Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retriev...Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retrieved from a common database.Especially,the effective and automatic method to reconstruct the above information for a CAD model is still rare.To address this issue,this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics.First,as the geometry of an assembly interface is formed by one or more adjacent faces on each model,a face-attributed adjacency graph integrated with face structure fingerprint is proposed.This can describe each CAD model as well as its assembly interfaces uniformly.After that,aided by the above descriptor,an improved graph attention network is developed based on a new dual-level anti-interference filtering mechanism,which makes it have the great potential to identify all representative kinds of assembly interface faces with high accuracy that have various geometric shapes but consistent kinematic semantics.Moreover,based on the abovementioned graph and face-adjacent relationships,each assembly interface on a model can be identified.Finally,experiments on representative CAD models are implemented to verify the effectiveness and characteristics of the proposed approach.The results show that the average assembly-interface-face-identification accuracy of the proposed approach can reach 91.75%,which is about 2%–5%higher than those of the recent-representative graph neural networks.Besides,compared with the state-of-the-art methods,our approach is more suitable to identify the assembly interfaces(with various shapes)for each individual CAD model that has typical kinematic pairs.展开更多
Assembly interfaces,the joint surfaces between the vertical tail and rear fuselage of a large aircraft,are thin-wall components.Their machining quality are seriously restricted by the machining vibration.To address th...Assembly interfaces,the joint surfaces between the vertical tail and rear fuselage of a large aircraft,are thin-wall components.Their machining quality are seriously restricted by the machining vibration.To address this problem,an in-process adaptive milling method is proposed for the large-scale assembly interface driven by real-time machining vibration data.Within this context,the milling operation is first divided into several process steps,and the machining vibration data in each process step is separated into some data segments.Second,based on the real-time machining vibration data in each data segment,a finite-element-unit-force approach and an optimized space–time domain method are adopted to estimate the time-varying in-operation frequency response functions of the assembly interface.These FRFs are in turn employed to calculate stability lobe diagrams.Thus,the three-dimensional stability lobe diagram considering material removal is acquired via interpolation of all stability lobe diagrams.Third,to restrain milling chatter and resonance,the cutting parameters for next process step,e.g.,spindle speed and axial cutting depth,are optimized by genetic algorithm.Finally,the proposed method is validated by a milling test of the assembly interface on a vertical tail,and the experimental results demonstrate that the proposed method can improve the machining quality and efficiency of the assembly interface,i.e.,the surface roughness reduced from 3.2μm to 1.6μm and the machining efficiency improved by 33%.展开更多
The real contact area(RCA)of randomly rough contacts has received a great deal of attention because it correlates strongly with friction,lubrication,sealing,and conductivity.Simulations have revealed that the RCA asso...The real contact area(RCA)of randomly rough contacts has received a great deal of attention because it correlates strongly with friction,lubrication,sealing,and conductivity.Simulations have revealed that the RCA associated with deterministic normal squeezing loads increases when tangential loads are also applied,in a phenomenon called junction growth.However,experimental investigations of the junction growth of randomly rough contacts are rare.Here,we used X-ray computed tomography(CT)to measure junction growth when two aluminum alloy surfaces were in contact.A high-resolution experimental setup was used to apply loads and observe contact behaviors at a resolution of 4μm.The RCA and average contact gaps were computed using a three-dimensional(3D)geometric model constructed from gray CT images using the Otsu thresholding method.The results showed that the RCA increased as the normal load increased.The RCA increased by 22.67%after a tangential load was applied(junction growth),and the average gap decreased by 14.01%after a tangential load was applied.Thus,X-ray CT accurately measured the junction growth as a novel quantitative method.展开更多
基金supported by the National Natural Science Foundation of China[61702147]the Zhejiang Provincial Science and Technology Program in China[2021C03137].
文摘Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retrieved from a common database.Especially,the effective and automatic method to reconstruct the above information for a CAD model is still rare.To address this issue,this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics.First,as the geometry of an assembly interface is formed by one or more adjacent faces on each model,a face-attributed adjacency graph integrated with face structure fingerprint is proposed.This can describe each CAD model as well as its assembly interfaces uniformly.After that,aided by the above descriptor,an improved graph attention network is developed based on a new dual-level anti-interference filtering mechanism,which makes it have the great potential to identify all representative kinds of assembly interface faces with high accuracy that have various geometric shapes but consistent kinematic semantics.Moreover,based on the abovementioned graph and face-adjacent relationships,each assembly interface on a model can be identified.Finally,experiments on representative CAD models are implemented to verify the effectiveness and characteristics of the proposed approach.The results show that the average assembly-interface-face-identification accuracy of the proposed approach can reach 91.75%,which is about 2%–5%higher than those of the recent-representative graph neural networks.Besides,compared with the state-of-the-art methods,our approach is more suitable to identify the assembly interfaces(with various shapes)for each individual CAD model that has typical kinematic pairs.
基金supported by the National Natural Science Foundation of China(No.51775024)the MIIT(Ministry of Industry and Information Technology)Key Laboratory of Smart Manufacturing for High-end Aerospace Products Program of China。
文摘Assembly interfaces,the joint surfaces between the vertical tail and rear fuselage of a large aircraft,are thin-wall components.Their machining quality are seriously restricted by the machining vibration.To address this problem,an in-process adaptive milling method is proposed for the large-scale assembly interface driven by real-time machining vibration data.Within this context,the milling operation is first divided into several process steps,and the machining vibration data in each process step is separated into some data segments.Second,based on the real-time machining vibration data in each data segment,a finite-element-unit-force approach and an optimized space–time domain method are adopted to estimate the time-varying in-operation frequency response functions of the assembly interface.These FRFs are in turn employed to calculate stability lobe diagrams.Thus,the three-dimensional stability lobe diagram considering material removal is acquired via interpolation of all stability lobe diagrams.Third,to restrain milling chatter and resonance,the cutting parameters for next process step,e.g.,spindle speed and axial cutting depth,are optimized by genetic algorithm.Finally,the proposed method is validated by a milling test of the assembly interface on a vertical tail,and the experimental results demonstrate that the proposed method can improve the machining quality and efficiency of the assembly interface,i.e.,the surface roughness reduced from 3.2μm to 1.6μm and the machining efficiency improved by 33%.
基金supported by the National Natural Science Foundation of China(Nos.U2141217 and 51935003)。
文摘The real contact area(RCA)of randomly rough contacts has received a great deal of attention because it correlates strongly with friction,lubrication,sealing,and conductivity.Simulations have revealed that the RCA associated with deterministic normal squeezing loads increases when tangential loads are also applied,in a phenomenon called junction growth.However,experimental investigations of the junction growth of randomly rough contacts are rare.Here,we used X-ray computed tomography(CT)to measure junction growth when two aluminum alloy surfaces were in contact.A high-resolution experimental setup was used to apply loads and observe contact behaviors at a resolution of 4μm.The RCA and average contact gaps were computed using a three-dimensional(3D)geometric model constructed from gray CT images using the Otsu thresholding method.The results showed that the RCA increased as the normal load increased.The RCA increased by 22.67%after a tangential load was applied(junction growth),and the average gap decreased by 14.01%after a tangential load was applied.Thus,X-ray CT accurately measured the junction growth as a novel quantitative method.
