• Chih-Hung Gilbert Li

    李志鴻

     

    National Taipei University of Technology (Taipei Tech)

    國立台北科技大學

    Graduate Institute of Manufacturing Technology

    製造科技研究所

    Industry 4.0 Laboratory

    工業4.0實驗室

  • Personal Information

    簡歷

    Education 學歷

    Ph.D. Carnegie Mellon University / Mechanical Engineering

    卡內基梅隆大學機械工程博士

    M.S. Carnegie Mellon University / Mechanical Engineering

    卡內基梅隆大學機械工程碩士

    B.S. National Tsing Hua University / Power Mechanical Engineering

    國立清華大學動力機械工程學士

    Taipei Municipal Jianguo High School

    台北市立建國中學

    Experience 經歷

    Associate Dean / College of Mechanical and Electrical Engineering / National Taipei University of Technology

    國立台北科技大學機電學院副院長

    Associate Professor / National Taipei University of Technology

    國立台北科技大學製造科技研究所副教授

    Director / Office of International Affairs / Minghsin University of Science and Technology

    明新科大國際交流中心主任

    Director / Automated Vehicles and Equipment Development Center

    明新科技大學自動化載具與設備研發中心主任

    Engineering Specialist / Lord Corporation (USA)

    工程專家/美商羅德企業

  • Fields 領域

    務實 創新 合作 堅持

    Research of Industry 4.0

    工業4.0應用研究

    Artificial Intelligence, Cyber Physical System, Internet of Things, intelligent robots and vehicles

    人工智慧、網宇實體系統、物聯網、智動化機器人及交通工具

    Industry 4.0 is a collective noun. Its technologies such as Internet of Things, big data, cloud computing, artificial intelligence, automation, etc. are revolutionizing many industries including manufacturing. It is expected that not only will much of the production and management efficiency and flexibility be significantly increased, but Industry 4.0 is also more likely to change many existing commercial and industrial operating models. Through systematic research and testing, we are committed to proposing forward-looking and innovative operating models or technologies, such as the development of intelligent service robots, related topics of human-machine collaboration, the automated personal rapid transit system, etc. to promote the advancement of technology for human well-being.

    工業4.0為一集合名詞。其所涵括之物聯網、大數據、雲端運算、人工智慧、自動化等技術,正對包括製造業在內的許多產業產生革命性的影響。預期中,不僅許多生產及管理之效率與彈性可以大幅提升,工業4.0更可能翻轉許多現有的工商業運行模式。透過系統化地研究與測試,我們志在此範疇中,提出前瞻具創見之嶄新運營模式或技術,例如智慧型服務機器人的開發、人機協作之相關課題、以及智慧自動化的個人捷運系統之開發等,以促進科技對人類福祉之提升。

     

    SELECTIVE PUBLICATIONS:

    JOURNAL

    • Chih-Hung G. Li*, Yu-Ming Chang, "Automated visual positioning and precision placement of a workpiece using deep learning," The International Journal of Advanced Manufacturing Technology (SCI), 2019.

    CONFERENCE

    • Chi-Cheng Lai, Chih-Hung G. Li*, "Video-Based Windshield Rain Detection and Wiper Control Using Holistic-View Deep Learning," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.
    • Yi-Feng Hong, Yu-Ming Chang, Chih-Hung G. Li*, "Real-time Visual-Based Localization for Mobile Robot Using Structured-View Deep Learning," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.
    • Yu-Ming Chang, Chih-Hung G. Li*, Yi-Feng Hong, "Real-Time Object Coordinate Detection and Manipulator Control Using Rigidly Trained Convolutional Neural Networks," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.

    Structural Stress Analysis (Finite Element Analysis) and Optimization

    結構應力分析(有限元素分析法)與最佳化

    Structural topology optimization, nonlinear stress and strain analysis, fatigue and fracture analysis

    結構拓樸最佳化、非線性應力與應變分析、疲勞與破裂分析

    We have accumulated more than 20 years of experience in the finite element analysis. Varieties of linear or nonlinear structural stress problems were solved using the finite element software such as ANSYS. Projects include simple models such as trusses or elastic structures and more complex ones such as huge composite structures, large deformation or high strain analysis, contact and friction analysis, plastic deformation analysis, fatigue and fracture analysis, and dynamic collision analysis. In addition, by using the ANSYS APDL, projects that require large amounts of finite element analyses can be efficiently processed and completed. In the advanced design, the topology optimization design of the structure is obtained by using the artificial intelligence algorithm or the Evolutionary Structural Optimization method.

