Abstract
With the rapid development of new-generation information technologies such as big data, cloud computing, Internet of Things, and mobile internet in traditional manufacturing, the development of intelligent manufacturing (IM) is accelerating. Digital twin is an important method to achieve the goal of IM, and provides an effective means for the integrated development of design and manufacturing (R & M). In view of the problems of long installation and debugging cycles, and process parameters requiring multiple trial and error in the research and development (R & D) process of laser melting deposition (LMD) equipment, this paper focuses on building an LMD equipment model based on digital twin technology. It involves performing virtual assembly, motion setting, collision inspection, and PLC debugging, thereby providing an innovative method and insights for improving the R & D efficiency of the IM of LMD equipment.
Highlights
In recent years, with the rapid development of new-generation information technologies such as big data, cloud computing, Internet of Things, and mobile internet in traditional manufacturing industries, the personalized needs of customers have become more and more urgent, and the development of intelligent manufacturing (IM) production models has been accelerating [1,2]
Based on the digital twin technology, this paper focuses on the construction of a laser melting deposition (LMD) equipment model, and performs virtual assembly, motion setting, collision inspection, and PLC debugging to form a combination of virtual and real, thereby providing a new method idea for improving the R & D efficiency of LMD equipment
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Summary
With the rapid development of new-generation information technologies such as big data, cloud computing, Internet of Things, and mobile internet in traditional manufacturing industries, the personalized needs of customers have become more and more urgent, and the development of IM production models has been accelerating [1,2]. Stavropoulos et al [10] simulated molecular dynamics by digital twin technology, which was accurate to the molecular level, and combined with machine learning technology, physics, and decision-making algorithms It reduced the probability of model adaptation and quality evaluation errors. Based on the digital twin technology, this paper focuses on the construction of a LMD equipment model, and performs virtual assembly, motion setting, collision inspection, and PLC debugging to form a combination of virtual and real, thereby providing a new method idea for improving the R & D efficiency of LMD equipment. FFiigguurree11..DDiiggiittaall ttwwiinn ssyysstteemm ffrraammeewwoorrkk ooff LLMMDD eeqquuiippmmeenntt. TThhee mmaaiinn ssttrruuccttuurree ooff LLMMDD eeqquuiippmmeenntt iiss iinntteeggrraatteeddbbyyffiivveemmaajojorrppaartrsts, ,nnaammeleylyththee eeqquuiippmmeenntt ppllaattffoorrmm ssttrruuccttuurree,, pprriinntt hheeaaddssttrruuccttuurree,,ppoowwddeerrfefeeeddininggstsrtuructcuturerelalsaesrerstsrturcu-cttuurree,,aannddccoooolliinngg ssttrruuccttuurree,, aass sshhoowwnn iinn FFiigguurree 22..TThheessttrruuccttuurreeooffththeeeeqquuipipmmeennt tpplaltaftoformrm iissmmaaiinnllyyffoorrmmeeddbbyyththeeXX, ,YY, Z, Zaxaixsislelaedadscsrcerwews asnadndthtehme motootro(rX(,XY,,YZ, gZugidueidreairlasitlrsotkroeskeasre 7a0r0e m70m0,m20m0,m20m0,manmd, 2a0n0dm20m0, mremsp,ercetsivpeelcyti)v, tehlye)w, tohrekwinogrpkilnatgfoprlmatf(o1r1m00(m11m00×m1m20×01m20m0), amndmt)w, aondadtdwiotioandadlitriootnaatilnrgotsahtianfgts.shItaifstsfi.
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