Jikai Liu

dblp:166/5587 · DBLP profile ↗
← Back
11ranked-venue papers
5as first author
6since 2021 · last 2026
0000-0002-9732-3791ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 8 · 4 first-author · 5 since 2021Databases, data management, data science and information retrieval · 2 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 since 2021
YearPublicationVenuePosition
2026 DME -Deeplabv3 + : A perception-driven semantic segmentation approach for intelligent pavement crack maintenance
Yushi Fan, Xiuze Fan, Jizhe Zhang, Jikai Liu, Heba M. Lakany, Yongsheng Ma, Wanqi Ma, Yufan Zheng
Expert Syst. Appl.4
2025 Waste-to-Value: Reutilized Material Maximization for Additive and Subtractive Hybrid Remanufacturing
abstract
Remanufacturing effectively extends component lifespans by restoring used or end-of-life parts to like-new or even superior conditions, with an emphasis on maximizing reutilized material, especially for high-cost materials. Hybrid manufacturing technology combines the capabilities of additive and subtractive manufacturing, with the ability to add and remove material, enabling it to remanufacture complex shapes and is increasingly being applied in remanufacturing. How to effectively plan the process of additive and subtractive hybrid remanufacturing (ASHRM) to maximize material reutilization has become a key focus of attention. However, current ASHRM process planning methods lack strict consideration of collision-free constraints, hindering practical application. This paper introduces a computational framework to tackle ASHRM process planning for general shapes with strictly considering these constraints. We separate global and local collision-free constraints, employing clipping planes and graph to tackle them respectively, ultimately maximizing the reutilized volume while ensuring these constraints are satisfied. Additionally, we also optimize the setup of the target model that is conducive to maximizing the reutilized volume. Extensive experiments and physical validations on a 5-axis hybrid manufacturing platform demonstrate the effectiveness of our method across various 3D shapes, achieving an average material reutilization of 69% across 12 cases. Code is publicly available at https://github.com/fanchao98/Waste-to-Value.
Fanchao Zhong, Zhenmin Zhang, Jikai Liu, Lin Lu 0001, Haisen Zhao
ACM Trans. Graph.5
2023 Challenges in topology optimization for hybrid additive-subtractive manufacturing: A review
Jikai Liu, Yufan Zheng, Shuzhi Xu, Yongsheng Ma, Chuanzhen Huang, Lei Li 0026
Comput. Aided Des.1
2023 Multiscale topology optimization for solid-lattice-void hybrid structures through an ordered multi-phase interpolation
Chenghu Zhang, Shuzhi Xu, Jikai Liu
Comput. Aided Des.4
2023 VASCO: Volume and Surface Co-Decomposition for Hybrid Manufacturing
abstract
Additive and subtractive hybrid manufacturing (ASHM) involves the alternating use of additive and subtractive manufacturing techniques, which provides unique advantages for fabricating complex geometries with otherwise inaccessible surfaces. However, a significant challenge lies in ensuring tool accessibility during both fabrication procedures, as the object shape may change dramatically, and different parts of the shape are interdependent. In this study, we propose a computational framework to optimize the planning of additive and subtractive sequences while ensuring tool accessibility. Our goal is to minimize the switching between additive and subtractive processes to achieve efficient fabrication while maintaining product quality. We approach the problem by formulating it as a Volume-And-Surface-CO-decomposition (VASCO) problem. First, we slice volumes into slabs and build a dynamic-directed graph to encode manufacturing constraints, with each node representing a slab and direction reflecting operation order. We introduce a novel geometry property called hybrid-fabricability for a pair of additive and subtractive procedures. Then, we propose a beam-guided top-down block decomposition algorithm to solve the VASCO problem. We apply our solution to a 5-axis hybrid manufacturing platform and evaluate various 3D shapes. Finally, we assess the performance of our approach through both physical and simulated manufacturing evaluations.
Fanchao Zhong, Haisen Zhao, Jikai Liu, Baoquan Chen, Lin Lu 0001
ACM Trans. Graph.5
2021 Mechanical property profiles of microstructures via asymptotic homogenization
Peiqing Liu, Hao Peng 0001, Lihao Tian, Jikai Liu, Lin Lu 0001
Comput. Graph.5
2019 Multi-view feature modeling for design-for-additive manufacturing
Lei Li 0026, Jikai Liu, Yongsheng Ma, Rafiq Ahmad 0004, Ahmed Qureshi
Adv. Eng. Informatics2
2019 Level set-based heterogeneous object modeling and optimization
Jikai Liu, Yufan Zheng, Rafiq Ahmad 0004, Jinyuan Tang, Yongsheng Ma
Comput. Aided Des.1
2017 Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint
Jikai Liu, Albert C. To
Comput. Aided Des.1
2016 Product design-optimization integration via associative optimization feature modeling
Jikai Liu, Zhengrong Cheng, Yongsheng Ma
Adv. Eng. Informatics1
2016 Minimum void length scale control in level set topology optimization subject to machining radii
Jikai Liu, Huangchao Yu, Yongsheng Ma
Comput. Aided Des.1