    累積個人與團隊超過20年的有限元素分析技術。舉凡各種線性及非線性之結構應力分析,透過有限元素分析軟體(如ANSYS)的運用,都可以迎刃而解。較簡單的如衍架分析與彈性結構分析等。較複雜的有超大型複合結構分析、彈性體大變形或大應變分析、接觸與摩擦分析、塑性變形分析、疲勞與破裂分析、及動態碰撞分析等。此外,透過程式自動化規劃(APDL),可以高效率處理需要大量有限元素分析的專案。在進階設計方面,運用人工智慧演算法或進化式結構拓樸最佳化法 (Evolutionary Structural Optimization)獲得結構之拓樸最佳化設計。

     

    SELECTIVE PUBLICATIONS:

    JOURNAL

    • Chih-Hung G. Li*, "Design of the lower chassis of a monorail personal rapid transit (MPRT) car using the evolutionary structural optimization (ESO) method," Structural and Multidisciplinary Optimization, 54 (1): 165-175 (SCI), 2016.
    • C. G. Li and B. P. Bautista, “On the compression of a stack of truncated elastomeric cones as a nonlinearly responsive spring,” Mech. Res. Commun, vol. 69, pp. 146–149 (SCI), 2015.

    CONFERENCE

    • Chih-Hung G. Li, "Strength-based Evolutionary Structural Optimization," in Proc. 24th International Congress of Theoretical and Applied Mechanics, Montreal, Canada, 2016.

    Development of Innovative Mechanisms

    創新機構開發設計

    Guitar robot, monorail system, mechanical damper, electromagnetic actuator, bus sliding door, vehicle suspension, retractable carriage, integrated music sounding teaching device, etc.

    吉他機器人、單軌電車系統、橡膠緩衝裝置、電磁致動裝置、巴士滑門、車輛懸吊結構、可伸縮之車廂機構、整合式音樂發聲教學裝置等等

    We are committed to invention and design of patent-protected mechanisms. Previous industry-university cooperation research projects include, but are not limited to, artificial intelligence applications, smart robots, intelligent automated transport systems, novel actuators, robot mechanisms, innovative shock absorbers, various mechanical structures, and equipment with special functions for vehicles. More than 20 domestic or foreign patents have been obtained, and many have been authorized to the industry.

    我們致力於各項專利保護之創新機構發明與設計。各項產學合作研究包含但不限定於人工智慧應用、智慧型機器人、智慧自動化運輸系統、新型致動器、機器人機構、新型避震裝置、各式機械結構、及各種車輛或載具所用的特殊功能設備等等。所獲得的國內外專利達二十件以上,並有多件已授權製造與販售。

     

    SELECTIVE PUBLICATIONS:

    JOURNAL

    • Chih-Hung G. Li*, Ming-Chang Lin, Basil A. Bautista, and Bettina E. To, "A Low-Noise Guitar Robot Featuring a New Class of Silent Actuators," IEEE ASME Transactions on Mechatronics (SCI), 2019.
    • Chih-Hung Li*, Zong Jun Lu, "An Innovative Straddle Monorail Track Switch Design for the Personal Rapid Transit," International Journal of Heavy Vehicle Systems (SCI), 2019.
    • Chih-Hung G. Li*, "A Novel Suspension Strut Featuring Constant Resonance Frequency," International Journal of Heavy Vehicle Systems, 22(4), 293-310. (SCI), 2015.

    CONFERENCE

    • C. G. Li and H. P. Nguyen, “Development of a linearly responsive electromagnetic actuator,” presented at Int. Conf. Computer Science, Data Mining & Mechanical Eng., Bangkok, Thailand, Apr. 20–21, 2015.
  • Projects 實績

    各項產學合作研究、開發、分析計畫之成果

    Workpiece Visual Placement Using Deep Learning

    深度學習之機器手工件精密定位擺放

    Deep Learning Application

    深度學習應用研究

    The deep learning framework for object coordinate detection can be applied to the precision visual placement of a workpiece. In the video, the robot arm is installed on a mobile platform. The deep learning visual detection system endows the manipulator a translational precision of ±0.2 mm; the binocular system controls the rotational error within ±0.1 degrees.

