臺灣博碩士論文加值系統

禽蛋品質的好壞不僅與食品安全息息相關，也影響之後製成食品的口感以及加工特性，品質檢測不僅是價值創造之關鍵，也是食品安全之核心。國際上已有荷蘭、日本等國大廠積極投入禽蛋品質自動化檢測市場，而我國蛋品設備僅有清洗與分級包裝之功能，缺乏裂痕等品質檢測之功能，無法滿足現代蛋品品質需求，蛋品設備也不具有國際競爭力。本實驗室根據前年之科技部計畫 -「蛋品裂痕檢測技術之研究」之結果，解決部分禽蛋常沾黏糞便、泥土等異物影響裂痕檢測準確率與良率的問題，以此成果作為基礎，本研究將檢測點數由56點精簡至8點，可簡化機構且降低敲擊在糞便、泥土上的機率，並改變機構敲擊方式擴大可檢測範圍，利用磁力相吸原理設計敲擊機構，將麥克風置於特殊集音設備內接收響應訊號，以濾波器過濾鴨蛋特徵頻率以外的音頻訊號減少干擾問題，由實驗結果得知，鴨蛋特徵頻率範圍落在4200 ~ 5700 Hz之間，無裂痕鴨蛋振幅介於0.62 ~ 2.82 V，而裂痕鴨蛋頻率可能與完整蛋相同，但其振幅多低於0.6 V，並由此結果設計濾波器通頻帶範圍，以裂痕檢測模組進行動態測試，準確率可達100 %。期望本實驗成果能解決國內之蛋品清洗分級機缺乏多無裂痕檢測功能之問題，提升蛋品經濟價值。

The quality of the poultry eggs is not only a food safety issue, but also a key to the taste and processing characteristics of the processed food. Besides, quality testing is not only the key to market value creation but also the core for food safty. Automated egg quality testing has already been conducted in various countries like Netherlands and Japan. However, in Taiwan, our facilities are only capable of performing cleaning, sorting, and packing of the eggs. We are still lack of technologies for detecting fissures, in that case ,our poultry egg are unable to meet the market requirement, besides, equipment lost its competitiveness in the globle market. By the results of the Ministry of Science and Technology project of the previous years, "Study of egg crack detection research", solved the problem of accuracy and the yield deficiency in fissures detection caused by feces and dirt stains on the egg shells. Based on the result , this study will reduce the number of test points from 56 to 8 , which can simplifying the mechanism and decrease the probability of tapping on the feces and soil. By changing the method of tapping will expand the detectable range, and design the mechanism by principle of magnetic. The microphone is set in a particular sound collector to receive the reponse signal, and the signal other than the frequency of the duck egg will be filtered by the filter to reduce the interference problem. From the result of experiment, the eigenfrequency range of eggs between 4200 Hz and 5700 Hz, and amplitude of crack of crack-free eggs ranges from 0.62 to 2.82 V. Besides, the frequency of cracked eggs might be the same to non-crcaked egg. However, its amplitude is less than 0.6 V. The range of passband is designed from this result. The crack detection module is uesd for dynamic testing, and the accuracy rate can reach to 100%. It is expected that the results of this experiment can solve the problem of insufficient crack-detected function in the domestic egg cleaning and classifying machine, and improve the economic value of the egg.

目錄
謝誌 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vii
表目錄 xii
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
第二章 文獻探討 3
2.1 共振檢測法之應用 3
2.2 共振檢測法應用於蛋殼裂痕檢測 4
2.3 濾波器之應用 7
第三章 理論分析 8
3.1 聲波基本原理 8
3.1.1 聲波的傳遞 8
3.1.2 開管與閉管原理 10
3.1.3 麥克風原理及機構 11
3.2 自然頻率 15
3.3 訊號處理 17
3.3.1 時域及頻域訊號 17
3.3.2 傅立葉轉換 18
3.4 濾波器 21
3.5 多階Butterworth濾波器設計 27
第四章 實驗材料與方法 31
4.1 實驗材料 31
4.2 實驗設備 32
4.3 實驗方法 37
4.3.1檢測點數之驗證 37
4.3.2 機台設計 39
4.3.3 第一次裂痕檢測模組動態測試 46
4.3.4 鴨蛋特徵頻率範圍測定 47
4.3.5 濾波器應用 48
4.3.6 第二次裂痕檢測模組動態測試 50
4.3.7 第三次裂痕檢測模組動態測試 51
第五章 結果與討論 53
5.1 檢測點數驗證結果 53
5.2 第一次裂痕檢測模組動態測試結果 62
5.3 鴨蛋特徵頻率範圍測定結果 63
5.4 第二次裂痕檢測模組動態測試結果 65
5.5 第三次裂痕檢測模組動態測試結果 66
第六章 結論與建議 80
6.1結論 80
6.2 建議 81
第七章 參考文獻 82

