Tool wear estimation using an analytic fuzzy classifier and support vector machines

Brezak, Danko; Majetić, Dubravko; Udiljak, Toma; Kasač, Josip

doi:10.1007/s10845-010-0436-x

Cited by 52 publications

(13 citation statements)

References 38 publications

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“…It is defined by the PHM community as the estimation of the Remaining Useful Life (RUL) of physical systems based on their health state and their future operating conditions. The RUL estimation can be done by using three main approaches [4]: data-driven prognostics [39], model-based prognostics [20] and hybrid prognostics [9].…”

Section: Introductionmentioning

confidence: 99%

Post-prognostics decision making in distributed MEMS-based systems

et al. 2017

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In this paper, the problem of using prognostics information of Micro-Electro-Mechanical Systems (MEMS) for post-prognostics decision in distributed MEMS-based systems is addressed. A strategy of postprognostics decision is proposed and then implemented in a distributed MEMS-based conveying surface. The surface is designed to convey fragile and tiny microobjects. The purpose is to use the prognostics results of the used MEMS in the form of Remaining Useful Life (RUL) to maintain as long as possible a good performance of the conveying surface. For that, a distributed algorithm for distributed decision making in dynamic conditions is proposed. In addition, a simulator to simulate the decision in the targeted system is developed. Simulation results show the importance of the postprognostics decision to optimize the utilization of the system and improve its performance.

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Section: Introductionmentioning

confidence: 99%

Post-prognostics decision making in distributed MEMS-based systems

et al. 2017

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“…The output block determines output process variables (W B tool wear [18], T tool life, C P operation cost, F resultant cutting force).…”

Section: Structure Of Adaptive Control Systemmentioning

confidence: 99%

Surface Roughness Control Simulation of Turning Processes

Čuš¹,

Župerl²

2015

SV-JME

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The aim of this paper is to present a surface roughness control in turning with an associated simulation block diagram. The objective of the new model based controller is to assure the desired surface roughness by adjusting the machining parameters and maintaining a constant cutting force. It modifies the feed rate on-line to keep the surface roughness constant and to make machining more efficient. The control model was developed based on simplified models of the turning process and the feed drive servo-system. The experiments were performed to find the correlation between surface roughness and cutting forces in turning and to provide functional correlation with the controllable factors. Simulation setup and results are presented to demonstrate the efficiency of the proposed control model. In terms of surface roughness fluctuations and cutting efficiency, the suggested control model is much better than a conventional CNC controller alone. Integrating the developed control model with the CNC machine controller significantly improves the quality of machined components.

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“…Tool wear in different chip removal methods has also been under investigation relatively often [14] . The methods of this include acceleration measurements [15,16,17] , acoustic emission measurements [18] and neural networks and fuzzy logic [19,20,21] . In a sense, this research is quite close to the topic of this paper, because it is very clear that tool wear has an effect on the stress of the machinery.…”

Section: Stress Evaluation | Featurementioning

confidence: 99%

Applying weighted lp norms and MIT measurement indices in the stress evaluation of a steel cutter

Karioja¹,

Lahdelma²

2015

Int J Cond Monitor

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The dimensionless MIT index is a new method of stress evaluation of guillotine-type steel cutters. The S surface is utilised in order to determine suitable features for the index. The S surfaces are obtained using real-order derivatives and weighted l p norms. In this paper, it is shown that these methods can provide easily understandable information on the levels of stress imposed on the cutter. The measurements were performed at Ruukki Metals in Raahe, Finland.

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Tool wear estimation using an analytic fuzzy classifier and support vector machines

Cited by 52 publications

References 38 publications

Post-prognostics decision making in distributed MEMS-based systems

Post-prognostics decision making in distributed MEMS-based systems

Surface Roughness Control Simulation of Turning Processes

Applying weighted <I>l</I><I><SUB>p</SUB></I> norms and MIT measurement indices in the stress evaluation of a steel cutter

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