    基於深度學習的物件偵測技術可應用於工件之精密視覺定位擺放。影片中之機器手臂位於一移動平台上。深度學習之視覺偵測系統賦予機器手臂±0.2 mm 毫米之平移定位精度,雙相機系統控制旋轉誤差在±0.1度以內。

    Chih-Hung G. Li*, Yu-Ming Chang, "Automated visual positioning and precision placement of a workpiece using deep learning," The International Journal of Advanced Manufacturing Technology (SCI), 2019.

    Holistic-view Deep Learning for Automatic Windshield Wiper Activation

    雨刷自動控制之全景深度學習

    Deep Learning Application

    深度學習應用研究

    A windshield rain detection system using holistic-view deep learning is constructed in this project. A wiper control algorithm based on a time-series treatment is also presented. The video images of ordinary driving recorders were used to train a deep convolutional neural network for wiper activation classification. Overall, we achieved an average precision rate of 0.88 in our video-based rain detection experiments; our recall rate of 0.87 is significantly higher than the state-of-the-arts that averaged around 0.6. It is also proved that the proposed system is practical for real-time vehicle windshield rain detection and wiper control. In this film, a blue square indicates that our detection system recommends that the wiper should activate, and a yellow circle indicates it should not.

    在本專案中我們構建了採用整體視覺深度學習的擋風玻璃雨水探測系統,還提出了一種基於時間序列處理的雨刷控制算法。我們使用普通行車記錄儀的視頻圖像以訓練深度卷積神經網絡,以進行雨刷啟動分類。總體而言,我們在基於視頻的雨水檢測實驗中實現了0.88的平均精確率; 我們的召回率達到0.87,亦明顯高於平均約為0.6的現有一般水平。我們所提出的系統亦證明了實時車輛擋風玻璃雨水檢測和雨刷控制是可行的。在此影片中,藍色方塊表示我們的偵測系統建議雨刷應作動,而黃色圓圈表示雨刷不應作動。

    Chi-Cheng Lai, Chih-Hung G. Li*, "Video-Based Windshield Rain Detection and Wiper Control Using Holistic-View Deep Learning," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.

    Indoor Place Recognition of Mobile Robot

    運動機器人的室內場景辨識

    Deep Learning Application

    深度學習應用研究

    We built the indoor localization capability of mobile robots with a purely visual architecture. By using a plurality of cameras mounted on the robot and capturing images at multiple predetermined positions along the path, visual feature training sets were established and used to train a location classifier. Using deep learning architecture, we train the robot to recognize the global features of each position. In this test film, one can see that the robot recognizes each location when it navigates along the corridor. The code displayed at the upper left corner of the video changes from 0 to 20 in order. The overall precision rate is 92%; the recall rate is 87%.

    我們以純視覺的架構建立運動機器人的室內定點辨識能力。藉由在機器人身上裝置的多個相機,在預先規畫好的多個路徑位置上,拍攝影像供機器人學習各個場景的辨識。藉由深度學習架構我們訓練機器人認得每個位置的影像。在此測試影片中,可以看到機器人辨識出場景的代號,影片左上角由0至20順序被認出。綜合訓練所得之辨識精確率已達92%,召回率已達87%。

    Yi-Feng Hong, Yu-Ming Chang, Chih-Hung G. Li*, "Real-time Visual-Based Localization for Mobile Robot Using Structured-View Deep Learning," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.

    Intelligent Robot Motion Control

    智慧型機器人運動控制

    Deep Learning Application

    深度學習應用研究

    Mobile service robots can provide many functional services such as personal guidance, delivery, cleaning, transportation, equipment operation, assembly line assistance, security, medical assistance, etc. To be able to perform the above services, the robots' ability to detect, maneuver, and work in the environment is crucial. Our laboratory uses the latest deep learning ConvNet image recognition technology to enhance the robot's working ability. Through the vision-position direct control method developed by the team, the robot directly obtains coordinate information from the observed images for motion control. It can significantly shorten the robot's training time in the workplace. Convolutional neural networks have a high degree of abstraction and classification capability, and can deal with many environmental variations or signal noises. They exhibit stronger and more intelligent identification capabilities than the traditional image processing methods. They are very suitable for applications in the fields of manufacturing, medical care, service, transportation, etc.

