請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94553完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李貫銘 | zh_TW |
| dc.contributor.advisor | Kuan-Ming Li | en |
| dc.contributor.author | 戴志和 | zh_TW |
| dc.contributor.author | Chih-Ho Tai | en |
| dc.date.accessioned | 2024-08-16T16:42:11Z | - |
| dc.date.available | 2024-08-17 | - |
| dc.date.copyright | 2024-08-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-14 | - |
| dc.identifier.citation | 1. 蔡穎德, 等負載加工之參數訂定策略. 2020, 國立臺灣大學. p. 1-82.
2. 陳翰霖, 應用考慮刀具磨耗之學習控制技術於銑削加工. 2021. 3. Landers, R.G., et al., A review of manufacturing process control. Journal of Manufacturing Science and Engineering, 2020. 142(11). 4. Pimenov, D.Y., et al., Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach. Measurement, 2022: p. 111503. 5. Mohamed, A., et al., Tool condition monitoring for high-performance machining systems—A review. Sensors, 2022. 22(6): p. 2206. 6. Compean, F.I., et al., Characterization and stability analysis of a multivariable milling tool by the enhanced multistage homotopy perturbation method. International Journal of Machine Tools and Manufacture, 2012. 57: p. 27-33. 7. Urbikain, G., L.N.L. De Lacalle, and A. Fernández, Regenerative vibration avoidance due to tool tangential dynamics in interrupted turning operations. Journal of Sound and Vibration, 2014. 333(17): p. 3996-4006. 8. Tran, M.-Q., et al., Machine Learning and IoT-based Approach for Tool Condition Monitoring: A Review and Future Prospects. Measurement, 2022: p. 112351. 9. Siddhartha, B., et al. IoT Enabled Real-Time Availability and Condition Monitoring of CNC Machines. in 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS). 2021. 10. Ghobakhloo, M., Industry 4.0, digitization, and opportunities for sustainability. Journal of cleaner production, 2020. 252: p. 119869. 11. Ulsoy, A.G., Y. Koren, and F. Rasmussen, Principal developments in the adaptive control of machine tools. 1983. 12. Zuperl, U., F. Cus, and M. Reibenschuh, Neural control strategy of constant cutting force system in end milling. Robotics and Computer-Integrated Manufacturing, 2011. 27(3): p. 485-493. 13. Koren, Y. Adaptive control systems for machining. in 1988 American Control Conference. 1988. IEEE. 14. Lauderbaugh, L. and A. Ulsoy, Dynamic modeling for control of the milling process. 1988. 15. Ulsoy, A.G. and Y. Koren, Applications of adaptive control to machine tool process control. IEEE Control Systems Magazine, 1989. 9(4): p. 33-37. 16. Liu, Y., T. Cheng, and L. Zuo, Adaptive control constraint of machining processes. The International Journal of Advanced Manufacturing Technology, 2001. 17(10): p. 720-726. 17. Liu, Y. and C. Wang, Neural network based adaptive control and optimisation in the milling process. The International Journal of Advanced Manufacturing Technology, 1999. 15(11): p. 791-795. 18. Yang, M.-Y., T.-M. Lee, and J.-G. Choi, A new spindle current regulation algorithm for the CNC end milling process. The International Journal of Advanced Manufacturing Technology, 2002. 19(7): p. 473-481. 19. Yang, M.-Y. and T.-M. Lee, Hybrid adaptive control based on the characteristics of CNC end milling. International Journal of Machine Tools and Manufacture, 2002. 42(4): p. 489-499. 20. Altintas, Y., Prediction of cutting forces and tool breakage in milling from feed drive current measurements. 1992. 21. Choi, J.-G. and M.-Y. Yang, In-process prediction of cutting depths in end milling. International Journal of Machine Tools and Manufacture, 1999. 39(5): p. 705-721. 22. Lee, K.-J., T.-M. Lee, and M.