Model-based tool condition prognosis using power consumption and scarce surface roughness measurements

Moliner-Heredia, Rubén; Peñarrocha-Alós, Ignacio; Abellán-Nebot, José V.

doi:10.1016/j.jmsy.2021.09.001

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…Furthermore, it is assumed that tool wear has a direct impact on cutting forces and thus, the current consumption from spindle motor can be used as a sensing device for the proposed TCM system. This condition has been previously reported in the literature [14,29]. Cloud computing has recently attracted a lot of attention in manufacturing.…”

Section: Sensor Devices and Edge-computing Nodesupporting

confidence: 78%

A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment

et al. 2023

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Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0 is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with plug and play capabilities, minimum interruption time during setup and minimal experimental tests. In this paper, we propose a TCM system based on low-cost and non-invasive sensors that are plug and play devices, an IoT platform for fast deployment and a mobile app for receiving operator feedback. The system is based on a sensing node by Arduino Uno Wi-Fi that acts as an edge-computing node to extract a similarity index for tool wear classification; a machine learning node based on a BeagleBone Black board that builds the machine learning model using a Python script; and an IoT platform to provide the communication infrastructure and register all data for future analytics. Experimental results on a CNC lathe show that a logistic regression model applied on the machine learning node can provide a low-cost and straightforward solution with an accuracy of 88% in tool wear classification. The complete solution has a cost of EUR 170 and only a few hours are required for deployment. Practitioners in SMEs can find the proposed approach interesting since fast results can be obtained and more complex analysis could be easily incorporated while production continues using the operator’s feedback from the mobile app.

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Section: Sensor Devices and Edge-computing Nodesupporting

confidence: 78%

A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment

et al. 2023

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“…Han et al [58] performed regression and classification algorithms for machine state monitoring. Many more articles in this field can be found in the literature not only for Machine Learning [59][60][61] but also for Deep Learning [19,62].…”

Section: Machine Learning Approachmentioning

confidence: 99%

Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

Domínguez-Monferrer

Fernández-Pérez²,

Santos³

et al. 2022

Journal of Manufacturing Systems

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“…Meanwhile, the estimation accuracy of tool wear is improved by 46.74%, 39.57%, 41.51%, 38.68%, and 39.57%, respectively. For the actual production process, when the surface roughness of the machined surface exceeds production quality requirements, it is also necessary to replace the cutting tool in time [52]. Therefore, the proposed MTL model is more suitable for practical applications to monitor multiple in-process parameters simultaneously.…”

Section: Stl_psaementioning

confidence: 99%

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

Cheng

Jiao

Yan

et al. 2022

Sensors

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For data-driven intelligent manufacturing, many important in-process parameters should be estimated simultaneously to control the machining precision of the parts. However, as two of the most important in-process parameters, there is a lack of multi-task learning (MTL) model for simultaneous estimation of surface roughness and tool wear. To address the problem, a new MTL model with shared layers and two task-specific layers was proposed. A novel parallel-stacked auto-encoder (PSAE) network based on stacked denoising auto-encoder (SDAE) and stacked contractive auto-encoder (SCAE) was designed as the shared layers to learn deep features from cutting force signals. To enhance the performance of the MTL model, the scaled exponential linear unit (SELU) was introduced as the activation function of SDAE. Moreover, a dynamic weight averaging (DWA) strategy was implemented to dynamically adjust the learning rate of different tasks. Then, the time-domain features were extracted from raw cutting signals and low-frequency reconstructed wavelet packet coefficients. Frequency-domain features were extracted from the power spectrum obtained by the Fourier transform. After that, all features were combined as the input vectors of the proposed MTL model. Finally, surface roughness and tool wear were simultaneously predicted by the trained MTL model. To verify the superiority and effectiveness of the proposed MTL model, nickel-based superalloy Haynes 230 was machined under different cutting parameter combinations and tool wear levels. Some other intelligent algorithms were also implemented to predict surface roughness and tool wear. The results showed that compared with the support vector regression (SVR), kernel extreme learning machine (KELM), MTL with SDAE (MTL_SDAE), MTL with SCAE (MTL_SCAE), and single-task learning with PSAE (STL_PSAE), the estimation accuracy of surface roughness was improved by 30.82%, 16.67%, 14.06%, 26.17%, and 16.67%, respectively. Meanwhile, the prediction accuracy of tool wear was improved by 46.74%, 39.57%, 41.51%, 38.68%, and 39.57%, respectively. For practical engineering application, the dimensional deviation and surface quality of the machined parts can be controlled through the established MTL model.

show abstract

Model-based tool condition prognosis using power consumption and scarce surface roughness measurements

Cited by 12 publications

References 37 publications

A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment

A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment

Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

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