(Đồ án hcmute) research, design, and manufuacture of a quantitative weighing system for the mango sorting line

MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION GRADUATION PROJECT MACHATRONICS ENGINEERING TECHNOLOGY RESEARCH, DESIGN, AND MANUFACTURE OF A QUANTITATIVE WEIGHING SYSTEM FOR THE MANGO SORTING LINE LECTURER: ASSOC.PROF NGUYEN TRUONG THINH STUDENT: NGUYEN PHI HUNG NGUYEN TRUNG THANH SKL 010350 Ho Chi Minh City, February 2023 HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY OF MECHANICAL ENGINEERING DEPARTMENT OF MECHATRONICS ENGINEERING TECHNOLOGY BACHELOR THESIS RESEARCH, DESIGN, AND MANUFACTURE OF A QUANTITATIVE WEIGHING SYSTEM FOR THE MANGO SORTING LINE INSTRUCTOR: ASSOC PROF NGUYEN TRUONG THINH STUDENT’S NAME: NGUYEN PHI HUNG STUDENT’S ID NUMBER: 18146312 STUDENT’S NAME: NGUYEN TRUNG THANH STUDENT’S ID NUMBER: 19146391 Ho Chi Minh City, February 2023 HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY OF MECHANICAL ENGINEERING DEPARTMENT OF MECHATRONICS ENGINEERING TECHNOLOGY BACHELOR THESIS RESEARCH, DESIGN, AND MANUFACTURE OF A QUANTITATIVE WEIGHING SYSTEM FOR THE MANGO SORTING LINE INSTRUCTOR: ASSOC PROF NGUYEN TRUONG THINH STUDENT’S NAME: NGUYEN PHI HUNG STUDENT’S ID NUMBER: 18146312 STUDENT’S NAME: NGUYEN TRUNG THANH STUDENT’S ID NUMBER: 19146391 Ho Chi Minh City, February 2023 ACKNOWLEDGEMENT We would like to thank the lecturers of Ho Chi Minh City University of Technology and Education in general and the Department of Mechanical Engineering Electronics in particular, for imparting extremely valuable knowledge and experience, as well as dedicated help and guidance throughout the subjects, allowing us to have a solid foundation to apply to our work in the future, putting theory into practice We would like to express our deep gratitude to Mr Nguyen Truong Thinh, who enthusiastically guided and guided us during the project implementation Thank you, Mr Thinh, for always giving sincere comments and dedicated instructions so that we could complete our thesis in the best way Although we tried very hard in the process of implementing the project, because of limited experience and time, we could not avoid mistakes I look forward to more guidance from you I wish the lecturers good health so that they can continue to be enthusiastic and full of energy as they educate us on how to be competent and ethical engineers Finally, we would like to thank our family and friends for their help and support while studying and completing this graduation project Thank you so much! i TĨM TẮT Theo Bộ Nơng nghiệp Phát triển Nơng thơn, xồi loại trái phổ biến trồng nhiều nơi khắp Việt Nam Tồn vùng đồng sơng Cửu Long có 47.000 xồi loại, suất bình qn đạt từ 11-13 tấn/ha, sản lượng khoảng 567.732 tấn/ha Trong đó, có 1.789 xoài trồng theo tiêu chuẩn VietGAP GlobalGAP phục vụ xuất Chúng ta thấy, tiềm để Việt Nam mở rộng thị trường xuất xồi cịn lớn, kim ngạch xuất loại trái Việt Nam chiếm 1.51% tổng kim ngạch xuất xoài giới Tuy nhiên, cơng việc phân loại xồi theo tiêu chuẩn xuất cịn gặp nhiều khó khăn Một khó khăn cơng việc cân xồi Do đó, nhóm nghiên cứu nghiên cứu, thiết kế chế tạo hệ thống cân định lượng dây chuyền phân loại xồi Mục tiêu hệ thống cân định lượng hoàn toàn thay người làm cơng việc cân xồi Trong người nhân công phải làm việc nhàm chán, lặp lặp lại ngày không đạt kết mong muốn hệ thống cân định lượng giúp thực cơng việc cách hiệu Hệ thống phần dây chuyền phân loại xồi nên có thơng số kích thước phù hợp với băng tải cấp liệu băng tải phân loại để chúng ghép nối với cách dễ dàng Hệ thống gồm phận cố định phận chuyển động Khi hoạt động, động điện pha truyền động cho phận chuyển động xoay để di chuyển xoài đến vị trí cảm biết load cell, nơi mà xồi cân định lượng, chuyển xồi đến vị trí Chúng tơi lập trình thuật tốn để đo lường khối lượng xồi xác Q trình đo lường gồm bước: Ghi nhận tín hiệu tương tự từ cảm biến load cell, chuyển đổi liệu sang khối lượng thực (đơn vị: gram) xuất khối lượng xoài với mã kích thước theo tiêu chuẩn VietGap GlobalGap Những tín hiệu đầu dùng để phân loại xồi cơng đoạn dây chuyền phân loại xoài Hệ thống thực cân định lượng thực tế đạt kết khả quan ii ABSTRACT According to the Ministry of Agriculture and Rural