3D printing for ultra-precision machining:current status,opportunities,and future perspectives

Additive manufacturing,particularly 3D printing,has revolutionized the manufacturing industry by allowing the production of complex and intricate parts at a lower cost and with greater efficiency.However,3D-printed parts frequently require post-processing or integration with other machining technologies to achieve the desired surface finish,accuracy,and mechanical properties.Ultra-precision machining(UPM)is a potential machining technology that addresses these challenges by enabling high surface quality,accuracy,and repeatability in 3D-printed components.This study provides an overview of the current state of UPM for 3D printing,including the current UPM and 3D printing stages,and the application of UPM to 3D printing.Following the presentation of current stage perspectives,this study presents a detailed discussion of the benefits of combining UPM with 3D printing and the opportunities for leveraging UPM on 3D printing or supporting each other.In particular,future opportunities focus on cutting tools manufactured via 3D printing for UPM,UPM of 3D-printed components for real-world applications,and post-machining of 3D-printed components.Finally,future prospects for integrating the two advanced manufacturing technologies into potential industries are discussed.This study concludes that UPM is a promising technology for 3D-printed components,exhibiting the potential to improve the functionality and performance of 3D-printed products in various applications.It also discusses how UPM and 3D printing can complement each other.

Microwave heating as a universal method to transform confined molecules into armchair graphene nanoribbons

Armchair graphene nanoribbons(AGNRs)with sub-nanometer width are potential materials for the fabrication of novel nanodevices thanks to their moderate direct band gaps.AGNRs are usually synthesized by polymerizing precursor molecules on substrate surface.However,it is time-consuming and not suitable for large-scale production.AGNRs can also be grown by transforming precursor molecules inside single-walled carbon nanotubes(SWCNTs)via furnace annealing,but the obtained AGNRs are normally twisted.In this work,microwave heating is applied for transforming precursor molecules into AGNRs.The fast heating process allows synthesizing the AGNRs in seconds.Several different molecules were successfully transformed into AGNRs,suggesting that it is a universal method.More importantly,as demonstrated by Raman spectroscopy,aberrationcorrected high-resolution transmission electron microscopy and theoretical calculations,less twisted AGNRs are synthesized by the microwave heating than the furnace annealing.Our results reveal a route for rapid production of AGNRs in large scale,which would benefit future applications in novel AGNRs-based semiconductor devices.

Source-Aware Embedding Training on Heterogeneous Information Networks

Heterogeneous information networks(HINs)have been extensively applied to real-world tasks,such as recommendation systems,social networks,and citation networks.While existing HIN representation learning methods can effectively learn the semantic and structural features in the network,little awareness was given to the distribution discrepancy of subgraphs within a single HIN.However,we find that ignoring such distribution discrepancy among subgraphs from multiple sources would hinder the effectiveness of graph embedding learning algorithms.This motivates us to propose SUMSHINE(Scalable Unsupervised Multi-Source Heterogeneous Information Network Embedding)-a scalable unsupervised framework to align the embedding distributions among multiple sources of an HiN.Experimental results on real-world datasets in a variety of downstream tasks validate the performance of our method over the state-of-the-art heterogeneous information network embedding algorithms.