-Y. Yang, Tool wear monitoring system for CNC end milling using a hybrid approach to cutting force regulation. The International Journal of Advanced Manufacturing Technology, 2007. 32(1): p. 8-17. 23. Hertz, H., Ueber die Berührung fester elastischer Körper. 1882. 24. Gorb, S.N., Review of" contact mechanics and friction: physical principles and applications" by Valentin L. Popov. Beilstein Journal of Nanotechnology, 2011. 2(1): p. 57-58. 25. Popov, V.L. and M. Heß, Methode der Dimensionsreduktion in Kontaktmechanik und Reibung. Springer. 26. Hung, J.-P., et al., Modeling the machining stability of a vertical milling machine under the influence of the preloaded linear guide. International Journal of Machine Tools and Manufacture, 2011. 51(9): p. 731-739. 27. Lin, Y. and W. Chen, A method of identifying interface characteristic for machine tools design. Journal of Sound and Vibration, 2002. 255(3): p. 481-487. 28. Swab, J.J. and J.J. Pittari III, Compression strength of tungsten carbide–cobalt hard metals. International Journal of Applied Ceramic Technology, 2023. 20(1): p. 509-518. 29. Horii, H. and S. Nemat-Nasser, Brittle failure in compression: splitting faulting and brittle-ductile transition. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 1986. 319(1549): p. 337-374. 30. Bansal, R., Engineering Mechanics and Strength of materials. 1998: Laxmi Publications. 31. Shigley, J., Shigley's mechanical engineering design. 2011. Tata McGraw-Hill Education. 32. Budynas, R.G. and J.K. Nisbett, Shigley's mechanical engineering design. Vol. 9. 2011: McGraw-Hill New York. 33. Callister Jr, W.D., Materials science and engineering an introduction. 2007. 34. Hartsuijker, C. and J.W. Welleman, Engineering Mechanics: Volume 2: Stresses, Strains, Displacements. Vol. 2. 2007: Springer Science & Business Media. 35. Sadowski, T. and T. Nowicki, Numerical investigation of local mechanical properties of WC/Co composite. Computational Materials Science, 2008. 43(1): p. 235-241. 36. Okamoto, S., et al., Mechanical properties of WC/Co cemented carbide with larger WC grain size. Materials Characterization, 2005. 55(4-5): p. 281-287. 37. Theocaris, P. and G. Stavroulakis, The homogenization method for the study of variation of Poisson's ratio in fiber composites. Archive of Applied Mechanics, 1998. 68: p. 281-295. 38. Lu, X., et al., A modified analytical cutting force prediction model under the tool flank wear effect in micro-milling nickel-based superalloy. The International Journal of Advanced Manufacturing Technology, 2017. 91: p. 3709-3716. 39. Sun, Y., et al., A modified analytical cutting force prediction model under the tool crater wear effect in end milling Ti6Al4V with solid carbide tool. The International Journal of Advanced Manufacturing Technology, 2020. 108: p. 3475-3490. 40. Budak, E., Y. Altintas, and E. Armarego, Prediction of milling force coefficients from orthogonal cutting data. 1996. 41. Liew Yun Hsien, I.D.W., I.M. Hutchings, and J.A. Williams, Friction and lubrication effects in the machining of aluminium alloys. Tribology Letters, 1998. 5: p. 117-122. 42. Shan, C., et al., Prediction of machining-induced residual stress in orthogonal cutting of Ti6Al4V. The International Journal of Advanced Manufacturing Technology, 2020. 107: p. 2375-2385. 43. Ding, H., et al., Instantaneous milling force prediction and valuation of end milling based on friction angle in orthogonal cutting. The International Journal of Advanced Manufacturing Technology, 2021. 116: p. 1341-1355. 44. Wang, J., C.Z. Huang, and W.G. Song, The effect of tool flank wear on the orthogonal cutting process and its practical implications. Journal of Materials Processing Technology, 2003. 