Development, mango is one of the most popular and widely grown fruits throughout Vietnam The whole Mekong Delta has more than 47,000 hectares of mangoes of all kinds The average yield is 11–13 tons per hectare, and the productivity is about 567,732 tons per hectare Of these, 1,789 hectares of mangoes are grown according to VietGAP and GlobalGAP standards for export We can see that the potential for Vietnam to expand the mango export market is still very large because the export turnover of this fruit in Vietnam only accounts for 1.51% of the total mango export turnover in the world However, in the work of classifying mangoes according to export standards, there are still many difficulties One of those difficulties is the work of weighing mangoes Therefore, the research team has researched, designed, and manufactured a quantitative weighing system for the mango sorting line The main goal of the weighing system is to completely replace the humans doing the work of weighing mangoes While workers have to boring and repetitive work every day but not achieve the desired results, the weighing system will help us that job most effectively The system is part of the mango sorting line, so it has the right dimensions for the feeder and the sorter so they can be coupled together easily The system consists of fixed parts and moving parts When operating, the 3-phase electric motor drives the rotary actuator to move the mango to the load cell sensing position, where the mango is weighed and then moves the mango to the next position We programmed the algorithm to measure the mango mass accurately The measurement process consists of three steps: recording the analog signal from the load cell sensor, converting the data to the actual weight (unit: grams), and exporting the mango weight along with the size code according to VietGap and GlobalGap standards These output signals will be used to classify mangoes in the next stages of the mango sorting chain The system has been implemented quantitatively in real life and has achieved positive results iii CONTENTS ACKNOWLEDGEMENT i TÓM TẮT ii ABSTRACT iii CONTENTS iv LIST OF TABLES vii LIST OF FIGURES viii LIST OF ABBREVIATIONS x CHAPTER 1: OVERVIEW 1.1 Introduction 1.2 Literature review 1.2.1 Domestic research 1.2.2 International research 1.3 Reasons for choosing the research 1.4 Aims of the research 1.5 Research methods 1.6 Research limitations CHAPTER 2: FOUNDATIONAL THEORIES 2.1 Characteristics of Hoa Loc mango and Cat Chu mango 2.2 The Global GAP standard 2.3 Theory of mass and mass scales 10 2.4 Theory of quantitative weighing 10 2.4.1 Positive batching 11 2.4.2 Negative batching 11 2.4.3 Material overload 12 iv 2.4.4 Quantitative accumulation 12 2.5 Applying the quantitative method to the topic 13 2.6 Conclusion 14 CHAPTER 3: ANALYSIS AND DESIGN OF MECHANICAL SYSTEM 15 3.1 Introduction 15 3.2 Requirements for a model of the system 15 3.3 Mechanical design options 17 3.3.1 Precision balance 17 3.3.2 Compression weigh module 17 3.3.3 High precision load cell 18 3.3.4 Single point load cell 18 3.4 Selection design 19 3.5 Analysis of the configuration of the system 25 3.5.1 Main motion frame 25 3.5.2 Engine use plan 27 3.5.3 System Framework 29 3.6 Chain drive 30 3.7 Motor 34 3.8 Conclusion 36 CHAPTER 4: ELECTRICAL-CONTROL SYSTEM 38 4.1 Introduction 38 4.2 Structure of the electrical-control system 38 4.3 Sensor system 40 4.4 Electro-mechanical drive systems 46 4.4.1 Three phase induction motor 46 v 4.4.2 Variable frequency drive 47 4.4.3 Speed control of three phase induction motor using variable frequency drive control system 50 4.5 Security system 53 4.6 Programming 54 4.7 Conclusion 59 CHAPTER 5: EXPERIMENTAL AND RESULTS ASSESSMENT 60 5.1 Introduction 60 5.2 System overview 60 5.3 Setup of the experimental system 63 5.4 Evaluation of results 64 5.5 Conclusion 66 CHAPTER 6: CONCLUSION AND FUTURE WORK 67 6.1 Conclusion 67 6.2 Future work 67 REFERENCES 68 APPENDIX A: Measured weight result (Hoa Loc mango) 70 APPENDIX B: Measured weight result (Cat Chu mango) 73 vi LIST OF TABLES Table 2.1: The weight standards of mango Table 3.1: Specifications of three phase motor (60 Hz) 34 Table 3.2: Specifications of three