Research On The Milling Force And Tool Wear In High-speed Milling Ultra-high-strength Steel With Cermet Cutter

The ultra high stre ngth steel is a difficult to cut mat erial. It has bee nwidely used in aviation, military, automotive and ma nufa cturing areasbecause of it s excellent mechanical pr oper ti es. Curre ntly, high-speedmilling of the di fficult to machine materials is interested by manyresearchers for the study can i mprove the cutting efficiency and the qualityof t he machined surface. We used t he cer met cutter for ul tra-high-strengt hsteel32Cr3NiMoVA of t he high speed milling test. Di fferent cuttingpara met er s on t ool wea r and cutting force were i nve stigat e d in this article.The cer met cutte r wea r mor phol ogy a nd wea r me cha nisms were reveal ed.And a for ce predict ion model and tool wear state monit oring model weredeveloped. It may lay a theoretical instruction for high speed millingproc ess of the ult ra-high-st rengt h st eel. The di ssert at ion includes thefollowi ng aspects:(1) The variation of the milling force with the cutting parameters andtool wears in the high-speed milling process was investigated. And weestablished a prediction mo del of cutting force by regression analysismethod. The results show that the milling force downward trend with thecutting speed increasing, but it increases with the feed, depth of cut andtool wear increasing.(2) Tool wear characteristics and tool wear mec hani sm were disc ussedunder t he experi mental study of high-speed milling of ult ra-high-st rengt hsteel. By cutting a workpiece length as standard, the results show that withthe increasing of cutting speed, depth of cut and tool wear, the tool wearshowed a trend of i ncrease, but it decrease with the increase of feed. Andthe mai n morphol ogies of t ool wear are crater wear of rake, strip we ar offlank and mi cro-cracks. The tool wear mechani sm of r ake and fla nk haveabrasi ve wear， adhesi on we ar, oxi dat ive wear and di ffusion wear.(3) Ac oustic e mi ssi on signal s wear col l ected by t he acousti c emi ssionsensor technology. Wavelet pac ket a nalysis t e chnology was e mpl oye d t odeco mpose the acoustic emi ssion si gnals. And then the energy of eachfrequency band was extracted to calculate the percenta ge of the fr e que ncy band e ner gy i n the total e ner gy. Fi nall y, t he e ne rgy of fre que ncy ba nd t ha tare sensiti ve t o t ool wea r wa s put int o t he neural net wor k as a feat ur e vectorto recogni ze the t ool wear state. The results show that the acoustic emissionsensor te chnology combi ne d wa velet packet a nalysis and ne ural net wor ktool we ar recogniti on tec hnol ogy ca n be a good wa y to mo nitori ng t ool we arstate.