**3. Conclusions**

**Figure 14.** Exit diameter error, tool flank wear, and exit delamination (6000 rpm–0.20 mm/rev).

with the corresponding flank wear value and the exit delamination factor.

on-line decision making on drill bit replacement need.

80 Characterizations of Some Composite Materials

by the exit delamination factor.

need, which is essential for drilling automation.

accurate, with an RMSE <4e-3, showing reliable correlations between fused signal features and tool wear level. The reliable predictions of tool wear development can be used to support

Moreover, the prediction of tool wear can be functionally utilized to forecast the quality of the drilled holes. The latter was assessed considering dimensional accuracy and entry/exit delamination, which both have an effect on the performance of the CFRP assembly. Dimensional accuracy was measured with reference to hole diameter error, that is, the difference between actual and nominal hole diameter divided by nominal hole diameter [30]. The entry/exit delamination was assessed with reference to the delamination factor, Fd, that is, the ratio between the diameter of the circle encompassing the damaged area and the nominal diameter of the hole [25].

The drilled hole quality evaluations were utilized to set up a criterion for tool replacement need, which is required when the tool wear is responsible for a drilled hole, the quality of which is no longer acceptable. As the lower limit of the tolerance range corresponds to the nominal diameter of the hole, any negative hole diameter error is unacceptable. For each drilling condition, the occurrence of negative hole diameter errors was detected and associated

Under all drilling conditions, the hole diameter error became negative when the flank wear, VB, reached the typical value of 0.04 mm (see **Figure 14**). The latter can be used as a threshold to determine the need for tool change due to an undersized hole diameter. For all drilling conditions, the exit delamination factor grew with increasing number of holes and reached a value between 1.3 and 1.4 when the flank wear reached 0.04 mm. This suggests that the flank wear threshold could also be associated with the second hole quality parameter represented

Hence, a correspondence between hole diameter error and exit delamination factor with tool wear level was observed. As a result, via on-line prediction of tool wear during drilling, taking into account the identified flank wear threshold, the cognitive sensor monitoring paradigm can provide diagnosis and prognosis services to support decision making on tool replacement This chapter provided an overview of the main challenges related to drilling of fiberreinforced plastic composite materials which are extensively employed in the aeronautical industry. Rapid tool wear is generated due to the abrasiveness of the reinforcing fibers, and different types of damages affecting material integrity and surface quality, with particular reference to delamination damage generation, are often produced by drilling.

With reference to aeronautical industry applications, where the assembly of CFRP components requires "one-shot" drilling processes so as to allow for easier subsequent riveting avoiding misalignment issues, drilling of CFRP/CFRP stacks made of two superimposed laminates was investigated.

Based on a wide experimental drilling campaign, the case studies analyzed the influence of drilling parameters, tool type and geometry on tool wear development, hole quality and surface integrity, and the opportunity to implement advanced sensor monitoring procedures for tool condition monitoring based on the acquisition and processing of thrust force and torque signals.

Diverse multiple sensor process monitoring procedures were implemented in the drilling of CFRP/CFRP stacks for the assembly of aircraft components, with the aim to support on-line decision making on tool replacement time through cognitive tool wear estimation and hole quality assessment. The monitoring procedures were based on the acquisition and processing of thrust force, torque, and acoustic emission sensor signals during the experimental drilling tests.

With the purpose to explore the complex frequency content of the thrust force and torque sensor signals acquired in the multidirectional CFRP/CFRP stack drilling experimental tests, advanced signal processing was also carried out in the frequency domain.

The sensor signal processing techniques, comprising signal conditioning, feature extraction in the time and frequency domain and data fusion, were implemented to construct sensor fusion feature pattern vectors—made of sensor signal features coming from sensors of different nature—with the aim to find correlations with tool state via artificial neural networkbased pattern recognition paradigms.

The ANN performance results achieved in the case studies indicated that, for all CFRP/CFRP stack drilling conditions, by using sensor fusion pattern vectors made of selected features extracted from force and torque sensor signals, a very accurate ANN prediction of tool wear is achieved. As a matter of fact, these procedures demonstrated reliable correlations between sensor signal features and tool wear level both in the case of the features extracted from the time domain and in the case of the features extracted from the frequency domain.

The prediction of tool wear can be functionally utilized to forecast the quality of the drilled holes. As a matter of fact, a correspondence between exit delamination factor and tool wear transition between the second and third phase of the wear curve was observed. In this transition, an exit delamination factor value, Df = 1.4, was identified and set as threshold beyond which unacceptable hole quality is generated.

As a result, taking into account the identified threshold, cognitive tool wear prediction via artificial neural networks can be used for on-line decision making on tool replacement to avoid unacceptable hole quality.

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