Research On Characteristics And Acquisition Methods For Running-status Of Job-shop Manufacturing Equipments

Modern job-shop is an advanced manufacturing system with many features, suchas various kinds of production, a high degree of personalization, rapid change, smallvolume and so on. For the optimized planning of the job-shop, a series of informationstatus must be ready to grasp, such as the dynamic situation of online manufacturingworkpiece, the condition and utilization of equipments, etc. With the comprehensiveneeds of "Fast, high quality, high efficiency, low cost, low power consumption" as thegoal, and in combination with the National Natural Science Fund Project, Research onSystem Characteristics and New Collecting Methods of the Operating StatusInformation of Modern Job Shop Manufacturing System（Project Number：50775228）,extraction methods and application models of running-status information of job-shopmanufacturing equipments are studied in the paper from the view of optimal operationof the modern job-shop manufacturing system. Main research contents are listedbellow：①According to the shotage of research in running-status information model ofjob-shop manufacturing equipmentsin at present, A multi-dimensional informationmodel of job-shop manufacturing equipmentsin is put forward. The model contains sixinformation dimensions, including basic information dimension, logistics informationdimension, power flow information dimension, environmental information dimension,quality information dimension, and each dimension is detailed decomposition anddescription. Based on the multi-dimensional information model, the Static and dynamiccharacteristics and associated characteristics of job-shop manufacturing equipmentsrunning-status information is analysised, then Several typical information associatedcharacteristics of job-shop manufacturing equipments is approved. Then a modelincluding target layer, information supporting layer, information sources and acquisitionlayer is established.②In order to support the data acquisition of jobshop manufacturing equipmentsrunning-status information, several acquisition methods based on power information areproposed. Include the following:1）According to analysis of the properties of spindle-power of CNC machine tool, akind of automatic identification and machining progress monitoring method formachined workpiece, based on dynamic time warping technology and spindle-power information, is proposed. Its purpose is to solve some problems in job shop, such asmachining progress information mainly relied on manual statistics incurring frequenterror in case of mixed type machining, some workpieces are scrapped because ofunauthorized change of machining parameter, and so on.2）In order to solve the problem that the workpiece machining progress informationmainly relied on manual statistics incurring frequent error in case of mixed categorymachining, a kind of workpiece online identification method based on Support VectorMachine(SVM) algorithm and power information is proposed. According to the testresult that the identification method is good at robustness and generalization.③In order to reliably monitor unexpected tool failure and to prevent workpiece ormachine tool from possible damages in batch machining, a tool breakage on-linemonitoring method based on power information and cross-correlation algorithm isproposed. In this method, wavelet coefficients of spindle-power signal are used as thecharacteristic vector of machining information, and then the vector sequence extractedfrom a normal machining process via Mallat wavelet is defined as the referencetemplate for monitoring cutting tool condition. In batch machining, real-timecharacteristic vector of the workpiece in machining process is extracted via an improvedreal-time wavelet algorithm. The correlation of two vector sub-sequences within asampling time window, which is described by generalized cross-correlation coefficient,decreases apparently when the tool is broken. The generalized cross-correlationcoefficient is defined as tool condition index (TCI), and tool breakage can be detectedby monitoring the TCI with a threshold value.In the end, the application of the methods is shown.