142(2): p. 338-346. 45. Liu, J., P. Zhang, and F. Wang. Real-time dc servo motor position control by pid controller using labview. IEEE. 46. Kooi, S. Robust adaptive control for nonlinear end milling process. in Proceedings of 1995 American Control Conference-ACC'95. 1995. IEEE. 47. Charbonnaud, P., F. Carrillo, and D. Ladevèze, Monitored robust force control of a milling process. Control Engineering Practice, 2001. 9(10): p. 1047-1061. 48. Yang, J., D. Zhang, and Z. Li. Modeling and Identification for High-Speed Milling Machines. in 2007 IEEE International Conference on Automation Science and Engineering. 2007. IEEE. 49. 邱雅琳, 等切削力控制系統動態特性建立之研究, in 機械工程學研究所. 2017, 國立臺灣大學: 台北市. p. 71. 50. 鄭力維, 工具機等切削力控制與刀具磨耗關係之探討, in 機械工程學研究所. 2018, 國立臺灣大學: 台北市. p. 62. 51. 鄭呈毅, 等負載銑削加工之主軸負載參考值設定流程, in 機械工程學研究所. 2019, 國立臺灣大學: 台北市. p. 106. 52. Altintaş, Y., Direct adaptive control of end milling process. International Journal of Machine Tools and Manufacture, 1994. 34(4): p. 461-472. 53. Stein, J., et al., Evaluation of dc servo machine tool feed drives as force sensors. 1986. 54. Kim, T.-Y. and J. Kim, Adaptive cutting force control for a machining center by using indirect cutting force measurements. International Journal of Machine Tools and Manufacture, 1996. 36(8): p. 925-937. 55. Xia, X.-G., System identification using chirp signals and time-variant filters in the joint time-frequency domain. IEEE Transactions on Signal Processing, 1997. 45(8): p. 2072-2084. 56. Alia, M.A., T.M. Younes, and S.A. Alsabbah, A design of a PID self-tuning controller using LabVIEW. Journal of Software Engineering and Applications, 2011. 4(3): p. 161. 57. Ohlsson, H., L. Ljung, and S. Boyd, Segmentation of ARX-models using sum-of-norms regularization. Automatica, 2010. 46(6): p. 1107-1111. 58. Li, M., C. Chen, and W. Liu. Identification based on MATLAB. in Proceedings. The 2009 International Workshop on Information Security and Application (IWISA 2009). 2009. Academy Publisher. 59. Spence, A. and Y. Altintas, CAD assisted adaptive control for milling. 1991. 60. Fussell, B. and K. Srinivasan, On-line identification of end milling process parameters. 1989. 61. Fussell, B. and K. Srinivasan. Model reference adaptive control of force in end milling operations. in 1988 American Control Conference. 1988. IEEE. 62. Li, Y., K.H. Ang, and G.C.Y. Chong, Patents, software, and hardware for PID control: an overview and analysis of the current art. IEEE Control Systems Magazine, 2006. 26(1): p. 42-54. 63. Haugen, F., Basic Dynamics and Control. Skien, Norway: TechTeach, 2009. 103. 64. Oosting, K.W., Simulation of control strategies for a two degree-of-freedom lightweight flexible robotic arm. 1987. 65. Alberts, T.E., AUGMENTING THE CONTROL OF A FLEXIBLE MANIPULATOR WITH PASSIVE MECHANICAL DAMPING. 1988. 66. Oosting, K.W. and S.L. Dickerson. Feed Forward Control for Stabilization. 1987. ASME. 67. Siciliano, B., O. Khatib, and T. Kröger, Springer handbook of robotics. Vol. 200. 2008: Springer. 68. Bristow, D.A., M. Tharayil, and A.G. Alleyne, A survey of iterative learning control. IEEE control systems magazine, 2006. 26(3): p. 96-114. 69. Li, H., H. Zeng, and X. Chen, An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts. Journal of Materials Processing Technology, 2006. 180(1-3): p. 296-304. 70. Elbestawi, M., T. Papazafiriou, and R. Du, In-process monitoring of tool wear in milling using cutting force signature. International Journal of Machine Tools and Manufacture, 1991. 31(1): p. 55-73. 71. Lee, B., Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. The International Journal of Advanced Manufacturing Technology, 1999. 