phase motor (50 Hz) 35 Table 4.1: Conversion result 45 Table 4.2: Input / output address of systems 54 Table 5.1: Hardware parameters of the system 61 vii connected to the mango sorting line The load cell sensors are fixed in their correct position, and the wires are neatly installed, helping to limit collisions and damage These sensors collect data to calculate for the system Bolts, nuts, or welds connect other single fixings to the system These links are sure to ensure smooth operation Table 5.1: Hardware parameters of the system No Feature Quantity Parameter Mechanical Height: 1440 - 1500 mm Overview Length: 720 - 730 mm Width: 1240 - 1400 mm Weight 150 kg Electrical – control system PLC controller PLC SIMATIC S7-1200 CPU 1214C DC/DC/DC Load cell sensor 0-10V, 5000g Load cell amplifier Motor Three-phase induction motor Power 24V, 5A Gear (3) and chain (1) Ratio: 1:1 Transmission Gear (1) and chain (1) Ratio: 1:1 61 Replaceable parts Computer Windows 10 minimum, 4GB RAM Screen 12 - 22 inch Scope of mechanical structure Height: 15 cm Biggest mango Length: 18 cm Width: 12 cm Weight: 1000 g Height: cm Smallest mango Length: 10 cm Width: cm Weight: 200 g 62 5.3 Setup of the experimental system Figure 5.2: Experiment in the workshop To test the performance of the system, we conducted an experiment with 20 Hoa Loc mangoes and 28 Cat Chu mangoes (Figures 5.1 and 5.2) All mangoes are moved smoothly and steadily to the balance We have not seen a case of mangoes getting stuck or falling out of the system within the allowable size range of mangoes shown above As for moving the mango to the conveyor after weighing, as well as when it is brought in, the mango is delivered correctly, without errors Furthermore, we also have a computer to control and monitor the system The parameters that will be displayed on the monitoring screen are the actual weight of each mango and the weight classification code These parameters will be displayed immediately when the mango is removed from the load cell sensor The calculation time and display of results are relatively fast, meeting the desired productivity It is estimated that the capacity of the system is 5400 fruits per hour 63 5.4 Evaluation of results Figure 5.3: Chart of the measured weight result – Hoa Loc mango Figure 5.4: Chart of the measured weight result – Cat Chu mango From the chart, we can see that the largest mango has a mass of 460 grams, and the smallest one has a mass of 278 grams The measured values are quite similar to the actual 64 mango weight That is a good sign for the assessment For a more accurate assessment, we will analyze the measurement process and calculate the error Figure 5.5: Chart of the measured weight result – one mango First of all, we numbered each mango and weighed it with an electronic scale After that, we feed the mangoes to the conveyor and start the weighing system Mangoes are transferred to the system and balanced by the load cell sensor of the weighing system All of this information was gathered in order to compare it to previous results We plotted the figures, and the results are as follows (figure 5.3, 5.4 and 5.5) We calculate the accuracy of your measurements in percentages by finding the percent error This can be a helpful tool for explaining our results to people To find the percent error, subtract the results of your measurement from the accepted value and divide by the accepted value Then, multiply that figure by 100 The formula looks like this: 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝑒𝑟𝑟𝑜𝑟 = 𝑎𝑐𝑡𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 − 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑣𝑎𝑙𝑢𝑒 × 100 𝑎𝑐𝑡𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 (6) Accuracy measures the range of errors in data, and there are various types of errors we can measure to determine accuracy Two common errors that can be helpful to calculate when we're measuring accuracy are the absolute error and the relative error The absolute 65 error is the difference between a measured value and an actual value The absolute error can give you an indication of how close your measurement is to the actual value of the item Typically, a low absolute error is a measure