- freely available
J. Manuf. Mater. Process. 2018, 2(4), 72; https://doi.org/10.3390/jmmp2040072
2. Related Work
2.1. Tool Wear Monitoring
2.2. Deep Learning in Condition Monitoring
3. Cutting Force Measurement during Dry Machining
3.1. Condition Monitoring Infrastructure
3.2. Computing Architecture
4. Data Engineering
4.1. Data Acquisition Calibration
4.2. Data Characterisation
- Velocity: The sensory data were generated at high frequency. Hence, they needed to be collected and stored in real time, for batch and stream processing, before they could be used in an effective way while keeping integrity, resilience, persistence and security at the required levels.
- Volume: The data generated from the tool passes can be seen as the result of a complex and highly process-oriented operation. Hence, this resulted in a high-frequency, nonlinear, vast quantity generation of large datasets that requires a fast and efficient management approach.
- Veracity: The sensory data were captured during the entire machining process. This includes force signals when the cutter is not touching the workpiece. Hence, the data contain different levels of trustworthiness, which had to be identified and treated at different application levels in order to ensure the correct harvesting and extraction of knowledge for gaining insight and learning.
- Variety: The sensory data, as well as the microscopic images define a plethora of data types categorised into structured data, i.e., information with a high degree of organisation, and unstructured data, i.e., information that has neither a pre-defined data model or organisation.
4.3. Data Cleansing
5. Online Tool Wear Classification
5.1. Model Building
5.1.1. Signals Collection
5.1.2. Tool Wear Calculation
5.1.3. Model Creation
5.2. Model Training and Testing
5.2.1. Model Training
5.2.2. Model Testing
5.3. Online Validation
6. Conclusions and Further Work
- An online tool wear classification system built in terms of a monitoring infrastructure, dedicated to performing dry milling on steel while capturing force signals in real time, and a computing architecture, assembled for the real-time assessment of the flank wear based on deep learning.
- An approach based on a very simple mathematical model that converts raw force signals into two-dimensional images (the GASF component) that, when used as input to an off-the-shelf CNN architecture, exploits internal spatial structures encoding edge devastation for reporting tool wear progression during dry machining on steel.
- An end-to-end smart system that exploits big data for the development of online indirect tool condition monitoring that is free of feature engineering, a signal analyst or image processing expertise. An offline test has successfully reported an accuracy of followed by an online validation that classifies force signals acquired in real time from a new milling process.
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|CNN Level||Type||Input Size||Kernel Size||Stride||Output Size||Filters|
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