15(4): p. 238-243. 72. Grosvenor, R., C. Johns, and P. Prickett. Machine tool axis signals for condition monitoring. in Proceedings of COMADEM. 1996. 73. Shao, H., H. Wang, and X. Zhao, A cutting power model for tool wear monitoring in milling. International Journal of Machine Tools and Manufacture, 2004. 44(14): p. 1503-1509. 74. Wasif, M., et al., Optimization of simplified grinding wheel geometry for the accurate generation of end-mill cutters using the five-axis CNC grinding process. The International Journal of Advanced Manufacturing Technology, 2019. 105. 75. Kusyairi, I., et al. Manufacture of Origami Pattern Crash Box Using Traditional Investment Casting Method. in IOP Conference Series: Materials Science and Engineering. 2019. IOP Publishing. 76. Wang, F., et al., Secondary cutting edge wear of one-shot drill bit in drilling CFRP and its impact on hole quality. Composite Structures, 2017. 178: p. 341-352. 77. DeVries, W.R., Analysis of material removal processes. 2012: Springer Science & Business Media. 78. Zuperl, U., F. Cus, and M. Milfelner, Fuzzy control strategy for an adaptive force control in end-milling. Journal of Materials Processing Technology, 2005. 164: p. 1472-1478. 79. Tai, C.-H., Y.-T. Tsai, and K.-M. Li, Establishment of Real-time Adaptive Control Strategy for Milling Parameters. IEEE Access, 2023. 80. Koenigsberger, F. and A.J.P. Sabberwal, An investigation into the cutting force pulsations during milling operations. International Journal of Machine Tool Design and Research, 1961. 1(1): p. 15-33. 81. Craig Jr, R.R. and E.M. Taleff, Mechanics of materials. 2020: John Wiley & Sons. 82. Cardarelli, F., Materials handbook: a concise desktop reference. 2008. 83. Mahmudah, A., G. Kiswanto, and D. Priadi. Fabrication of punch and die of micro-blanking tool. in IOP Conference Series: Materials Science and Engineering. 2017. IOP Publishing. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94553 | - |
| dc.description.abstract | 隨著航太科技、電子產品、通訊設備、醫療器械與電動車產業的快速發展,近幾年來材料的銑削加工技術已被廣泛應用在這些領域的產品製程中,而自動化的加工機台必須有正確的加工條件,才能穩定的生產來創造效率,提升材料良率,降低刀具損壞或斷刀的影響,達成高經濟效率。銑削加工的參數儼然成為加工材料製程的主流研究之一。
銑削適應性控制加工是經由控制切削力,來達成恆定負載的加工。本研究就前饋控制及回饋控制建置系統進行探討,方便於日後在生產線上能實際應用。若要優化加工結果,就需了解銑削的加工機制進而透過最大進給率及最大磨耗量的估算,來設置加工參數及建立控制模型,進而得出一套銑削參數設定的策略來改善傳統銑削加工靠經驗建置加工參數的問題。本研究以刀具與工件的接觸應力模型計算刀具所能承受之最大切削力,並以主軸電流作為控制切削力的參數,建置回饋控制與前饋控制系統,在兩種不同的控制模式中調控進給率來進行適應性控制加工。並透過對刀具磨耗的監測,來研究銑削適應性控制加過程工中磨耗的機制,進一步驗證適應性控制加工參數設定的策略對改善傳統加工之刀具壽命的效果。 傳統的銑削加工方法常常是由老師傅的經驗來設定加工參數,為避免加工機器、工件及刀具的故障,會使用較為保守的固定進給率來加工。採用適應性控制在加工過程中適當調整進給率,是可以有效的提高加工效率的方法。已經有許多關於切削力適應性控制的控制器設計相關的研究,但這些研究都集中在如何調控切削力的方法上。很少討論以主軸電流作為控制切削力訊號,來建立在適應性控制中,切削力與刀具磨耗之間的關係,並作為適應性控制加工參數的設定方法。本研究提出了銑削加工的接觸應力模型假設,經由接觸應力模型來計算刀具所能承受之最大切削力,再推估出最大切削力對應的最大進給率,與相對應的最大主軸電流,由此開始進行適應性控制製程參數的設定策略,包括設定主軸參考電流及進給率上限和下限。設定主軸參考電流和進給速率上限,以維持切削力在刀具所能承受的安全值。而將進給率下限設定在刀具磨耗的安全範圍內,以避免刀具崩壞或嚴重磨耗。 在前饋與回饋控制的實驗結果皆驗證,當進給率下降至下限值時,本論文提出的參數設定方法可以使刀具磨耗維持在設定範圍內,控制良好且沒有刀具損壞。 | zh_TW |
| dc.description.abstract | In recent years, with the rapid progress of aerospace technology, electronic products, communication equipment, medical equipment and the electric vehicle industry, the milling processing technology of materials has been widely used in product processes in these fields. The automated machining machine must have correct processing parameters to stabilize production to create efficiency, improve the quality of materials, reduce the effects of tool damages, and achieve high economic efficiency. The parameters of this milling processing have become one of the mainstream research projects on material processing.
