The multi sensor-based machining signal fusion to compare the relative efficacy of machine learning based tool wear models
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This dataset contains a force dynamometer, accelerometer sensor, acoustic emission sensor, and tool wear values for different milling conditions. For each condition, 12 experiments were conducted. Tool 1 (T1) to Tool 4 (T4) were used to develop the machine learning models and is validated with Tool 5 (T5) to Tool 8 (T8) respectively. This dataset contains raw data taken from each sensor output for each experimental cut.From this dataset, the relative efficacy of machine learning-based tool wear models was developed. Also, two sensor combination was used to compare the sensor effectiveness in tool wear prediction' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Multi sensor data fusion, Condition monitoring, Machine Learning, Tool wear prediction"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This dataset contains a force dynamometer, accelerometer sensor, acoustic emission sensor, and tool wear values for different milling conditions. For each condition, 12 experiments were conducted. Tool 1 (T1) to Tool 4 (T4) were used to develop the machine learning models and is validated with Tool 5 (T5) to Tool 8 (T8) respectively. This dataset contains raw data taken from each sensor output for each experimental cut.From this dataset, the relative efficacy of machine learning-based tool wear models was developed. Also, two sensor combination was used to compare the sensor effectiveness in tool wear prediction' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This dataset contains a force dynamometer, accelerometer sensor, acoustic emission sensor, and tool wear values for different milling conditions. For each condition, 12 experiments were conducted. Tool 1 (T1) to Tool 4 (T4) were used to develop the machine learning models and is validated with Tool 5 (T5) to Tool 8 (T8) respectively. This dataset contains raw data taken from each sensor output for each experimental cut.From this dataset, the relative efficacy of machine learning-based tool wear models was developed. Also, two sensor combination was used to compare the sensor effectiveness in tool wear prediction
Multi sensor data fusion, Condition monitoring, Machine Learning, Tool wear prediction