Effect of tool wear on delamination in drilling composite materials

Abstract Among all machining operations, drilling using twist drill is the most frequently applied for secondary machining of composite materials owing to the need for structure joining. Delamination is mostly considered as the principal failure model in drilling of composite materials. Drill wear is a serious concern in hole-making industry, as it is necessary to prevent damage of cutting tools, machine tools and workpieces. The industrial experience shows the worn drill causes more delamination. This paper presents a comprehensive analysis of delamination caused by the drill wear for twist drill in drilling carbon fiber-reinforced composite materials. The critical thrust force at the onset of delamination for worn drill is predicted and compared with that of ideal drill. The experimental results demonstrate that though the critical thrust force is higher with increasing wear ratio, the delamination becomes more liable to occur because the actual thrust force increases to larger extent, as the thrust factor (Z) illustrates. Compared to sharp drill, the worn twist drill allows for lower feed rate below which the delamination damage can be avoided.

Comprehensive Study on Tool Wear During Machining of Fibre-Reinforced Polymeric Composites

© 2021 Springer-Verlag. The final publication is available at Springer via https://dx.doi.org/10.1007/978-981-33-4153-1.The use of fibre reinforced polymeric (FRP) composites has increased rapidly, especially in many manufacturing (aerospace, automobile and construction) industries. The machining of composite materials is an important manufacturing process. It has attracted several studies over the last decades. Tool wear is a key factor that contributes to the cost of the machining process annually. It occurs due to sudden geometrical damage, frictional force and temperature rise at the tool-work interaction region. Moreover, tool wear is an inevitable, gradual and complex phenomenon. It often causes machined-induced damage on the workpiece/FRP composite materials. Considering the geometry of drill, tool wear may occur at the flank face, rake face and/or cutting edge. There are several factors affecting the tool wear. These include, but are not limited to, drilling parameters and environments/conditions, drill/tool materials and geometries, FRP composite compositions and machining techniques. Hence, this chapter focuses on drilling parameters, tool materials and geometries, drilling environments, types of tool wear, mechanisms of tool wear and methods of measurement of wear, effects of wear on machining of composite materials and preventive measures against rapid drill wear. Conclusively, some future perspectives or outlooks concerning the use of drill tools and their associated wears are elucidated, especially with the advancement in science and technology.Peer reviewedFinal Accepted Versio

Artificial neural networks for vibration based inverse parametric identifications: A review

Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs’ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes

Indirect multisignal monitoring and diagnosis of drill wear

A machine tool utilisation rate can be improved by an advanced condition monitoring system using modern sensor and signal processing techniques. A drilling test and analysis program for indirect tool wear measurement forms the basis of this thesis. For monitoring the drill wear a number of monitoring methods such as vibration, acoustic emission, sound, spindle power and axial force were tested. The signals were analysed in the time domain using statistical methods such as root mean square (rms) value and maximum. The signals were further analysed using Fast Fourier Transform (FFT) to determine their frequency contents. The effectiveness of the best sensors and analysis methods for predicting the remaining lifetime of a tool in use has been defined. The results show that vibration, sound and acoustic emission measurements are more reliable for tool wear monitoring than the most commonly used measurements of power consumption, current and force. The relationships between analysed signals and tool wear form a basis for the diagnosis system. Higher order polynomial regression functions with a limited number of terms have been developed and used to mimic drill wear development and monitoring parameters that follow this trend. Regression analysis solves the problem of how to save measuring data for a number of tools so as to follow the trend of the measuring signal; it also makes it possible to give a prognosis of the remaining lifetime of the drill. A simplified dynamic model has been developed to gain a better understanding of why certain monitoring methods work better than others. The simulation model also serves the testing of the developed automatic diagnostic method, which is based on the use of simplified fuzzy logic. The simplified fuzzy approach makes it possible to combine a number of measuring parameters and thus improves the reliability of diagnosis. In order to facilitate the handling of varying drilling conditions and work piece materials, the use of neural networks has been introduced in the developed approach. The scientific contribution of the thesis can be summarised as the development of an automatically adaptive diagnostic tool for drill wear detection. The new approach is based on the use of simplified fuzzy logic and higher order polynomial regression analysis, and it relies on monitoring methods that have been tested in this thesis. The diagnosis program does not require a lot of memory or processing power and consequently is capable of handling a great number of tools in a machining centre.reviewe