The adaptive control of milling process is to achieve a constant load machining by controlling the cutting force. Constant load milling processing is mainly divided into feedforward control and feedback control, as well as complicated neural network control and fuzzy control. The latter two are less likely to be used at the actual application of the processing site. The feedforward control and feedback control models were studied in this dissertation. To optimize the processing results, we need to understand the processing mechanism of milling, and then set the processing parameters and establishing a model through the estimation of the maximum feedrate and maximum grinding range, thereby improving the problem of traditional milling processing parameters. In this study, the spindle current was used as the parameter for cutting force adaptive control, and feedback control and feedforward control were constructed for constant load machining. By monitoring tool wear, the mechanism of the wear process in constant load milling processing was studied. Further verified the effect of the constant load machining parameter setting strategy on improving the tool life of traditional machining. Traditional milling processing methods often set processing parameters based on the experience of master craftsmen. In order to prevent failures of processing machines, workpieces and tools, a more conservative constant feedrate is used for processing. However, the processing efficiency of this method is difficult to further improve. Using adaptive control to appropriately adjust the feedrate during processing is an effective method to improve processing efficiency. There have been many studies related to the design of controllers for adaptive control of cutting forces, but these studies have focused on how to regulate cutting forces. There is little discussion about using spindle current as a cutting force control signal to establish the relationship between cutting force and tool wear in adaptive control, and as a method for setting constant load machining parameters. This study proposed a contact stress model hypothesis for milling processing, and uses the contact stress model to calculate the maximum cutting force that the tool can withstand. Then estimate the maximum feedrate corresponding to the maximum cutting force and the corresponding maximum spindle current. From this point on, the setting strategy for the constant load process parameters was began, including setting the reference spindle current and the upper and lower limits of the feedrate. Set the spindle reference current and feedrate upper limit to maintain the cutting force at a safe value that the tool can withstand. And set the lower limit of the feedrate within the safe range of tool wear to avoid tool collapse or serious wear. The experimental results of feedforward and feedback control both verified that when the feedrate drops to the lower limit, the parameter setting method proposed in this paper can maintain the tool wear within the set range, with good control and no risk of tool damage. The set processing parameters were further used for end milling, slot milling and the mixed milling processes to verify the feasibility of simple microfluidic mold processing, and also obtain good control results. In this study, the strategy for setting constant load machining parameters proposed was superior to traditional fixed feedrate machining in terms of tool life and material removal rate. Traditional fixed feedrate