Liuchao Jin

Liuchao Jin

PhD Candidate @ CUHK

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From Jin Village to Global Stage: A Journey Toward Equity and Belonging

3 minute read

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My name is Liuchao Jin. I was born in Jin Village, a remote farming village in eastern China where everyone shares the same family name—Jin (金). Legend says our ancestors were descendants of Emperor Liu Bang (劉邦) of the Han dynasty (202 BC–9 AD, 25–220 AD). Around 9 AD, during the political upheaval of the Xin Dynasty, one branch of the Liu (劉) clan fled south to escape persecution under the usurper Wang Mang. To avoid capture, they dropped the radicals “卯” and “刂” from the traditional “劉” and became “金”—a new family name for a new beginning in exile. That name has endured for nearly two thousand years—through dynastic changes, revolutions, and modernization—but in Jin Village, it became less a symbol of royal ancestry and more a quiet thread of survival passed down through generations.

Hong Kong Daily Information

1 minute read

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This blog provides the information of the life in Hong Kong (in Chinese).

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publications

The Unified Tracking Controller for a Tilt-Rotor Unmanned Aerial Vehicle Based on the Dual Quaternion

Published in 2022 IEEE International Conference on Unmanned Systems (ICUS), 2022

Tilt-rotor multi-rotor UAV is a rapidly expanding field of research due to its benefits of full actuation, high force and torque capabilities, and great efficiency of hovering. The current controllers of tilt-rotor multi-rotor UAVs are primarily based on the Cartesian coordinate system to describe the position combined with the classical Euler angle approach, the direction cosine matrix or the quaternion to represent the attitude, which makes control lose its mathematical simplicity and has some singularity cases. In this paper, the system modelling of a tilt-rotor multi-rotor UAV using the unit dual quaternion is presented and a novel PID feedback linearization tracker is proposed. The developed controller has advantages of singularity free, attitude/position coupled motion tracking, and robustness to external disturbance. Applying the Laplace transform, the stability analysis is conducted by analyzing the poles and zeros of the closed-loop system. Simulation studies including the 6 DoF trajectory tracking and disturbance rejection are also performed to demonstrate the effectiveness of the proposed method. The simulation results illustrate that the proposed PID feedback linearization tracker has good tracking performance for both position and attitude and strong robustness against disturbance.

Recommended citation: Liuchao Jin, Yuchen Lou, Lu-An Chen, and Qi Lu. (2022). "The Unified Tracking Controller for a Tilt-Rotor Unmanned Aerial Vehicle Based on the Dual Quaternion." 2022 IEEE International Conference on Unmanned Systems (ICUS). pp. 1356-1363. https://ieeexplore.ieee.org/abstract/document/9986880

A survey of additive manufacturing reviews

Published in Materials Science in Additive Manufacturing, 2022

Nowadays, additive manufacturing (AM) technologies have been widely used in construction, medical, military, aerospace, fashion, etc. The advantages of AM (eg, more design freedom, no restriction on the complexity of parts, and rapid prototyping) have attracted a growing number of researchers. Increasing number of papers are published each year. Until now, thousands of review papers have already been published in the field of AM. It is, therefore, perhaps timely to perform a survey on AM review papers so as to provide an overview and guidance for readers to choose their interested reviews on some specific topics. This survey gives detailed analysis on these reviews, divides these reviews into different groups based on the AM techniques and materials used, highlights some important reviews in this area, and provides some discussions and insights.

Recommended citation: Xiaoya Zhai, Liuchao Jin, and Jingchao Jiang. (2022). "A survey of additive manufacturing reviews." Materials Science in Additive Manufacturing. 1(4), 21. https://accscience.com/journal/MSAM/1/4/10.18063/msam.v1i4.21

Low-melting-point alloys integrated extrusion additive manufacturing

Published in Additive Manufacturing, 2023

Additive manufacturing has developed significantly. In contrast to established fabricated materials, low-melting-point alloys (LMPAs) are increasingly attractive because they have favorable electrical/thermal conductivities and mechanical strengths. However, LMPA additive manufacturing is still in its infancy. We report a novel strategy for fabricating the complex and/or multifunctional components of LMPAs by extrusion additive manufacturing with two nozzles (for extruding the polymer and for extruding the LMPA). The proposed strategy was used to successfully fabricate complex LMPA components for the first time. We fabricated LMPA/polymer composite parts with improved mechanical properties, and implemented the integrated manufacturing of circuits and 3D products. The strategy will enable the use of LMPAs in applications such as smart structures, electromagnetic shielding, biomedicine, thermal management, energy harvesting, and advanced electronics.

