge extraction of flexible workpiece path processing isnecessary. After discussing the flexible workpiece path processing simulation of simplifiedmechanical model and finite element solution,gained relationship of deformation of flexibleworkpiece path processing, and force, role of position, structural parameters of flexible piecesand other factors;Pointed out the change of forces is effected by depth of feed, feed angle,primitive type and angle,processing step,interpolation means, interpolation speed, processing direction angle, clamping means and clamping location of flexible workpiece, etc., If a largenumber of factors that affecting the deformation of flexible workpiece path processing aredesignated as input of the following prediction model, it will form an extremely complexsystem architecture, it need to extract the deformation factors for flexible workpiece pathprocessing.⑵Proposed the method of deformation decision knowledge extraction for flexibleworkpiece path processing based on RS and entropy reduction. Regarded factors of flexibleworkpiece path processing deformation as the condition attributes A, level of processingpath deformation as decision attribute D to build decision table of processing deformationwhich expressed as DDT; Research is based on information entropy of DDT attributesignificance reduction algorithm, Changes in the level of mutual information I (P;D)as acondition attribute importance to the decision attribute evaluation, the greater the mutualinformation I (P;D),the condition attribute for decision attribute is more important,it hasstrong understanding, objectivity, operational. To achieve decisions knowledge extraction forflexible workpiece path processing deformation based on DDT reduction algorithm flowchartof deformation decision table. Application examples show that, the highest impact factors ofdeformation for flexible workpiece path processing which extracted through entropyreduction method are same as the result by Pawlak reduction methods and genetic reductionalgorithm, However, the prediction error decreased32.58%,21.45%. Meanwhile, the entropyreduction method can be flexibly configured thresholdC Ito meet the requirements of thedifferent needs of modeling accuracy, easier access to a high accuracy deformationstreamlined set of factors.⑶One distortion-compensated prediction ATS-FNN modeling method for flexibleworkpiece path processing is proposed, it focuses on advantages of adaptive fuzzyclustering AFCM method and fuzzy neural network modeling TS-FNN, the method iseffective integrated by the fuzzy clustering AFCM, fuzzy neural network TS-FNN modelingmethod,TS-FNN with learning ability, nonlinear function approximation and good mappingability, with ffuzzy clustering method AFCM, to gain input space of TS-FNN antecedentnetwork, the membership function extraction, and calculation of rule fitness. TS-FNNconsequent network adds hidden layer relative to the standard TS model, and further improveuniversal approximation properties of the model. Experimental results show that, buildingtime of ATS-FNN model is smaller than the STS-FNN model reduced52.34%, ATS-FNNmodel prediction error MSE than the STS-FNN decreased36.50%,33.34%; The processing path angle errors, straightness errors which is pre-compensated by ATS-FNN model, thanpre-compensation by the STS-FNN models were, processing with non-compensation toreduce40.44%,52.55%and28.76%,44.45%; Pixel minimum processing time ATS-FNNmodel is better than STS-FNN decreased46.09%, compared with6.65%increase innon-compensation processing.⑷Proposed feedback ATS-FNN model which path error is measured by machine vision.To design flexible workpiece path processing distortion compensation hardware controllerwith dual32-bit MicroBlaze processor core, TS-FNN, wavelet transform IP core for thespecial auxiliary. Processing path geometry is measured by the machine vision, the processingtrack error is adjusted by the PID regulator, and then correct the pre-compensation value ofATS-FNN model, so precision is expected to solve which caused by the thickness of theworkpiece, the feed rate, machining trajectory pattern changes and other issues.Dual-processor of the hardware controller based on mechanism of message mailboxcommunication to work together, so it can speed up image processing tasks. Multi-core datacommunication of dedicated IP core links to MicroBlaze processor by FSL bus can solve theproblem of bus and memory limitations of data transfer delay between main processor and IPcore. Experimental results show that, with the introduction of error feedback, making theprocessing even if processing conditions changes the error produce only small fluctuations;ATS-FNN controller with dual-core processors work together to help accelerate thecalculation speed controller.⑸Proposed the design which can accelerate decomposition/reconstruction of wavelettransform for image processing by FIR filter. The decomposition of Daubechies (4) andreconfigurable computing IP core are designed by using the8-tap transpose FIR filter. Thetotal time consuming of wavelet two-level decomposition in this IP coreTw mrtis onlyincreased5.561%compared with PC computing timeTwmrtpc. In order to accelerate theTS-FNN calculation, multi-stage pipeline design is introduced, it achieved the systempartitioning of combinational delay logic circuit between hardware for the TS-FNNantecedent and consequent network, registers are inserted between the various classificationsto store intermediate data temporarily, as well as timing path was shorter, it achieved parallelcomputing between TS-FNN antecedent and consequent network. IP core performanceindicators have been significantly improved using pipelined design, taking8-bit floating-pointoperations as sample, the IP core operating frequencyFipcoreplwith pipeline design is improved17.85%compared toFipcorenplwithout pipeline design.⑹Combined with the deformation compensation application of flexible pieces, itintroduces ATS-FNN controller with feedback process system in quilting and thedevelopment of computer bending machine processing system which based on open-loopATS-FNN controller. According to processing deformation factors in actual quilting process,it developed a pattern mold making and control software module, which based on hardwarestructure of quilting process system designed on ATS-FNN controller. And the applicationresult shows that, the processing path angle errorf and the straightness errorf lofquilting process system based on ATS-FNN controller respectively reduced32.9%and36.1%compared to those system which based on PC+NC, it solved the error increase problem inquilted path processing meanwhile the thickness of the flexible pieces increased. Thetechnical parameters of bending machine processing system which based on open-loopATS-FNN controller have reached the feeding accuracy±0.015mm, the maximum bendingangle of130°and the maximum bending radius of200mm,which shows that bending machineprocessing system using the basic theory of deformation compensation processing control hasachieved good application results.