milling processes estimate tool life through the cutting length. However, this study used the change in feedrate decrease to estimate the change in tool wear and predict the tool change timing. And experimental results had verified that this method had better tool life than traditional fixed feedrate milling. The experimental results of feedforward control effectively improved the poor surface finish caused by rapid up and down changes in feedrate in feedback control. The surface quality of the workpiece was better than that of traditional fixed feedrate milling. When the feedrate was reduced to reach the lower limit, the tool wear is also verified to be maintained within the set safety range, which could effectively prevent excessive wear of the tool and predict the tool replacement opportunity. The adaptive milling parameter setting strategy and stress model proposed in this study had better results in material removal rate, surface roughness and tool life than traditional fixed feedrate processing. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-16T16:42:11Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-16T16:42:11Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 論文口試委員審定書 I
謝辭 II 摘要 III Abstract V 目次 VIII 圖次 XI 表次 XV 第1章 緒論 1 1.1研究動機 1 1.2研究目的 2 1.3論文架構 2 第2章 適應性控制控制與相關學理 4 2.1適應性控制 4 2.2主軸電流與切削力控制 6 2.3接觸應力 10 2.4銑削切削力模型 15 2.5回饋控制技術 16 2.5.1系統識別(System Identification) 17 2.5.2回饋控制器之設計 19 2.6前饋控制技術 22 2.7刀具磨耗 25 2.8表面粗糙度 27 2.9小結 28 第3章 實驗配置與設備 30 3.1實驗配置 30 3.2 五軸立式加工機 33 3.3 加工材料與碳化鎢銑刀 34 3.4 訊號擷取卡(DAQ) 35 3.5 電流鉤表 36 3.6 顯微數位攝影機 37 3.7 千分錶 38 3.8 表面粗度測定儀 38 3.9 動力計與訊號放大器 40 3.10 數位類比轉換器 42 第4章 適應性控制銑削加工參數設定之研究 45 4.1 適應性控制銑削參數之選定策略 46 4.1.1最大接觸應力計算 46 4.1.2 槽銑進給率上限與主軸參考電流之設定 47 4.1.3端銑進給率上限與主軸參考電流之設定 51 4.1.4 刀具磨耗範圍與進給率下限之設定 52 4.2 回饋控制參數設定與驗證 57 4.2.1 PI 控制器參數設定 57 4.2.2回饋控制效果驗證- 槽銑 59 4.2.3回饋控制效果驗證- 端銑 67 4.3 前饋控制參數設定與驗證 70 4.3.1 前饋控制初始進給率之設定 71 4.3.2 前饋控制進給率下限之設定 72 4.3.3 前饋控制進給率調控參數 77 4.3.4 前饋控制效果驗證 79 4.4 小結 82 第5章 銑削加工參數設定策略驗證之研究 83 5.1 回饋控制之加工驗證 83 5.1.1 回饋控制加工驗證- 槽銑 83 5.1.2 回饋控制加工驗證- 端銑 86 5.1.3 回饋控制混合(槽銑與端銑)加工驗證 90 5.2 前饋控制之加工驗證 93 5.2.1 前饋控制之加工驗證- 槽銑 93 5.2.2 前饋控制之加工驗證- 端銑 98 5.2.3 前饋控制之加工驗證- 槽銑混合端銑 104 第6章 結論與未來展望 112 6.1 結論 112 6.2 未來展望 113 參考文獻 115 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 適應性控制 | zh_TW |
| dc.subject | 接觸應力 | zh_TW |
| dc.subject | 主軸電流 | zh_TW |
| dc.subject | 換刀時機 | zh_TW |
| dc.subject | 刀具壽命 | zh_TW |
| dc.subject | 刀具磨耗 | zh_TW |
| dc.subject | Contact stress | en |
| dc.subject | Adaptive control | en |
| dc.subject | Tool wear | en |
| dc.subject | Tool life | en |
| dc.subject | Tool change | en |
| dc.subject | Spindle current | en |
| dc.title | 等負載銑削加工中製程參數設定策略及其對刀具壽命的影響 | zh_TW |
| dc.title | Process Parameter Setting Strategy and its Impact on Tool Life in Constant Load Milling Process | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 楊宏智;蔡曜陽;蔡孟勳;盧銘詮 | zh_TW |
| dc.contributor.oralexamcommittee | Hong-Tsu Young;Yao-Yang Tsai;Meng-Shiun Tsai;Ming-Chyuan Lu | en |
| dc.subject.keyword | 接觸應力,適應性控制,刀具磨耗,刀具壽命,換刀時機,主軸電流, | zh_TW |
| dc.subject.keyword | Contact stress,Adaptive control,Tool wear,Tool life,Tool change,Spindle current, | en |
| dc.relation.page | 119 | - |
| dc.identifier.doi | 10.6342/NTU202304449 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-14 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| 顯示於系所單位: | 機械工程學系 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf 授權僅限NTU校內IP使用(校園外請利用VPN校外連線服務) | 10.79 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
