Detection of Tool Wear in Drilling CFRP/TC4 Stacks by Acoustic Emission

Leng, Sheng; Wang, Zhan; Min, Tao; Dai, Zhiqiang; Chen, Gang

doi:10.1007/s42417-019-00190-5

Cited by 30 publications

(9 citation statements)

References 15 publications

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“…As this is a non-contact measurement method, the contact of the sensor with the vibrating surface of the machine is eliminated. The research issues have included the investigation of acoustic signals in tool steel-hardening [14] and cold-drawn steel [15], cutting tool stability against impacts [16], tool wear and surface roughness in milling [17], tool wear in drilling by acoustic emission [18], detection of contact mechanism changes due to grinding tool wear by acoustic emission [19], and detection of suitable cutting operations [20]. The close relationship between the parasitic machine tool vibrations and the machined surface roughness is clearly shown in [21].…”

Section: Adaptive Control Methodsmentioning

confidence: 99%

Method of Using the Correlation between the Surface Roughness of Metallic Materials and the Sound Generated during the Controlled Machining Process

et al. 2022

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The article aims to use the generated sound as operational information needed for adaptive control of the metalworking process and early monitoring and diagnosis of the condition of the machined materials using a newly introduced surface roughness quality index due to the sound-controlled machining process. The object of the measurement was correlation between the sound intensity generated during cutting and the material parameters of the machined surface, i.e., the roughness of the machined surface and the degree of wear of the cutting tool. The roughness was measured during longitudinal turning of a steel billet with a P25 insert made of 12X18H10T steel and a T15K6 cutting insert made of a titanium, cobalt, and tungsten group alloy. The correlation between the sound and roughness of the machined surface was 0.93, whereas between the sound and wear of the cutting tool was 0.93. The correlation between sound and tool wear in the experiment with P25 and T15K6 cutting inserts and the correlation between sound and roughness is positive.

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Section: Adaptive Control Methodsmentioning

confidence: 99%

Method of Using the Correlation between the Surface Roughness of Metallic Materials and the Sound Generated during the Controlled Machining Process

et al. 2022

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“…The physics of one-shot drilling strategy when making holes in CFRP/Ti alloy stacks was studied in terms of the generated torque [ 21 , 33 ], thrust force [ 4 , 8 , 41 , 55 , 56 ], frictional heat [ 57 ], acoustic emission [ 58 ], wear rate [ 27 , 38 , 59 ], chip formation [ 13 , 22 , 43 , 60 ], and drilling temperature [ 11 , 19 , 40 , 55 , 56 ]. The last factor was reported as crucial for hole quality assurance.…”

Section: Introductionmentioning

confidence: 99%

Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks

et al. 2022

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Drilling of Carbon Fiber-Reinforced Plastic/Titanium alloy (CFRP/Ti) stacks represents one of the most widely used machining methods for making holes to fasten assemblies in civil aircraft. However, poor machinability of CFRP/Ti stacks in combination with the inhomogeneous behavior of CFRP and Ti alloy face manufacturing and scientific community with a problem of defining significant factors and conditions for ensuring hole quality in the CFRP/Ti alloy stacks. Herein, we investigate the effects of drilling parameters on drilling temperature and hole quality in CFRP/Ti alloy stacks by applying an artificial neuron network (ANN). We varied cutting speed, feed rate, and time delay factors according to the factorial design L9 Taguchi orthogonal array and measured the drilling temperature, hole diameter, and out of roundness by using a thermocouple and coordinate measuring machine methods for ANN analysis. The results show that the drilling temperature was sensitive to the effect of stack material layer, cutting speed, and time delay factors. The hole diameter was mainly affected by feed, stack material layer, and time delay, while out of roundness was influenced by the time delay, stack material layer, and cutting speed. Overall, ANN can be used for the identification of the drilling parameters–hole quality relationship.

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“…The recognition results were compared with those of an energy-based monitoring technique and found that the method proposed could determine the tool state more accurately for both normal wear and premature failure of micro-end mills. Leng et al [ 20 ] showed that the RMS value of the AE signals and the energy of the wavelet packet are correlated with the tool wear in drilling process. However, in many cases, the number of features extracted at MA was redundant and non-optimal [ 21 ], limiting the capability of predictors to estimate the tool wear.…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals

Chacon

Barrena

García

et al. 2021

Sensors

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There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%.

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Detection of Tool Wear in Drilling CFRP/TC4 Stacks by Acoustic Emission

Cited by 30 publications

References 15 publications

Method of Using the Correlation between the Surface Roughness of Metallic Materials and the Sound Generated during the Controlled Machining Process

Method of Using the Correlation between the Surface Roughness of Metallic Materials and the Sound Generated during the Controlled Machining Process

Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks

A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals

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