Use of nearest neighbors (k-NN) algorithm in tool condition identification in the case of drilling in melamine faced particleboard
The purpose of this study was to develop an automatic indirect (non-invasive) system to identify the condition of drill bits on the basis of the measurement of feed force, cutting torque, jig vibrations, acoustic emission and noise which were all generated during machining. The k-nearest neighbors a...
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| Published in | Maderas Vol. 22; no. ahead; pp. 189 - 196 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Universidad del Bío-Bío
01.04.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0718-221X 0717-3644 0718-221X |
| DOI | 10.4067/S0718-221X2020005000205 |
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| Abstract | The purpose of this study was to develop an automatic indirect (non-invasive) system to identify the condition of drill bits on the basis of the measurement of feed force, cutting torque, jig vibrations, acoustic emission and noise which were all generated during machining. The k-nearest neighbors algorithm classifier (k-NN) was used. All data analyses were carried out in MATLAB (MathWorks - USA) environment. It was assumed that the most simple (but sufficiently effective in practice) tool condition identification system should be able to recognize (in an automatic way) three different states of the tool, which were conventionally defined as “Green” (tool can still be used), “Red” (tool change is necessary) and “Yellow” (intermediate, warning state). The overall accuracy of classification was 76 % what can be considered a satisfactory result at this stage of studies. |
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| AbstractList | The purpose of this study was to develop an automatic indirect (non-invasive) system to identify the condition of drill bits on the basis of the measurement of feed force, cutting torque, jig vibrations, acoustic emission and noise which were all generated during machining. The k-nearest neighbors algorithm classifier (k-NN) was used. All data analyses were carried out in MATLAB (MathWorks – USA) environment. It was assumed that the most simple (but sufficiently effective in practice) tool condition identification system should be able to recognize (in an automatic way) 3 different states of the tool, which were conventionally defined as “Green” (tool can still be used), “Red” (tool change is necessary) and “Yellow” (intermediate, warning state). The overall accuracy of classification was 76 % what can be considered a satisfactory result at this stage of studies. The purpose of this study was to develop an automatic indirect (non-invasive) system to identify the condition of drill bits on the basis of the measurement of feed force, cutting torque, jig vibrations, acoustic emission and noise which were all generated during machining. The k-nearest neighbors algorithm classifier (k-NN) was used. All data analyses were carried out in MATLAB (MathWorks - USA) environment. It was assumed that the most simple (but sufficiently effective in practice) tool condition identification system should be able to recognize (in an automatic way) three different states of the tool, which were conventionally defined as “Green” (tool can still be used), “Red” (tool change is necessary) and “Yellow” (intermediate, warning state). The overall accuracy of classification was 76 % what can be considered a satisfactory result at this stage of studies. |
| Author | Jegorowa, Albina Górski, Jarostaw Kruk, Michat Kurek, Jarostaw |
| AuthorAffiliation | Warsaw University of Life Sciences |
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| Author_xml | – sequence: 1 givenname: Albina surname: Jegorowa fullname: Jegorowa, Albina – sequence: 2 givenname: Jarostaw surname: Górski fullname: Górski, Jarostaw – sequence: 3 givenname: Jarostaw surname: Kurek fullname: Kurek, Jarostaw – sequence: 4 givenname: Michat surname: Kruk fullname: Kruk, Michat |
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| References | Fix, E.; Hodges, J.L 1951 Bergweiler, S 2016; 9 Lemaster, R.L.; Lu, L.; Jackson, S 2000; 50 Górski, J.; Szymanowski, K.; Podziewski, P.; Śmietańska, K.; Czarniak, P.; Cyrankowski, M 2019; 14 Szwajka, K.; Górski, J 2006; 48 Iskra, P.; Hernandez, R.E 2012; 46 Schwab, K 2017 Wagner, T.; Herrmann, C.; Thiede, S 2017; 63 Jegorowa, A.; Górski, J.; Kurek, J.; Kruk, M 2019; 77 Swiderski, B.; Kurek, J.; Osowski, S.; Kruk, M.; Jegorowa, A 2017 Hall, P.; Park, B.U.; Samworth, R.J 2008; 36 Wilkowski, J.; Górski, J 2011; 56 |
| References_xml | – volume: 50 start-page: 31 issue: 7/8 year: 2000 end-page: 38 article-title: The use of process monitoring techniques on a CNC wood router publication-title: Sensor selection. Forest Prod J – volume: 50 start-page: 59 issue: 9 year: 2000 end-page: 64 article-title: The use of process monitoring techniques on a CNC wood router. Part 2. Use of vibration accelerometer to monitor tool wear and workpiece quality publication-title: Forest Prod J – volume: 46 start-page: 115 issue: 1-3 year: 2012 end-page: 128 article-title: Toward a process monitoring of CNC wood router. Sensor selection and surface roughness prediction publication-title: Wood Sci Technol – volume: 36 start-page: 2135 issue: 5 year: 2008 end-page: 2152 article-title: Choice of neighbor order in nearest - neighbor classification publication-title: Ann Stat – volume: 63 start-page: 125 year: 2017 end-page: 131 article-title: Industry 4.0 impacts on lean production system publication-title: Procedia CIRP – year: 2017 publication-title: The Fourth Industrial Revolution – volume: 14 start-page: 3379 issue: 2 year: 2019 end-page: 3388 article-title: Use of cutting force and vibro-acoustic signals in tool wear monitoring based on multiple regression technique for compreg milling publication-title: Bioresources – year: 2017 publication-title: Diagnostic system of drill condition in laminated chipboard drilling proces – volume: 77 start-page: 957 year: 2019 end-page: 959 article-title: Initial study on the use of support vector machine (SVM) in tool condition monitoring in chipboard drilling publication-title: Eur J Wood Wood Prod – year: 1951 publication-title: Discriminatory analysis, nonparametric discrimination, consistency properties – volume: 48 start-page: 1205 year: 2006 end-page: 1209 article-title: Evaluation tool condition of milling wood on the basis of vibration signal publication-title: J Phys: Conf Ser – volume: 56 start-page: 57 issue: 1 year: 2011 end-page: 66 article-title: Vibro-acoustic signals as a source of information about tool wear during laminated chipboard milling publication-title: Wood Res-Slovakia – volume: 9 start-page: 91 issue: 1-2 year: 2016 end-page: 101 article-title: Smart Factory Systems - Fostering Cloud-based Manufacturing based on Self-Monitoring Cyber-Physical Systems publication-title: International Journal on Advances in Systems and Measurements |
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| Title | Use of nearest neighbors (k-NN) algorithm in tool condition identification in the case of drilling in melamine faced particleboard |
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