Enhanced particle filter for tool wear prediction

Wang, Jinjiang; Wang, Peng; Gao, Robert X.

doi:10.1016/j.jmsy.2015.03.005

Cited by 124 publications

(40 citation statements)

References 29 publications

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“…Moreover, it is difficult to shorten the replacement interval without a reliable method of predicting the tool state because there is a huge risk of catastrophic damage to expensive material when a failure occurs. In terms of inference methods of tool replacement, recent studies have focused on powerful tools such as statistical data-driven [39], physics-based [40] and model-based approaches [41,42] and combinations of these approaches as reviewed in [43]. In this paper, the fundamental properties of form milling as well as a validation experiment for the proposed TCM system are presented with the help of statistical tests.…”

Section: Form Milling Of a Steam Turbine Rotor And A Strategy For Toomentioning

confidence: 99%

Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals

Uekita

Takaya

2016

Int J Adv Manuf Technol

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To ensure overall quality of a precision large-scale component, a tool condition monitoring (TCM) technique for multi-step form milling is presented. The form milling of fir tree slots for a steam turbine rotor is an appropriate example that requires a fine surface finish and high dimensional accuracy. Therefore, we propose a novel TCM system based on a multi-sensor fusion strategy which utilises the combination of spindle motor current and acoustic emission (AE) as well as adaptive thresholding for multiple manufacturing steps (roughing, semi-finishing and finishing). To investigate the tool deterioration process, tool longevity tests using a test piece are carried out for each step. With the aid of qualitative inspection, it is found that AE signals provide comprehensive tool state information regarding tool flank wear, crack propagation and severe adhesive wear. In addition, by intentionally adding a bundle of chips to the surface, bursts of AE of large amplitudes occur in finishing, which provides the possibility of discovering anomalous events related to surface quality. By careful consideration of such characteristics, provisional alert levels are determined using a two-dimensional diagram with respect to both sensors. The strategy is verified throughout the actual manufacturing processes of the rotors. The proposed TCM system shows not only an excellent ability to prevent catastrophic tool failure and surface irregularities in form milling but also acceptable expendability for various groove specifications.

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Section: Form Milling Of a Steam Turbine Rotor And A Strategy For Toomentioning

confidence: 99%

Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals

Uekita

Takaya

2016

Int J Adv Manuf Technol

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“…Its popularity has increased rapidly in the reliability field and various versions, such as auxiliary PF [ 1 ], regularized PF [ 2 ] and unscented PF [ 3 ], are proposed to improve the performance of the standard particle filtering algorithm. The standard PF algorithm and its variants have been implemented for diagnostics and prognostics in a wide range of applications, including life prediction of batteries and fuel cells [ 4 , 5 , 6 , 7 ], degradation assessment and prediction in gears and bearings [ 8 , 9 , 10 ], health monitoring and prognostics of gas turbines [ 11 ], machine tools [ 12 ], and pumps [ 13 , 14 ], and also, in damage estimation and prediction of composite materials [ 15 , 16 , 17 , 18 ]. More application examples can be found in a recent review paper on the PF algorithm by Jouin et al [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis

Rabiei

Droguett

Modarres

2018

Entropy

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A fully adaptive particle filtering algorithm is proposed in this paper which is capable of updating both state process models and measurement models separately and simultaneously. The approach is a significant step toward more realistic online monitoring or tracking damage. The majority of the existing methods for Bayes filtering are based on predefined and fixed state process and measurement models. Simultaneous estimation of both state and model parameters has gained attention in recent literature. Some works have been done on updating the state process model. However, not many studies exist regarding an update of the measurement model. In most of the real-world applications, the correlation between measurements and the hidden state of damage is not defined in advance and, therefore, presuming an offline fixed measurement model is not promising. The proposed approach is based on optimizing relative entropy or Kullback-Leibler divergence through a particle filtering algorithm. The proposed algorithm is successfully applied to a case study of online fatigue damage estimation in composite materials.

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“…Wang et al [238,239] developed a PF-based framework for precise RUL estimation, which was effective in a prognosis case study on tool wear and RUL prediction; this framework is shown in Fig. 18.…”

Section: Particle Filter-based Modelmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

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Enhanced particle filter for tool wear prediction

Cited by 124 publications

References 29 publications

Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals

Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals

Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis

Cloud-enabled prognosis for manufacturing

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