Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

Azmi, Azri

doi:10.1016/j.advengsoft.2014.12.010

Cited by 77 publications

(28 citation statements)

References 25 publications

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“…During milling processes, tool wear increases surface roughness and leads to a corresponding increase in cutting force. A number of studies [25][26][27][28] have demonstrated that the cutting force is very sensitive to changes in tool condition and can, therefore, accurately estimate the state of the tool. Wang et al [29] determined that the cutting force signal is the most stable and reliable signal among the commonly employed sensor signals closely related to tool wear.…”

Section: Introductionmentioning

confidence: 99%

Influence of machining parameters on the polymer concrete milling process

Kępczak

Zgórniak

Lajmert

et al. 2020

Int J Adv Manuf Technol

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The paper examines the wear of end mill cutters, the parameters of a workpiece's surface layer and the values of cutting forces during the machining of polymer concrete, which is one of the most difficult-to-cut materials. Increased abrasive wear of the active cutting edges is a result of the heterogeneous structure of this material, and it has a negative effect on the cutting process, the durability of the cutting tools as well as the roughness of the workpiece. It is very important to determine the machining parameters for this material from a tool durability and quality of machining point of view. The research was divided into two stages. In the first stage, the appropriate machining parameters were determined based on the measurement of the components of the cutting force. In the second stage, durability tests of four end mill cutters were carried out. During the tests, the component cutting forces, tool wear and surface roughness were measured. The highest tool durability was recorded for the monolithic cemented carbide cutter with coating NC Mill G9F42120N 4F. Tools made of high-speed steel should not be used for machining polymer concrete, as they exhibited the highest intensity of tool wear. The rapid loss of machinability of these tools leads to a rapid increase in cutting force and the roughness of the machined surface. From an economic point of view, cemented carbide end mill cutters with coatings seem to be the appropriate choice for machining polymer concrete.

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Section: Introductionmentioning

confidence: 99%

Influence of machining parameters on the polymer concrete milling process

Kępczak

Zgórniak

Lajmert

et al. 2020

Int J Adv Manuf Technol

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show abstract

“…There are two methods normally employed for tool wear sensing: Direct and Indirect methods. Direct method involves measuring the actual wear using optical devices such as radioactive analysis on the tool which is generally a quite difficult process [2]. The direct method is capable of providing higher accuracy only at certain conditions and has not been yet proven to be useful economically as well as technologically.…”

Section: Introductionmentioning

confidence: 99%

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

Shantisagar¹,

Jegadeeshwaran²,

Gandhi³

et al. 2019

Structural Durability &Amp; Health Monitoring

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The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools. This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach. A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe, where the condition of tool is monitored using vibration characteristics. The vibration signals for conditions such as heathy, damaged, thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system. The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques. The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm. The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis. The decision tree model produced the classification accuracy as 94.78% with five selected features. The developed fuzzy model produced the classification accuracy as 94.02% with five membership functions. Hence, the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

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“…[18]. Recently, the same research group has applied neuro-fuzzy modeling approaches for machining of GFRP composites [19]. Thus, considering the importance of modeling techniques in predicting the performance characteristics of µEDM, a model is developed to correlate the processing conditions with the MRR as well as the hardness of the recast layer.…”

Section: Analysis Of Materials Removal Rate (Mrr)mentioning

confidence: 99%

Modeling the Effects of Electrical and Non-Electrical Parameters on the Material Removal and Surface Integrity in Case of μEDM of a Non-Conductive Ceramic Material using a Combined Fuzzy-AOM Approach

Puthumana¹

2016

IJAST

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Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

Cited by 77 publications

References 25 publications

Influence of machining parameters on the polymer concrete milling process

Influence of machining parameters on the polymer concrete milling process

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

Modeling the Effects of Electrical and Non-Electrical Parameters on the Material Removal and Surface Integrity in Case of μEDM of a Non-Conductive Ceramic Material using a Combined Fuzzy-AOM Approach

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