第七章 參考文獻
1.張勝善。1994。蛋品加工學。增訂版。P524-560。台北：華香園出版社。
2.鄭經偉、李蒼郎、沈明輝。1998。振動頻譜應用於鴨蛋外殼裂痕檢測之研究。農業機械學刊。
3.沈明輝。1999。鴨蛋振動頻譜分析.。碩士論文。台中：國立中興大學生土木工程學系。
4.白世煜。2000。利用聲波檢測鴨蛋外殼裂痕之研究。碩士論文。國立中興大學農業機械工程研究所。
5.林鼎凱。2001。蛋殼裂痕位置與檢測點關係之研究。碩士論文。國立中興大學農業機械工程研究所。
6.白世煜、紀偉民、李仲暐、鄭經偉。2001。利用聲波檢測鴨蛋外殼裂痕之研究。農林學報, Volume 50, Issue 4, Page（s） 79-102.
7.劉宇清。2005。蛋殼裂痕自動化檢測系統敲擊機構改良之研究。碩士論文。國立中興大學生物產業機電工程學系研究所。
8.周奕廷。2010。脈衝激振技術於檢測文蛤貝殼裂縫之研究。碩士論文。國立嘉義大學生物機電工程研究所。
9.陳怡琪。2014。非破壞性鴨蛋蛋殼厚度量測技術之基礎研究。碩士論文。國立中興大學生物產業機電工程研究所。
10.馮珮宣。2017。利用共振檢測法判別蛋殼裂痕並應用支撐向量機方法驗證之研究。碩士論文。國立中興大學生物產業機電工程學系研究所。
11.陳俊樺。2018。磁動式敲擊機構蛋殼裂痕檢測之研究。碩士論文。國立中興大學生物產業機電工程學系研究所。
12.Abbasion, S., Rafsanjani, A., Farshidianfar, A., & Irani, N. （2007）. Rolling Element Bearings Multi-Fault Classification Based on the Wavelet Denoising and Support Vector Machine. Mechanical Systems and Signal Processing, 21（7）, 2933-2945.
13.Bono, R. W., Sorensen, S. A., & Stultz, G. R..2010. New Methodology Applying Resonant Inspection for Quality Testing of Very Small Powder Metal Components.
14.Chang, C.-C., & Lin, C.-J. （2011）. Libsvm： A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology （TIST）, 2（3）, 27.
15.De Ketelaere, B., Coucke, P., & De Baerdemaeker, J. （2000）. Eggshell Crack Detection Based on Acoustic Resonance Frequency Analysis. Journal of Agricultural Engineering Research, 76（2）, 157-163.
16.Deng, X., Wang, Q., Chen, H., & Xie, H. （2010）. Eggshell Crack Detection Using a Wavelet-Based Support Vector Machine. Computers and electronics in agriculture, 70（1）, 135-143.
17.Garcia, G. V., R. Osegueda, and D. Meza.1999. “Damage Detection Comparison Between Damage Index Method and ARMA Method,” Proceeding of The 17th International Modal Analysis Conference, Vol. 1, pp. 93-598.
18.Haoqi, L., & Fei, R. （2014）. Resonant Ultrasound Spectroscopy Offers Unique Advantages as a Nondestructive Test Method. Advanced Materials & Processes, 172（10）, 17-19.
19.Ketelaere B.De,P. Coucke and J. De Baerdemaeker.2000.Eggshell crack detection based on acoustic resonance frequency analysis. Silcoe Research Institute.76：157-163
20.LU, W., ZHANG, C., LUO, H., DING, W., & WANG, L. （2015）. Micro-Cracks Detection of Eggshells Based on a Magnetostrictive Transducer.
21.Lai, C., Sun, X., Dasch, C., Harmon, G., & Jones, M. （2011）. Quantify Resonance Inspection with Finite Element-Based Modal Analyses. Journal of Vibration and Acoustics, 133（3）, 031004.
22.Rao, S. S., & Yap, F. F. （1995）. Mechanical Vibrations （Vol.4）： Addison-Wesley Reading.
23.Youden, W. J. 1950. Index for rating diagnostic tests. Cancer 3（1）： 32-35
24.Wang, J., Jiang, R., & Yu, Y. （2004）. Relationship between Dynamic Resonance Frequency and Egg Physical Properties. Food research international, 37（1）, 45-50.
25.Stultz, G. R., Bono, R. W., & Schiefer, M. I. （2005）. Fundamentals of Resonant Acoustic Method Ndt Advances in Powder Metallurgy and Particulate Materials 3.
26.Pei-Hsuan Feng（2019）. Eggshell Crack Detection and Egg Classification Using Resonance and Support Vector Machine Methods Citation . Applied Engineering in Agriculture. 35(1): 23-30. (doi: 10.13031/aea.12749