    運動型服務機器人能夠在生活或工作場域中,提供許多功能性服務,如個人引導、遞交、清潔、運送、操作器材、裝配線的援助、保全及醫療協助等等。而為了達成上述多項任務,機器人在環境中的偵測、運動、及工作能力至關重要。本實驗室運用最新的深度學習捲積神經網路(ConvNet)影像辨識技術提升機器人的工作能力,藉由本團隊開發之視覺-位置直接控制法,使機器人直接由觀察影像獲得座標資訊進行運動控制,可以大幅縮短機器人在工作場域的訓練時間。捲積神經網路擁有高度抽象化辨別能力,能處理許多環境變異或雜訊,比起傳統之影像處理法展現了更強大的智慧辨識能力,為非常適合應用於製造、醫療、服務、交通等產業上之技術。

    Yu-Ming Chang, Chih-Hung G. Li*, Yi-Feng Hong, "Real-Time Object Coordinate Detection and Manipulator Control Using Rigidly Trained Convolutional Neural Networks," in Proc. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) Vancouver, BC, Canada.

    Real-time Object Coordinate Detection Using Deep Learning

    深度學習之物件實時座標偵測

    Deep Learning Application

    深度學習應用研究

    We use a deep learning framework for training object coordinate detection based on a single basis photo. As shown in the video, to train a coordinate detection scheme of a specific object, it only takes about 4 minutes on an ordinary notebook PC from taking the basis photo to completing the deep learning process.

    我們使用深度學習框架,只需基於一張基礎照片,即可訓練物件之座標位置偵測。如影片所示,訓練某特定物件之座標偵測,從開始拍攝一張基礎照片到完成全部深度學習,只需在一台普通筆記型電腦上運算約4分鐘。

    Indoor Self Driving of Balancing Robot

    平衡車機器人的室內自動駕駛

    Deep Learning Application

    深度學習應用研究

    In this project, the team uses a depth camera to capture continuous images of the front scene while the balance car is moving fast indoors. Learning through the deep convolutional neural networks, robots are taught to react naturally like humans while encountering various conditions in the indoor environment, such as obstacles, left and right walls, moving objects, and so on. The red indicator in the video represents the direction response of the deep neural network by instant image recognition. The reaction can be further used to control the electromechanical system of the robot to controll the direction of movement of the robot.

    在本計畫中,團隊以深度相機在平衡車機器人於室內快速移動時,拍攝前方景物之連續影像。再透過深度捲積神經網路之學習,教導機器人在面對室內環境的各種狀況,如障礙物、左右牆壁、移動物體等等時,擁有類似人類的自然反應。影像中的紅色指標代表深度神經網路藉由即時影像識別,所做出的方向反應。該反應可進一步用來控制機器人的機電系統,以達到控制機器人運動方向的目的。

     

    Industry 4.0 introduction method for small and medium enterprises

    中小企業工業4.0導入方案

    Can SMEs introduce the technologies of Industry 4.0, implement production tracking, flow-control on raw materials and finished products, and collect big data to optimize production processes or the product itself? The answer is - YES! By slightly expanding your basic ERP/MES and adopting a low-cost QR Code system, we can easily implement an automatic tracking system on the flow of raw material, storage, work order, scheduling, and shipment. The important parameters in the manufacturing processes can also be captured with sensors for real-time monitoring. The accumulated data can be stored to establish a production history database. The long-term accumulated data can be further analyzed using the Artificial Intelligence to optimize the production process or the products' performance and quality.

    中小企業是否也能導入工業4.0的相關技術,實施生產履歷建立、控管原物料及成品、蒐集大數據以優化生產過程或產品?