of high accuracy, though the range of accuracy can vary based on the type of data you're collecting To find the absolute error, we subtract the actual value from the measured value The formula looks like this: 𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝑒𝑟𝑟𝑜𝑟 = 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑣𝑎𝑙𝑢𝑒 − 𝑎𝑐𝑡𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 (7) A relative error shows the ratio of the absolute error to the actual measurement This can be helpful when we want to know the size of the error in comparison with the actual value A lower relative error typically means we've produced an accurate measurement We can calculate the relative error by dividing the absolute error by the measured value The formula looks like this: 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑒𝑟𝑟𝑜𝑟 = 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝑒𝑟𝑟𝑜𝑟 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑣𝑎𝑙𝑢𝑒 (8) After calculating, we get an average absolute error about -2.67 (APPENDIX A and APPENDIX B) In addition, according to the long-term test, three mangoes can be weighed in seconds, and the yield can reach 5,400 fruits per hour If we assume each mango weighs approximately 500 grams, then the yield is equivalent to 2.7 tons per hour With this result, we are completely satisfied and will continue to increase the accuracy of the measurement 5.5 Conclusion Setting up the system to evaluate the weighing system is relatively easy and straightforward We just need to carefully number the mangoes as well as weigh them manually Regarding the evaluation results, we found that the system operates smoothly, with almost no errors at all stages Another argument is that the data result is also as expected 66 CHAPTER 6: CONCLUSION AND FUTURE WORK 6.1 Conclusion After six months of research, design, and manufacturing, we have successfully completed the mango sorting line weighing system From analyzing the available production lines, giving design options, manufacturing mechanical systems, installing equipment details, constructing electrical systems, building algorithms to calculate block values quality and programming on the control unit In each stage of implementation, we always think carefully, consider many situations, many options to optimize and ensure the stability of the system In addition, the selected options must be suitable to actual needs, that's why we always survey the requirements from the orders of mango orchards in the Mekong Delta, especially Dong Thap province, export standards VietGap, GlobalGap before embarking on design and construction Our system also ensures the standards of food safety and hygiene, ensuring the quality of mangoes 6.2 Future work The proposed development direction of the system is to optimize the weight of the mechanisms in the weighing system by using lighter materials such as synthetic resins to minimize the effects from the motion of the system is caused by the oscillations In the near future, we will move the system to a mango farm in Dong Thap province to weigh and classify mangoes according to the studied standards Moreover, we will collect the system's data for a long time We will directly monitor the operation of the machine within week to make sure there is no unexpected problem If there are any problems, we will immediately fix and improve Then we will improve the system to be able to weigh more types of fruit, not only mango but many similar fruits like mango 67 REFERENCES [1] Ngô Thế Hiên (2020) Báo cáo thống kê - Cổng thông tin điện tử Bộ Nông nghiệp Phát triển nông thôn Accessed 05/11/2022, from https://www.mard.gov.vn/Pages/ bao-cao-thong-ke.aspx# [2] Công ty Máy Thiết Bị Miền Nam (2017) Máy Cân Định Lượng Accessed 06/11/2021, from https://maymiennam.vn/danh-muc/may-dong-goi/may-can-dinhluong/ [3] Focus Technology Co., Ltd (2022) Quantitative Combination Small Fruit Weight Machine with Packaging Production Line Accessed 06/11/2022, from https:// rehoo00.en.made-in-china.com/product/fwImzOZdnQVa/China-QuantitativeCombination-Small-Fruit-Weight-Machine-with-Packaging-Production-Line.html [4] Zhengzhou First Industry Co., Ltd Fruit sorting weighing packing line Accessed 