Recommended citation: Jingchao Jiang, Xiaoya Zhai, Kang Zhang, Liuchao Jin, Qitao Lu, Zhichao Shen, and Wei-Hsin Liao (2023). "Low-melting-point alloys integrated extrusion additive manufacturing." Additive Manufacturing. 103633. https://doi.org/10.1016/j.addma.2023.103633

On technical issues for underwater charging of robotic fish schools using ocean renewable energy

Published in Ships and Offshore Structures, 2023

Robotic fish will become the next generation of submersibles due to their advantages of high propulsion efficiency, high mobility, excellent environmental compatibility and good load capacity. However, short battery life and high charging costs would be the main obstacles restricting the deployment of robotic fish for long–term ocean monitoring and cruises. The present methods of either using a mother ship or laying cables are very expensive. In order to greatly reduce the cost, a nearby cheap charging station is necessary. In this paper, a comprehensive review of underwater automatic charging methods and systems for robotic fish based on the existing marine renewable energy conversion technology is carried out, including robotic fish underwater docking and charging technology. Based on the review and comparative analysis, a design idea for a novel and feasible system for underwater charging for a school of robotic fish through renewable energy is proposed.

Recommended citation: Liuchao Jin and Weicheng Cui. (2023). "On technical issues for underwater charging of robotic fish schools using ocean renewable energy." Ships and Offshore Structures. 1-11. https://doi.org/10.1080/17445302.2023.2245164

Isogeometric topology optimization of auxetic materials based on moving morphable components method

Published in Materials Research Proceedings, 2023

Auxetic materials are a class of materials that exhibit a negative Poisson’s ratio. They have held a major interest in academics and engineering focusing on finding the material distribution and examining the mechanisms, properties, and applications. Inverse homogenization theory is taken as an effective material design tool and has been applied to optimize various metamaterials. In this paper, we derive and implement the energy-based isogeometric homogenization to generate auxetic materials. Numerical examples show that the homogenized elasticity matrix obtained by the energy-based isogeometric homogenization method is almost the same as that obtained by the finite element homogenization method within a tolerated error. On this basis, we applied the isogeometric Moving Morphable Components (MMC) method to the optimization design of auxetic materials which is named the TOP-IGA-MMC method. We further make a comparison of the Solid Isotropic Material with the Penalization (SIMP) method and the TOP-IGA-MMC method in the geometries and properties of the final optimal auxetic materials. Parameter tests and physical tests are also introduced to verify the robustness and effectiveness of the proposed method.

Recommended citation: Xiaoya Zhai, Yundong Gai, Liuchao Jin, Wei-Hsin Liao, Falai Chen, and Ping Hu. (2023). "Isogeometric topology optimization of auxetic materials based on moving morphable components method." Materials Research Proceedings. 31, 172-186. https://doi.org/10.21741/9781644902592-19

Design for reversed additive manufacturing low-melting-point alloys

Published in Journal of Engineering Design, 2023

Additive manufacturing (AM) technologies have been widely used in construction, medical, military, aerospace, fashion, etc. As AM advances, increasing new AM-based manufacturing methods have been developed (e.g. CNC machining and AM hybrid manufacturing). Recently, a new manufacturing method ‘reversed additive manufacturing (RAM)’ was proposed by the authors. First, the designed objective part needs to be reversed using a bounding box, obtaining the reversed outside part. Then fabricate the reversed outside part using AM with dissolvable material (e.g. PLA). After that, fill the reversed outside part using aimed material (e.g. low-melting-point alloys) of the objective part. Lastly, soak the whole part into the dissolvent to dissolve the outside part, obtaining the final objective part. In this paper, design for RAM is proposed. Print orientation, print parameter settings, injection parameter settings, shrinkage, cost and post-processing are discussed. Experiments with several lattice structures are carried out and case studies are demonstrated. The findings of this paper can benefit the design process for RAM, improving the design efficiency for RAM.