    答案是-YES! 透過稍微擴充基礎的ERP/MES系統,就可以低成本之二維碼掃描系統將原料進貨、倉儲、工單領料、排程、出貨等作業制度化。製程中之重要參數則可加裝感測器進行實時監控,並將數據上傳與儲存,建立生產履歷資料庫。長期累積的數據則可更進一步以各種人工智慧的方法進行分析,以優化製程或產品性能與品質。

    Development of educational materials

    for Industry 4.0 and Artificial Intelligence

    工業4.0與人工智慧教材開發與教育推廣

    文教機構合作研發計畫

    國內針對青少年程度編撰關於工業4.0及當代人工智慧之教材與教程仍相當少見。藉由與文教機構之產學合作案,本實驗室參考了國內外眾多資料,開發了青少年工業4.0營隊教材及青少年人工智慧營隊教材二份教材及實作組件,並指導營隊師資之教學內容及建立師資團隊。

    Development of intelligent and automated personal rapid transit (PRT)

    智動化個人捷運系統開發

    休閒科技股份有限公司合作開發案

    現代休閒社區的生活藍圖中,藉由整合相關的服務、網通、運輸、商業、物流、智慧與自動化等理念與科技,得以實現絕對休閒生活的目標。其中最為特別的是,在此社區規劃中,單軌個人捷運將肩負起整個休閒社區的運輸動靜脈。物聯網將成為各地的眼耳,串聯起感應各個脈動的神經。大數據將成為資料與訊息的寶庫,成為許多生活或商業活動理性判斷的基礎。而人工智慧則是社區的大腦,自動無誤地管理社區內日常生活的大小事。這些科技將具體實現並活絡整體的人流、物流、商流、與資訊流之智慧自動化,使得絕對休閒社區生活的理念得以實現。

     

    在本開發案中,團隊從零開始,設計了單軌電車的車體架構、動力系統、控制系統、與機電配置等,並開發了世界第一套專利的單軌車快速換軌系統,各車可以在十秒鐘之內接連通過換軌點而不需等待。驅動與軌道系統並可適應高低起伏的地形,特別適合應用於山林保護區的代步系統。輕量化的高架單軌系統最大限度地降低對環境的破壞,並可提供乘坐者接近大自然的舒適體驗。

    Chih-Hung Li*, Zong Jun Lu, "An Innovative Straddle Monorail Track Switch Design for the Personal Rapid Transit," International Journal of Heavy Vehicle Systems (SCI), 2019.

    Innovative Soft Actuator

    創新軟致動器

    基礎元件開發計畫

    In this research project, we have successfully developed an innovative electromagnetic actuator, which not only has the characteristics of quietness and softness, but also has the features of simple structure, moderate power, easy control, and low cost, ideal for applications such as the robots and automated machines that require superior quietness or human-machine collaboration. The specially designed tapered elastomer provides a highly nonlinear elastic response that achieves force equilibrium with the highly nonlinear electromagnetic force at various displacements and voltages. This actuator has the special characteristics that the input voltage is linearly proportional to the output displacement, and thus the motion control can be easily performed using a simple open circuit.

    本研究計畫成功開發出一種創新之電磁式致動器。不但具有靜音與軟的特點,並擁有構造簡單、力量適中、控制容易、與造價低廉等特性。非常適合應用在需要靜音或等需要人機協作之安全機器人或自動化機械上。特殊設計的錐形彈性體可發揮高度非線性彈性之功能,與高度非線性的電磁力在各個位移與電壓下達到力平衡。使得本致動器具有輸入電壓與輸出位移呈線性正比的特性,可輕易使用簡單之開迴路就能做運動控制。

    C. G. Li and H. P. Nguyen, “Development of a linearly responsive electromagnetic actuator,” presented at Int. Conf. Computer Science, Data Mining & Mechanical Eng., Bangkok, Thailand, Apr. 20–21, 2015.

    Quiet guitar robot

    靜音吉他機器人

    先導型研究計畫

    In order to solve the noise problems often associated with robots or automation equipment, we developed an innovative silent electromagnetic actuator. In addition to providing silent linear actuation, simple control of linear voltage response was also achieved. In this project, a guitar robot was created; the experimental evidence has shown that the mechanical noise of the guitar robot is much lower than that of conventional actuators such as pneumatic cylinders, servo motors, stepping motors, solenoids, et al., and is much lower than the guitar sound itself.

