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Lightweight materials like Carbon Fiber Reinforced Polymers (CFRPs) present certain challenges, mainly regarding the resultant surface integrity of the holes. Different techniques such as optimized tool geometries, machining techniques and cooling/lubricating techniques have been tested for improving surface integrity on CFRPs. Unfortunately using coolants presents certain challenges when machining CFRPs, as the moisture absorption can degrade the mechanical properties of the composite, on top of the environmental and health hazards created by water and oil based metalworking fluids. Therefore, this study aims to evaluate the influence of environmentally friendly liquid carbon dioxide (LCO2) based cooling/lubricating conditions and Polycrystalline Diamond (PCD) drill bit geometries (point angle) on the delamination when drilling CFRP composites. The results showed significant influence of the tool geometry and cooling condition on the surface integrity of CFRPs, especially in the case of the former.

In the quest for decreasing fuel consumption and resulting gas emissions in the aeronautic sector, lightweight materials such as Carbon Fiber Reinforced Polymers (CFRPs) and Ti-6Al-4V alloys are being used. These materials, with excellent weight-to-strength ratios, are widely used for structural applications in aircraft manufacturing. To date, several studies have been published showing that the use of metalworking fluids (MWFs), special tool geometries, or advanced machining techniques is required to ensure a surface quality that meets aerospace component standards. Conventional MWFs pose a number of environmental and worker health hazards and also degrade the mechanical properties of CFRPs due to water absorption in the composite. Therefore, a transition to more environmentally friendly cooling/lubrication techniques that prevent moisture problems in the composite is needed. This research shows that lubricated LCO2 is a viable option to improve the quality of drilled CFRP and titanium aerospace components compared to dry machining, while maintaining clean work areas. The results show that the best combination of tool geometry and cooling conditions for machining both materials is drilling with Brad point drills and lubricated LCO2. Drilling under these conditions resulted in a 90 % improvement in fiber pull-out volume compared to dry machined CFRP holes. In addition, a 33 % reduction in burr height and a 15 % improvement in surface roughness were observed compared to dry drilling of titanium.

Although much research has been carried out in the field of the milling of GFRP (Glass Fibre Reinforced Polymer) composites, the complexity of the process is such that it is still not mastered in many industrial cases. The current work is aimed at studying the influence of three different geometries of PCD (PolyCrystalline Diamond) and cemented tungsten carbide cutting tools during the up-milling of GFRP composites at fixed cutting conditions (vc = 502 m/min and vf = 420 mm/min). Delamination, cutting forces and tool wear are compared at the fresh and worn states, and the correlation between the lifespan and the cost of the cutting tool is analysed. The main wearing phase of the tools was performed under the conditions of production in the facilities of a company (Sobelcomp, Loncin, Belgium). The results indicate that the PCD tool with the straight edge, inclined peripheral tooth shape produces the smallest total cutting force and less delamination (shortest and lowest number of delaminated fibres) at both fresh and worn states. Moreover, the grinding ability of PCD makes the cutting tool cost per part lower than for cemented carbide. The PCD tool is therefore the best option to mill GFRP parts.

A known problem of additive manufactured parts is their poor surface quality, which influences product performance. There are different surface treatments to improve surface quality: blasting is commonly employed to improve mechanical properties and reduce surface roughness, and electropolishing to clean shot peened surfaces and improve the surface roughness. However, the final surface roughness is conditioned by multiple parameters related to these techniques. This paper presents a prediction model of surface roughness (Ra) using an Artificial Neural Network considering two parameters of the SLM manufacturing process and seven blasting and electropolishing processes. This model is proven to be in agreement with 429 experimental results. Moreover, this model is then used to find the optimal conditions to be applied during the blasting and the electropolishing in order to improve the surface roughness by roughly 60%.

Aerostructures withstand cyclic mechanical loads and therefore, their design must fulfil surface integrity and fatigue strength requirements. This paper studies the effect of pneumatic machine hammer peening on surface integrity of 7050-T7451 aluminium alloy. Specimens were hammer peened using two different stepover distances (0.07 and 0.35 mm) and initial offset (0.3 and 0.5 mm). A Kistler dynamometer was used to measure the forces generated by hammer peening. Then, the surface topography, residual stresses and microstructural damage of the specimens were characterised. The results demonstrate that the magnitude of residual stresses and the thickness of the affected layer increases when reducing the stepover distance, while surface roughness reduces. Additionally, none of the tested conditions produced microstructural defects. These findings suggest that pneumatic hammer peening is an additional process to manufacture thin-walled structural aluminium alloy components.

Drilling is a continuous cutting process where two or more cutting edges remove the material, to obtain the desired feature. During the chip evacuation, it generally rubs against the generated surface. Thus, the roughness obtained differs from other machining processes such as turning or milling. Therefore, surface roughness can be different from the analytically expected one. In this research work, an analysis of the cutting conditions where a level of roughness is expected to meet specific requirements has been carried out. 600 holes were made with two different tool geometries on steel without modifying the cutting conditions. When analysing the surface generated, certain variability in the roughness profiles obtained can be observed. External signals to the machine tool were acquired with sensors (cutting forces, vibrations, and acoustic emissions) as well as internal signals (spindle power, spindle torque in the Z-axis, spindle current and positions, speeds, accelerations, and jerk of the tool tip in the three axes of the machine). The most representative statistical features of the signals regarding roughness were selected using correlation analysis. Besides that, the hierarchical clustering of statistical features of the external and internal signals of the process was compared with clusters obtained using roughness parameters. Results show that clusters appear using signals highly related to the roughness parameters obtained from the measured profiles, confirming a mapping between the acquired signals during the machining process and the roughness of the holes.