Recommended citation: Jingchao Jiang, Xiaoya Zhai, Liuchao Jin, Kang Zhang, Jun Chen, Qitao Lu, and Wei-Hsin Liao. (2023). "Design for reversed additive manufacturing low-melting-point alloys." Journal of Engineering Design. 1-14. https://doi.org/10.1080/09544828.2023.2261096

A novel strategy to fabricate low-melting-point alloy and its composite parts using extrusion additive manufacturing

Published in The 50th International Conference on Computers and Industrial Engineering, 2023

Additive manufacturing (AM) has been continuously developed for more than 30 years. Different novel AM techniques, including electric-assisted, magnetic-assisted, robot-assisted, and UAV-assisted AM technologies have been created with various objectives. In this paper, we propose a new strategy for fabricating complex and/or multifunctional components of low-melting-point alloys (LMPA) using extrusion additive manufacturing (EAM). The main idea is using two nozzles in EAM, one for extruding polymers (e.g., PLA, PVA) while another for extruding LMPA. By using the proposed EAM system, there are three novel aspects of this study. First, the proposed system can achieve composite parts (polymer & LMPA) fabrication with improved mechanical properties. Second, the proposed system can be used to fabricate 3D products with LMPA wire inside acting as electrical wire. This achieves electrical wire inside a product without assembly, unlike conventional method which needs further step to insert the wires. Third, complex pure LMPA parts can be fabricated by dissolving the polymer after manufacturing, achieving complex LMPA parts fabrication using EAM (a low-cost AM technique, compared with traditional metal AM).

Recommended citation: Jingchao Jiang, Liuchao Jin, Xiaoya Zhai, Kang Zhang, Jun Chen, Wei-Hsin Liao. (2023). "A novel strategy to fabricate low-melting-point alloy and its composite parts using extrusion additive manufacturing." The 50th International Conference on Computers and Industrial Engineering. http://dx.doi.org/10.6084/m9.figshare.25434592

Optimizing stimuli-based 4D printed structures: a paradigm shift in programmable material response

Published in Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 2024

This paper introduces a methodology for optimizing 4D printing design through the integration of Residual Neural Network (ResNet) and Genetic Algorithms (GA). Departing from traditional forward design approaches, our inverse design methodology addresses both the forward prediction and inverse optimization problems. ResNet efficiently predicts the performance of 4D-printed parts given their design, while GA optimizes material allocation and stimuli distribution to achieve desired configurations. The ResNet model exhibits high accuracy, converging to a small error (10−3), as validated across diverse cases. The GA demonstrates effectiveness in achieving optimal or near-optimal solutions, illustrated through case studies shaping parts into a parabola and a sinusoid. Experimental results align with optimized and simulated outcomes, showcasing the practical applicability of our approach in 4D printing design optimization.

Recommended citation: Liuchao Jin, Xiaoya Zhai, Jingchao Jiang, Kang Zhang, Wei-Hsin Liao. (2024). "Optimizing stimuli-based 4D printed structures: a paradigm shift in programmable material response." Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems. http://dx.doi.org/10.1117/12.3014941

Achieving rapid actuation in liquid crystal elastomers

Published in National Science Open, 2024

Liquid crystal elastomer (LCE) is one kind of soft actuating material capable of producing large and reversible actuation strain, versatile and programmable actuation modes, and high work density, which can be widely exploited for nextgeneration soft robots. However, the slow response speed and low power density in LCE-based actuators remain a challenge, limiting their practical applications. Researchers have been considering how to improve these performances. In this review, we discuss the fundamentals of the LCEs and emphasize the fast actuation strategies developed in recent years. Firstly, we introduce conventional preparation strategies. Then, we describe typical actuation mechanisms of LCEs, discussing their features and limitations. Subsequently, we summarize several possible approaches as case studies to enhance the actuation performance of LCEs, including reducing physical sizes, introducing active heating-cooling mechanisms, utilizing mechanical instability, and developing dielectric LCEs. Finally, we discuss the future research opportunities and challenges for rapid actuation of LCEs.

