Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes

Penedo, F.; Haber, Rodolfo E.; Gajate, Agustín; Toro, Raúl M. del

doi:10.1109/tii.2012.2205699

Cited by 52 publications

(21 citation statements)

References 24 publications

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“…Hybrid Incremental Modeling (HIM) aims to provide an approximation of the behavior of a locally nonlinear system [16]. To that end, the strategy uses a global model that captures the basic or general behavior of the system and then superimposes a local model on it that captures the local behavior.…”

Section: Hybrid Incremental Modelingmentioning

confidence: 99%

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

Haber¹,

Toro²

2017

Preprint

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Abstract-A novel method based on a hybrid incremental modeling approach has been designed and applied to imbalance detection in ultra-high precision rotating machines. The model is obtained by a two-step iterative process that combines an overall model (least-squares fitting) with a local model (fuzzy k-nearestneighbour) to take advantage of their complementary capacities. Three normalization strategies of evaluating the effect on accuracy are analyzed. A comparative study demonstrates that the hybrid incremental model provides better error-based performance indices for detecting imbalance than a nonlinear regression model and an adaptive neural-fuzzy inference system model. The suitability of Mahanolobis normalization for hybrid incremental modeling is also demonstrated in this case study. The proposed strategy for imbalance detection is simple, fast, and non-intrusive, reducing the deterioration in the performance of ultra-high precision rotating machines due to vibrations.Index Terms-modeling, intelligent systems, imbalance detection, ultra-high precision rotating machine.

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Section: Hybrid Incremental Modelingmentioning

confidence: 99%

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

Haber¹,

Toro²

2017

Preprint

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“…All these three forces contain the information of tool wear while their sensitivities to the tool wear condition are different when applied in different methods. In [11], the thrust cutting force is found to be most sensitive when applying the singularity analysis, while Penedo et al [33] chose the twisting force Fz to monitor the tool wear by a hybrid incremental model, and in [32], all the forces in three directions were applied. In this work, since the cutting force fluctuates a lot in cutting the Inconel 718 material, its amplitude would not be applied.…”

Section: Tool Wear Process In Machiningmentioning

confidence: 99%

Multi-scale hybrid HMM for tool wear condition monitoring

Liao

Gao

Lü

et al. 2015

Int J Adv Manuf Technol

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In a machining system, accurate tool wear condition monitoring is paramount for guaranteeing the quality of the workpiece and tool life. Cutting force signal is the most commonly used signal to depict the tool wear variation during the machining process. In this paper, a novel approach for tool wear condition monitoring is proposed, which is based on the multi-scale hybrid hidden Markov model (HHMM) analysis of cutting force signal. The proposed approach captures the deeply mined information of tool wear states and holds an accurate tool wear value monitoring performance from both local and global analyses point of view. The local model deals with the wavelet coefficients of cutting force in different frequencies as a cross-twist Markov depended structure within instant time resolution, which reflects the tool wear state feature from frequency dimension. The global model depicts the long time dynamical degradation of tool wear condition combined with the local model as a composite structure. Experimental studies on CNC turning of nickel alloy, Inconel 718, show that the proposed HHMM approach is efficient in tool wear monitoring and outperforms the single hidden Markov model (HMM).

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“…Up to date, monitoring systems of industrial processes have demonstrated the importance and economic benefits of the application of network-based solutions and Artificial Intelligence techniques [1][2][3]. Monitoring of infrastructures is essential to build the next generation of predictive models.…”

Section: Introductionmentioning

confidence: 99%

Wireless Monitoring of Pavement Temperature Based on Low Cost Computing Platform

García

Haber

Godoy

et al. 2017

The 4th International Electronic Conference on Sensors and Applications

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Nowadays, the preservation, the maintenance, the rehabilitation and improvement of the road network are key issues. Many of the parameters that define the road surface conditions are influenced by various environmental factors, mainly temperature. Hence the importance of having databases enriched form real-time monitoring systems that enable the analysis and modeling of the properties of the road. The main goal of this work is the design and development of a road monitoring system for temperature measurement at different pavement depths, capable of transmitting the information to a moving vehicle. The practical realization required a modular device, of easy installation, low cost and with reduced energy consumption. The proposed monitoring system makes easy the auscultation procedure, improving the reliability of the measures collected which, in turn is the basement to estimate the useful life of the pavement. The results of the tests and validation of the proposed prototype in either static system with two types of pavement (asphalt and concrete), and in real driving situations, demonstrate the good performance and accuracy of the proposed monitoring system.

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Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes

Cited by 52 publications

References 24 publications

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

Multi-scale hybrid HMM for tool wear condition monitoring

Wireless Monitoring of Pavement Temperature Based on Low Cost Computing Platform

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