    為了解決機器人或自動化設備經常伴隨的噪音問題,本研究計畫開發了一種創新的靜音電磁致動器。除了可提供安靜無聲的線性致動,並具備線性電壓立即反應的簡單控制功能。為了展現此新型致動器之優點,本計畫特地創作了一具吉他演奏機器人。經由實驗證明,該吉他機器人之機械噪音遠低於一般傳統致動器如氣壓缸、伺服馬達、步進馬達、或電磁閥等所製造的噪音,並遠低於吉他本身所發出之樂音,因此得以彈奏出純淨之樂音。

    Chih-Hung G. Li*, Ming-Chang Lin, Basil A. Bautista, and Bettina E. To, "A Low-Noise Guitar Robot Featuring a New Class of Silent Actuators," IEEE ASME Transactions on Mechatronics (SCI), 2019.

    C. G. Li and B. P. Bautista, “On the compression of a stack of truncated elastomeric cones as a nonlinearly responsive spring,” Mech. Res. Commun, vol. 69, pp. 146–149 (SCI), 2015.

    中華民國發明專利 / 可撥弦之機械手指裝置 / 發明人李志鴻、包提達巴希爾 / 2017 / I582752

    Design optimization for monorail chassis structure

    單軌車架結構最佳化設計

    休閒科技股份有限公司合作開發案

    本案應用進化式結構最佳化(ESO)分析技術,對單軌車架進行最佳化設計。首先規劃出車架設計範圍,再運用進化式結構最佳化分析的精神,逐一去除較不重要的材料,而精煉成型出最佳之車架結構設計。由於車架在車輛運行中,無可避免會經歷加減速及左右轉等動態負載,因此本計畫特地著重於在計算中,反映出所有複雜的動態負載,以獲得可承受所有這些負載的綜合最佳化設計。此設計將有效減低車體重量,大量節省車輛運行所耗費的能量,成為環保節能的示範設計。

    Chih-Hung G. Li*, "Design of the lower chassis of a monorail personal rapid transit (MPRT) car using the evolutionary structural optimization (ESO) method," Structural and Multidisciplinary Optimization, 54 (1): 165-175 (SCI), 2016.

    Analysis and testing of huge equipment in amusement parks

    大型遊戲設備測試與分析

    產學合作案

    • 代表作品1:本案對摩天輪模型進行高速風洞測試,量測模型在各級風速下之受力,以預測真實的120米摩天輪是否能承受預定之風力。本實驗室製作一座二百分之一的摩天輪模型置於風洞中接受試驗,並透過相似性理論推測出實體摩天輪所可能承受之力量。最後用有限元素分析估算在各級風力下摩天輪主結構所承受之應力程度,並確認摩天輪主結構之強度。
    • 代表作品2:超大型遊戲設備飛行平台具有六軸自由度,且極限操作項目達三百餘種,再加上其架構非常複雜,因此各部位結構之應力與疲勞分析難度相當高。本專案以有限元素分析軟體建構擁有一千七百萬元素之模型,並透過自行撰寫之分析自動化程式,使得本分析得以在短時間內完成。

    Patented automobile suspension strut featuring constant frequency

    專利車用定頻懸吊柱

    休閒科技股份有限公司合作開發案

    為了符合單軌個人捷運電車輕量化的需求,並提升乘坐的舒適度,特別成立本計畫,以開發一款不受乘客人數與載重影響,而能自動維持在舒適彈跳頻率的懸吊柱。捨棄傳統金屬彈簧的設計,本設計採用彈性體高度非線性的特性,利用大變形與接觸有限元素分析,成功地設計出具有高度彈性變異性的懸吊柱。並透過模型製作與測試,驗證所預定的性能。因此,即便車體淨重極低,而不論載重多少,車輛運行時都可自適應地維持在舒適的彈跳頻率。本案已獲得專利並與相關廠商洽談技術授權中。

    Chih-Hung G. Li*, "A novel suspension strut featuring constant resonance frequency," International Journal of Heavy Vehicle Systems, 22 (4): 293-310 (SCI), 2015.