Tool condition monitoring have an important role in machining processes to reduce defective component and ensure quality requirements. Stopping the process before the tool breaks or an excessive tool wear is reached can avoid costs resulting from that undesirable situation. This research work presents the results obtained in drilling process monitoring carried out on Inconel 718. Monitoring systems should be light and scalable. Following this idea, multiple sensors for external signal acquisition are used in this work (cutting forces, vibrations, and acoustic emissions) and several machine internal signals are collected. The main objective is to evaluate the capacity of each acquisition source for the reconstruction of the tool wear curve and subsequently detection of tool breakage. Given the difficulty of using all of these signals in a real system, the methodology used to analyse the data makes it possible to have a comparative analysis of the potential of each of these sources for tool wear monitoring during the drilling process. The results indicate cutting forces whether they come from internal signals or external signals can carry out this task accurately. At the same time of data acquisition, detailed tool wear measurements were added.

Machining operations affect the properties of the final surface layer, and these can impact on its functional per- formance, particularly on fatigue behaviour. Among the properties of the machined surface, surface topography is one major parameter affecting fatigue behaviour. The literature review has demonstrated that stress concen- tration factors K t of the surface provide a more reliable estimation of the impact on the fatigue behaviour of machined components. Finite Element (FE) simulations can accurately calculate the stress concentration factor of machined surfaces, but they incur a high computational cost. Recent advances have shown that analytical models can reliably determine stress concentration factors of 2D roughness profiles. However, analytical models that predict stress concentration factors of 3D surface topographies are still lacking. This paper is aimed at devel- oping an analytical method to calculate the stress concentration factor K t of 3D surfaces generated by machining operations. To validate the model, a specimen of 7475-T7351 aluminium alloy was face milled and its surface topography was characterised using an Alicona IFG4 profilometer. Stress concentration factors were calculated in the selected surface regions using the proposed analytical model, and later compared to results obtained by FE simulations. The mean difference in the stress concentration factor K t calculated by the proposed analytical and FE models is of 1.53%. Importantly, the developed analytical model reduces the computing time by 3000 times compared to FE models, and enables the analysis of larger surfaces.

Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes.Through this, it is intended to improve the management of resources, decision-making, production costs and productiontimes. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, itis necessary to develop light and scalable TCMS that can provide information about the tool status using the signals providedby the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First,the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of toolwear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed.Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometrywere trained and a model was obtained. Then, the model was tested using signals from two different tool geometries. Theexperiments were carried out on a vertical milling machine on a steel with composition 35CrMo4LowS under pre-establishedcutting conditions. The results show that the most sensitive signals to monitor the tool wear in the time domain are the feedforce (external) and thez-axis motor torque (internal). The models created for the fulfilment of the second objective show agreat capacity of prediction even when dealing with tools with different geometrical characteristics.

This directory contains the raw data acquired by Mondragon Unibertsitatea during the execution of drilling tests. These data were used to obtain the results presented in the article "The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process" published in International Journal of Advanced Manufacturing Technology on January 2019. Drilling is one of the most critical processes in the machining sector. This process is carried out in the last phases of the machining of a part, therefore, an almost finished part could be destroyed if the proper conditions are not met. Tool wear is one of the biggest problems in machining processes having an increasingly negative impact on the machined part. In order to carry out a study on the effect that tool wear has on the different signals acquired during the process and identify the sensitivity of each of the signals with respect to the others, drilling tests were carried out at different levels of tool wear. This document details the acquired signals and their organization in this directory.

The performance of aeronautic critical components is strongly dependent on its fatigue behavior, which is directly linked to their surface integrity condition. Broaching operation is a machining operation extensively used for the manufacturing of some features due to the good dimensional quality and surface integrity condition obtained. Thus, the characteristics of surface integrity obtained in broaching is a key aspect to be considered for the improvement of the fatigue life. This work proposes a Finite Element Method (FEM) model for the prediction of the surface integrity (material damage and residual stresses) of the workpiece obtained after the broaching process using the commercial finite element software DEFORM 2D. The model includes a self-characterized Johnson-Cook flow stress constitutive law for the titanium alloy Ti64. Experimental tests were carried out in an EKIN RAS 10x160x320 hydraulic broaching machine at different cutting conditions for the validation of the predictive model. Apart from the fundamental output variables, such as, forces and chip morphology, a comprehensive study of the surface integrity of the machined piece was done. The residual stresses generated by the cutting process were measured by the hole-drilling technique. Microstructural alterations (material damage) of the workpiece was analyzed by optical microscopy and Scanning Electron Microscope. Finally, the surface topography was examined by contact and optical profilometers. The results of the predictions showed significant good agreement with the experimental tests.

The guarantee of surface integrity has become a primary objective for researchers when analysing the reliability of machined aircraft aluminium alloy structural parts in high-speed machining. This work studies the effect of cutting speed on the surface integrity of face milled 7050-T7451 aluminium workpieces. First, 7050-T7451 aluminium workpieces were face milled under dry conditions using three cutting speeds (200, 800, 1400 m/min) at constant feed (0.20 mm/tooth) and depth of cut (1 mm). An indexable face milling cutter with a diameter of 50 mm with five uncoated inserts was used in the face milling tests. During the machining process, cutting forces were acquired employing a Kistler dynamometer in order to understand the influence of the mechanical load on the final surface quality. The surface roughness p roduced by the milling process was measured using a portable rugosimeter. The residual stresses generated by the cutting process were measured by the holedrilling technique. In addition, small specimens were cut out from the workpieces and microstructural alterations of the surface layer were analysed employing optical microscopy techniques. The results demonstrate that the magnitude of residual stresses and the thickness of the affected layer is sensitive to the cutting speed, while surface roughness and microstructural defects do not show significant variations for the tested conditions.