07/11/2021, from https://fruitprocess.com/en-us/Fruit%20Processing%20Line/fruitsorting-weighing-packing-line [5] WIPO IP Portal (25/09/2014) QUANTITATIVE WEIGHING SYSTEM AND QUANTITATIVE WEIGHING METHOD Accessed 07/11/2021 from https:// patentscope.wipo.int/search/en/detail.jsf?docId=WO2014148564 [6] GLOBALG.A.P FoodPLUS GmbH (2022), Document Center Accessed 15/11/2021 from https://www.globalgap.org/uk_en/documents/# [7] METTLER TOLEDO Group (09/2017), XPR Precision Balances [8] METTLER TOLEDO Group (09/2017), Compression Weigh Modules [9] METTLER TOLEDO Group (09/2017), High precision load cell [10] METTLER TOLEDO Group (09/2017), Aluminum Single Point Load Cells [11] SIMATIC, S7-1200 Programmable controller, System Manual [12] Omega Engineering, Inc (1996 - 2022), Load Cells & Force Sensors Accessed 12/11/2021 from https://www.omega.com/en-us/resources/load-cells [13] Omega Engineering, Inc (1996 - 2022), What is a Load Cell Amplifier? 68 [14] Quantum Controls Ltd (2008 - 2022), What is the operating principle of a 3-phase induction motor? Accessed 13/11/2021 from https://www.quantum-controls.co.uk/ insights/faqs/what-is-the-operating-principle-of-a-3-phase-induction-motor/ [15] VFDS (2022), What is a Variable Frequency Drive? Accessed 13/11/2021 from https://vfds.com/blog/what-is-a-vfd/ [16] VicRun (2019), VD120 Series Inverters [17] Qaisar Hayat (January 2020) Speed Control of Three Phase Induction Motor using Variable Frequency Drive Control System International Journal of Current Engineering and Technology 69 APPENDIX A: Measured weight result (Hoa Loc mango) No Measured weight Actual weight Percent error Absolute error Relative error 360 359 -0.279 0.003 343 343 0.000 0.000 341 343 0.583 -2 -0.006 337 337 0.000 0.000 337 337 0.000 0.000 392 393 0.254 -1 -0.003 360 359 -0.279 0.003 366 364 -0.549 0.005 353 359 1.671 -6 -0.017 10 368 365 -0.822 0.008 11 341 343 0.583 -2 -0.006 12 278 278 0.000 0.000 13 341 343 0.583 -2 -0.006 14 367 365 -0.548 0.005 15 335 337 0.593 -2 -0.006 16 315 314 -0.318 0.003 17 375 371 -1.078 0.011 18 391 393 0.509 -2 -0.005 19 343 343 0.000 0.000 20 335 337 0.593 -2 -0.006 21 368 365 -0.822 0.008 22 312 314 0.637 -2 -0.006 70 23 341 343 0.583 -2 -0.006 24 343 343 0.000 0.000 25 341 343 0.583 -2 -0.006 26 395 393 -0.509 0.005 27 458 459 0.218 -1 -0.002 28 325 324 -0.309 0.003 29 360 359 -0.279 0.003 30 368 365 -0.822 0.008 31 325 324 -0.309 0.003 32 340 338 -0.592 0.006 33 325 324 -0.309 0.003 34 368 365 -0.822 0.008 35 360 359 -0.279 0.003 36 368 365 -0.822 0.008 37 374 371 -0.809 0.008 38 360 359 -0.279 0.003 39 310 314 1.274 -4 -0.013 40 343 343 0.000 0.000 41 273 278 1.799 -5 -0.018 42 460 459 -0.218 0.002 43 323 324 0.309 -1 -0.003 44 367 365 -0.548 0.005 45 458 459 0.218 -1 -0.002 46 336 337 0.297 -1 -0.003 71 47 360 359 -0.279 0.003 48 314 314 0.000 0.000 49 337 337 0.000 0.000 50 337 337 0.000 0.000 51 368 365 -0.822 0.008 52 273 278 1.799 -5 -0.018 53 322 324 0.617 -2 -0.006 54 337 337 0.000 0.000 55 273 278 1.799 -5 -0.018 56 322 324 0.617 -2 -0.006 57 337 337 0.000 0.000 58 323 324 0.309 -1 -0.003 59 366 365 -0.274 0.003 60 392 393 0.254 -1 -0.003 61 335 337 0.593 -2 -0.006 62 312 314 0.637 -2 -0.006 63 341 343 0.583 -2 -0.006 64 368 365 -0.822 0.008 65 360 359 -0.279 0.003 66 374 371 -0.809 0.008 67 323 324 0.309 -1 -0.003 68 324 324 0.000 0.000 69 343 343 0.000 0.000 Average 0.057 -0.101 -0.001 72 APPENDIX B: Measured weight result (Cat Chu mango) No Measured weight Actual weight Percent error Absolute error Relative error 407 414 1.69 -7 -0.02 382 390 2.05 -8 -0.02 345 350 1.43 -5 -0.01 407 414 1.69 -7 -0.02 383 390 1.79 -7 -0.02 346 350 1.14 -4 -0.01 407 414 1.69 -7 -0.02 382 390 2.05 -8 -0.02 382 390 2.05 -8 -0.02 10 382 390 2.05 -8 -0.02 11 407 414 1.69 -7 -0.02 12 346 350 1.14 -4 -0.01 13 346 350 1.14 -4 -0.01 14 383 390 1.79 -7 -0.02 15 407 414 1.69 -7 -0.02 16 393 390 -0.77 0.01 17 353 350 -0.86 0.01 18 352 350 -0.57 0.01 19 346 350 1.14 -4 -0.01 20 407 414 1.69 -7 -0.02 21 345 350 1.43 -5 -0.01 22 407 414 1.69 -7 -0.02 73 23 382 390 2.05 -8 -0.02 24 413 414 0.24 -1 0.00 25 345 350 1.43 -5 -0.01 26 382 390 2.05 -8 -0.02 27 407 414 1.69 -7 -0.02 28 345 350 1.43 -5 -0.01 Average -5.25 -0.01 74