    Fatigue analysis of high speed pump shaft高速幫浦轉軸疲勞分析

    知名高速幫浦製造廠

    本分析專案使用有限元素分析法,進行一系列轉軸外型對應力分佈影響之探討,並據以進行疲勞分析。該疲勞分析不僅證實疲勞破裂的產生位置,並提供了後續幾何外型改善的建議方案,幫助合作廠商釐清與改善問題。

    Patented retractable carriage design

    專利車廂伸縮機構

    特殊用途車輛開發專案

    為了滿足本案業主對伸縮車廂的設計需求,在該伸縮車廂的機構開發中,特別著重於伸縮車廂展開後,地板的平整無落差,以及收縮後,地板的有效收納。伸縮車廂結構與支撐結構,以及所有相關零組件的強度與可能變形,亦透過有限元素分析法及理論公式的計算與分析加以驗證。該成果已獲得專利保護,並技術授權予業主製造。

    中華民國新型專利 / 可伸縮之車廂機構 / 發明人李志鴻 / 2015 / M496590

    Improvement on the brim profile of thick spin-coating layers

    厚層旋轉塗佈邊緣厚度改善方案

    知名藍光碟片製造商

    本計畫針對高厚度塗佈層邊緣易堆積,而產生厚度不均之現象,進行量測與分析,並提出改善方案。透過對塗料升溫可降低其黏滯性與表面張力的原理,對邊緣進行局部加溫,以降低塗料堆積的現象。我們運用有限元素分析對邊緣加溫的暫態反應進行計算,以掌握加溫功率對溫度變化的影響。並製作實驗驗證了邊緣局部加溫,對去除堆積現象所獲得的效果。

    Patented bus slide door

    專利巴士滑門開發

    國科會補助產學合作案

    本開發案針對巴士門的開關方式進行安全檢討,並獲得緊貼平移為最安全設計的結論。在此基礎上,本案對巴士門進行結構改善設計,除了利用滑軌與連桿建立緊貼平移的運動方式,並將動力傳動機構放置於門後之可利用空間內。該計畫獲得科技部產學合作研究補助。本案獲得美國及台灣在內之多國專利,並已技術授權予合作業者進行生產與銷售。

    美國發明專利 / Longitudinal-Slide Door Controlling Mechanism /發明人Chih-Hung Li / 2012 / US 8292349

    中華民國新型專利 / 巴士之橫移式門體連動機構 / 發明人李志鴻 / 2011 / M418828

    Patented lub-rubber dampers

    專利橡潤式緩衝棒開發

    櫃門滑軌與鉸練製造商

    櫃門的緩衝多半運用油壓的原理設計緩衝棒。多家國際知名五金製造商均有供應類似產品。然而油壓式的設計經常遭遇漏油的問題,並可能汙染居家環境造成困擾。基於除去此漏油問題的理念,本專案開發了專利的橡膠潤滑式緩衝棒,並取得包含美國、大陸、台灣等多國專利。由於緩衝機制來自於潤滑的橡膠而非油壓,因此完全除去漏油的問題。在開發過程中,本案大量運用有限元素分析橡膠件的接觸壓力與變形,使用非線性材料模式與非線性求解過程,以精確計算出相關數據,並透過實體模型之耐久性能測試,證實此產品之實用性。部分產品設計已技術授權予相關廠商進行量產銷售。

    美國發明專利 / Cabinet Door Buffer Bar / 發明人Chih-Hung Li / US 7076834

    中華民國發明專利 / 緩衝棒 / 發明人李志鴻 / 2003 / 538202

    中華民國發明專利 / 櫃門緩衝棒 / 發明人李志鴻 / 2004 / I225533

    Integrated music sounding teaching device

    專利整合式音樂教學裝置

    跨領域合作研發計畫

    本計畫榮獲科技部研究創作獎,為跨領域結合工程與幼教專業師生共同研究之成果,並整合相關業界之意見,成為具商品化價值之成品。此教學裝置可對幼兒之音樂表現產生反應,以引導幼兒學習正確的音感與韻律感。在開發過程中,由幼教專業的師生進行教具的功能設定與教學設計,並由工程專業的師生進行教具的結構與電子設計,以提供幼兒音樂教學所需之功能。本案之成果經遠見雜誌報導為跨領域合作研發之優良範例,並正與相關廠商洽談專利與技術授權中。

    中華民國新型專利 / 整合式音樂發聲教學裝置 / 發明人廖美瑩、林鈺姍、李志鴻 / 2015 / M515186

    活動剪影

    Dynamically Balanced Robot with a Manipulator

    擁有機械手臂之自平衡機器人

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