Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators
Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. This article applies an evolutionary algorithm...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10423805/ |
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| author | Hoa Thi Nguyen Roland Olsson Oystein Haugen |
| author_facet | Hoa Thi Nguyen Roland Olsson Oystein Haugen |
| author_sort | Hoa Thi Nguyen |
| collection | DOAJ |
| description | Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. This article applies an evolutionary algorithm to synthesize recurrent neurons optimized for specific datasets. This adds another layer to the AutoML framework, targeting the internal structure of neurons. We developed an imitation learning control system for an industry CNC machine to enhance operators' productivity. We specifically examine two recorded operator actions: adjusting the engagement rates for linear feed rate and spindle velocity. We compare the performance of our evolved neurons with support vector machine and four well-established neural network models commonly used for time series data: simple recurrent neural networks, long-short-term-memory, independently recurrent neural networks, and transformers. The results demonstrate that the neurons evolved via the evolutionary approach exhibit lower syntactic complexity than LSTMs and achieve lower error rates than other networks. They yield error rates 270% lower for the first operation action, while the error rates are 20% lower for the second action. We also show that our evolutionary algorithm is capable of creating skip-connections and gating mechanisms adapted to the specific characteristics of our dataset. |
| format | Article |
| id | doaj-art-54dc882de70b4b6b9d2af31b0fb21b09 |
| institution | Kabale University |
| issn | 2644-1284 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj-art-54dc882de70b4b6b9d2af31b0fb21b092025-01-17T00:00:52ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842024-01-0159110810.1109/OJIES.2024.336350010423805Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine OperatorsHoa Thi Nguyen0https://orcid.org/0009-0009-5202-2563Roland Olsson1https://orcid.org/0000-0002-8345-2613Oystein Haugen2https://orcid.org/0000-0002-0567-769XFaculty of Computer Sciences, Engineering and Economics, Høskolen I Østfold Universit, Halden, NorwayFaculty of Computer Sciences, Engineering and Economics, Høskolen I Østfold Universit, Halden, NorwayFaculty of Computer Sciences, Engineering and Economics, Høskolen I Østfold Universit, Halden, NorwayAnalyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. This article applies an evolutionary algorithm to synthesize recurrent neurons optimized for specific datasets. This adds another layer to the AutoML framework, targeting the internal structure of neurons. We developed an imitation learning control system for an industry CNC machine to enhance operators' productivity. We specifically examine two recorded operator actions: adjusting the engagement rates for linear feed rate and spindle velocity. We compare the performance of our evolved neurons with support vector machine and four well-established neural network models commonly used for time series data: simple recurrent neural networks, long-short-term-memory, independently recurrent neural networks, and transformers. The results demonstrate that the neurons evolved via the evolutionary approach exhibit lower syntactic complexity than LSTMs and achieve lower error rates than other networks. They yield error rates 270% lower for the first operation action, while the error rates are 20% lower for the second action. We also show that our evolutionary algorithm is capable of creating skip-connections and gating mechanisms adapted to the specific characteristics of our dataset.https://ieeexplore.ieee.org/document/10423805/CNC machineevolutionary algorithmimitation learningrecurrent neural networkssmart manufacturingtime series analysis |
| spellingShingle | Hoa Thi Nguyen Roland Olsson Oystein Haugen Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators IEEE Open Journal of the Industrial Electronics Society CNC machine evolutionary algorithm imitation learning recurrent neural networks smart manufacturing time series analysis |
| title | Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators |
| title_full | Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators |
| title_fullStr | Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators |
| title_full_unstemmed | Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators |
| title_short | Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators |
| title_sort | automatic synthesis of recurrent neurons for imitation learning from cnc machine operators |
| topic | CNC machine evolutionary algorithm imitation learning recurrent neural networks smart manufacturing time series analysis |
| url | https://ieeexplore.ieee.org/document/10423805/ |
| work_keys_str_mv | AT hoathinguyen automaticsynthesisofrecurrentneuronsforimitationlearningfromcncmachineoperators AT rolandolsson automaticsynthesisofrecurrentneuronsforimitationlearningfromcncmachineoperators AT oysteinhaugen automaticsynthesisofrecurrentneuronsforimitationlearningfromcncmachineoperators |