The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process
Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an i...
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| Autor*in: |
Duo, Aitor [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - London : Springer, 1985, 102(2019), 5-8 vom: 22. Jan., Seite 2133-2146 |
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| Übergeordnetes Werk: |
volume:102 ; year:2019 ; number:5-8 ; day:22 ; month:01 ; pages:2133-2146 |
| Links: |
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| DOI / URN: |
10.1007/s00170-019-03300-5 |
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| Katalog-ID: |
SPR001489208 |
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| 520 | |a Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics. | ||
| 650 | 4 | |a Tool wear |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Drilling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Tool condition monitoring |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Basagoiti, Rosa |4 aut | |
| 700 | 1 | |a Arrazola, Pedro J. |4 aut | |
| 700 | 1 | |a Aperribay, Javier |4 aut | |
| 700 | 1 | |a Cuesta, Mikel |4 aut | |
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10.1007/s00170-019-03300-5 doi (DE-627)SPR001489208 (SPR)s00170-019-03300-5-e DE-627 ger DE-627 rakwb eng Duo, Aitor verfasserin (orcid)0000-0003-1001-7845 aut The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics. Tool wear (dpeaa)DE-He213 Drilling (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Tool condition monitoring (dpeaa)DE-He213 Basagoiti, Rosa aut Arrazola, Pedro J. aut Aperribay, Javier aut Cuesta, Mikel aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 102(2019), 5-8 vom: 22. Jan., Seite 2133-2146 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:102 year:2019 number:5-8 day:22 month:01 pages:2133-2146 https://dx.doi.org/10.1007/s00170-019-03300-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 102 2019 5-8 22 01 2133-2146 |
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10.1007/s00170-019-03300-5 doi (DE-627)SPR001489208 (SPR)s00170-019-03300-5-e DE-627 ger DE-627 rakwb eng Duo, Aitor verfasserin (orcid)0000-0003-1001-7845 aut The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics. Tool wear (dpeaa)DE-He213 Drilling (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Tool condition monitoring (dpeaa)DE-He213 Basagoiti, Rosa aut Arrazola, Pedro J. aut Aperribay, Javier aut Cuesta, Mikel aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 102(2019), 5-8 vom: 22. Jan., Seite 2133-2146 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:102 year:2019 number:5-8 day:22 month:01 pages:2133-2146 https://dx.doi.org/10.1007/s00170-019-03300-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 102 2019 5-8 22 01 2133-2146 |
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10.1007/s00170-019-03300-5 doi (DE-627)SPR001489208 (SPR)s00170-019-03300-5-e DE-627 ger DE-627 rakwb eng Duo, Aitor verfasserin (orcid)0000-0003-1001-7845 aut The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics. Tool wear (dpeaa)DE-He213 Drilling (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Tool condition monitoring (dpeaa)DE-He213 Basagoiti, Rosa aut Arrazola, Pedro J. aut Aperribay, Javier aut Cuesta, Mikel aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 102(2019), 5-8 vom: 22. Jan., Seite 2133-2146 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:102 year:2019 number:5-8 day:22 month:01 pages:2133-2146 https://dx.doi.org/10.1007/s00170-019-03300-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 102 2019 5-8 22 01 2133-2146 |
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10.1007/s00170-019-03300-5 doi (DE-627)SPR001489208 (SPR)s00170-019-03300-5-e DE-627 ger DE-627 rakwb eng Duo, Aitor verfasserin (orcid)0000-0003-1001-7845 aut The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics. Tool wear (dpeaa)DE-He213 Drilling (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Tool condition monitoring (dpeaa)DE-He213 Basagoiti, Rosa aut Arrazola, Pedro J. aut Aperribay, Javier aut Cuesta, Mikel aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 102(2019), 5-8 vom: 22. Jan., Seite 2133-2146 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:102 year:2019 number:5-8 day:22 month:01 pages:2133-2146 https://dx.doi.org/10.1007/s00170-019-03300-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 102 2019 5-8 22 01 2133-2146 |
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10.1007/s00170-019-03300-5 doi (DE-627)SPR001489208 (SPR)s00170-019-03300-5-e DE-627 ger DE-627 rakwb eng Duo, Aitor verfasserin (orcid)0000-0003-1001-7845 aut The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics. Tool wear (dpeaa)DE-He213 Drilling (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Tool condition monitoring (dpeaa)DE-He213 Basagoiti, Rosa aut Arrazola, Pedro J. aut Aperribay, Javier aut Cuesta, Mikel aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 102(2019), 5-8 vom: 22. Jan., Seite 2133-2146 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:102 year:2019 number:5-8 day:22 month:01 pages:2133-2146 https://dx.doi.org/10.1007/s00170-019-03300-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 102 2019 5-8 22 01 2133-2146 |
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Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). 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Duo, Aitor misc Tool wear misc Drilling misc Machine learning misc Tool condition monitoring The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process |
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The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process Tool wear (dpeaa)DE-He213 Drilling (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Tool condition monitoring (dpeaa)DE-He213 |
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capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process |
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The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process |
| abstract |
Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstractGer |
Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. The experiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-established cutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feed force (external) and the z-axis motor torque (internal). The models created for the fulfilment of the second objective show a great capacity of prediction even when dealing with tools with different geometrical characteristics. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
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The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process |
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https://dx.doi.org/10.1007/s00170-019-03300-5 |
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Basagoiti, Rosa Arrazola, Pedro J. Aperribay, Javier Cuesta, Mikel |
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Basagoiti, Rosa Arrazola, Pedro J. Aperribay, Javier Cuesta, Mikel |
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10.1007/s00170-019-03300-5 |
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2024-07-03T22:54:52.677Z |
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| score |
7.3998747 |