Recommended citation: Changyue Liu, Liuchao Jin, Wei-Hsin Liao, Zhijian Wang, Qiguang He. (2024). "Achieving rapid actuation in liquid crystal elastomers." National Science Open. http://dx.doi.org/10.1360/nso/20240013

Big data, machine learning, and digital twin assisted additive manufacturing: A review

Published in Materials & Design, 2024

Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM data and utilize it for optimizing various aspects such as the manufacturing process, supply chain, and real-time monitoring. Data integration into proposed digital twin frameworks and the application of machine learning techniques is expected to play pivotal roles in advancing AM in the future. In this paper, we provide an overview of machine learning and digital twin-assisted AM. On one hand, we discuss the research domain and highlight the machine-learning methods utilized in this field, including material analysis, design optimization, process parameter optimization, defect detection and monitoring, and sustainability. On the other hand, we examine the status of digital twin-assisted AM from the current research status to the technical approach and offer insights into future developments and perspectives in this area. This review paper aims to examine present research and development in the convergence of big data, machine learning, and digital twin-assisted AM. Although there are numerous review papers on machine learning for additive manufacturing and others on digital twins for AM, no existing paper has considered how these concepts are intrinsically connected and interrelated. Our paper is the first to integrate the three concepts big data, machine learning, and digital twins and propose a cohesive framework for how they can work together to improve the efficiency, accuracy, and sustainability of AM processes. By exploring latest advancements and applications within these domains, our objective is to emphasize the potential advantages and future possibilities associated with integration of these technologies in AM.

Recommended citation: Liuchao Jin, Xiaoya Zhai, Kang Wang, Kang Zhang, Dazhong Wu, Aamer Nazir, Jingchao Jiang, Wei-Hsin Liao. (2024). "Big data, machine learning, and digital twin assisted additive manufacturing: A review." Materials & Design. 113086. https://doi.org/10.1016/j.matdes.2024.113086

Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes

Published in Applied Materials Today, 2024

The forward prediction and inverse design of 4D printing have primarily focused on 2D rectangular surfaces or plates, leaving the challenge of 4D printing parts with arbitrary shapes underexplored. This gap arises from the difficulty of handling varying input sizes in machine learning paradigms. To address this, we propose a novel machine learning-driven approach for forward prediction and inverse design tailored to 4D printed hierarchical architectures with arbitrary shapes. Our method encodes non-rectangular shapes with special identifiers, transforming the design domain into a format suitable for machine learning analysis. Using Residual Networks (ResNet) for forward prediction and evolutionary algorithms (EA) for inverse design, our approach achieves accurate and efficient predictions and designs. The results validate the effectiveness of our proposed method, with the forward prediction model achieving a loss below 0.02 mm, and the inverse optimization model maintaining an error near 1 mm, which is low relative to the entire shape of the optimized model. These outcomes demonstrate the capability of our approach to accurately predict and design complex hierarchical structures in 4D printing applications.

Recommended citation: Liuchao Jin, Shouyi Yu, Jianxiang Cheng, Haitao Ye, Xiaoya Zhai, Jingchao Jiang, Kang Zhang, Bingcong Jian, Mahdi Bodaghi, Qi Ge, Wei-Hsin Liao. (2024). "Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes." Applied Materials Today. 40. pp. 113086. https://doi.org/10.1016/j.apmt.2024.102373

Spider Web-Inspired Additive Manufacturing: Unleashing the Potential of Lightweight Support Structures

Published in 21st International Conference on Manufacturing Research, 2024

This paper explores the methodology for the utilization of spider web-inspired additive manufacturing to enhance overhang support structures in 3D printing. Inspired by the strength and flexibility of spider silk, we propose an approach that reduces material consumption and postprocessing efforts. The methodology includes 3D printing spider webs, addressing key questions on silk production, web strength, and printing path generation. Experimental results demonstrate substantial weight reduction in printed objects, showcasing the efficiency of spider web-inspired support compared to traditional methods. The potential applications extend to hollow shell printing and efficient mass production.

Recommended citation: Liuchao Jin, Xiaoya Zhai, Kang Zhang, Jingchao Jiang, Wei-Hsin Liao. (2024). "Spider Web-Inspired Additive Manufacturing: Unleashing the Potential of Lightweight Support Structures." 21st International Conference on Manufacturing Research. https://www.researchgate.net/publication/379928621_Spider_Web-Inspired_Additive_Manufacturing_Unleashing_the_Potential_of_Lightweight_Support_Structures

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