Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Modelling the influence of machined surface roughness on the fatigue life of aluminium alloy

The influence of machined surface roughness on the fatigue life of 7010 aluminium alloy has been investigated. Four-point bending specimen have been machined according to various machining conditions and tested in fatigue. In order to explain the high dependence of SN curves on the surface roughness of the specimen, an approach based on the finite element analysis of measured surface topography is proposed. Surface grooves due to machining are supposed to generate stress concentrations that are so calculated. A model of fatigue life prediction is developed, using this definition of local Kt

Influence of anodizing process on fatigue life of a machined aluminium alloy

In order to investigate the coupled effects of machining and anodizing processes on fatigue life of alloy 7010-T7451, a series of rotating bending fatigue tests were conducted at 60Hz. In the as machined condition, test results showed that fatigue life is surface roughness dependent and that fatigue life decreases with an increase in surface roughness and this effect is found to be more pronounced in high cycle fatigue where major portion of fatigue life is consumed in nucleating the cracks. Effects of pretreatments, like degreasing and pickling employed prior to anodizing, on fatigue life of the given alloy were also studied. Results demonstrated that degreasing showed no change in fatigue life while pickling had negative impact on fatigue life of specimens. The small decrease in fatigue life of anodized specimens as compare to pickled specimens is attributed to brittle and microcracking of the coating. Scanning electron microscopic (SEM) examination revealed multi-site crack initiation for the pickled and anodized specimens. SEM examination showed that pickling solution attacked the grain boundaries and intermetallic inclusions present on the surface resulting in pits formation. These pits are of primary concern with respect to accelerated fatigue crack nucleation and subsequent anodized coating formation

Statistical analysis of the effect of machining parameters on fatigue life of aerospace grade aluminum alloy (AL 6082T6)

In this research work, aerospace grade aluminium alloy (Al 6082-T6) was analysed for the effect of cutting parameters on the fatigue life of the machined samples and optimization of cutting parameters for response factor. Different combinations of machining parameters were selected according to the ISO 3685 for sample preparation. Fatigue life of the samples was the response variable under investigation. Specimens for the rotating bending fatigue test were prepared according to the BS ISO 1143:2010 standards. The cutting inserts were selected from Sandvik Coromant catalogue recommended for machining of Al 6082-T6 alloy. A Designed of Experiment (DoE) with full factorial design was employed and a total of 81 experiments were performed for combination of cutting parameters. Fatigue life of the samples was observed to decreases with increasing feed rate, which is attributed to the compressive residual stresses at the surface of the samples. However, fatigue life increased with higher cutting speed and Depth of Cut (DoC)

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

Multi-scale simulation of the nano-metric cutting process

Molecular dynamics (MD) simulation and the finite element (FE) method are two popular numerical techniques for the simulation of machining processes. The two methods have their own strengths and limitations. MD simulation can cover the phenomena occurring at nano-metric scale but is limited by the computational cost and capacity, whilst the FE method is suitable for modelling meso- to macro-scale machining and for simulating macro-parameters, such as the temperature in a cutting zone, the stress/strain distribution and cutting forces, etc. With the successful application of multi-scale simulations in many research fields, the application of simulation to the machining processes is emerging, particularly in relation to machined surface generation and integrity formation, i.e. the machined surface roughness, residual stress, micro-hardness, microstructure and fatigue. Based on the quasi-continuum (QC) method, the multi-scale simulation of nano-metric cutting has been proposed. Cutting simulations are performed on single-crystal aluminium to investigate the chip formation, generation and propagation of the material dislocation during the cutting process. In addition, the effect of the tool rake angle on the cutting force and internal stress under the workpiece surface is investigated: The cutting force and internal stress in the workpiece material decrease with the increase of the rake angle. Finally, to ease multi-scale modelling and its simulation steps and to increase their speed, a computationally efficient MATLAB-based programme has been developed, which facilitates the geometrical modelling of cutting, the simulation conditions, the implementation of simulation and the analysis of results within a unified integrated virtual-simulation environment

Design of an instrumented smart cutting tool and its implementation and application perspectives

This paper presents an innovative design of a smart cutting tool, using two surface acoustic wave (SAW) strain sensors mounted onto the top and the side surface of the tool shank respectively, and its implementation and application perspectives. This surface acoustic wave-based smart cutting tool is capable of measuring the cutting force and the feed force in a real machining environment, after a calibration process under known cutting conditions. A hybrid dissimilar workpiece is then machined using the SAW-based smart cutting tool. The hybrid dissimilar material is made of two different materials, NiCu alloy (Monel) and steel, welded together to form a single bar; this can be used to simulate an abrupt change in material properties. The property transition zone is successfully detected by the tool; the sensor feedback can then be used to initiate a change in the machining parameters to compensate for the altered material properties.The UK Technology Strategy Board (TSB) for supporting this research (SEEM Project, contract No. BD266E

Comprehension of chip formation in laser assisted machining

Laser Assisted Machining (LAM) improves the machinability of materials by locally heating the workpiece just prior to cutting. Experimental investigations have confirmed that the cutting force can be decreased, by as much as 40%, for various materials. In order to understand the effect of the laser on chip formation and on the temperature fields in the different deformation zones, thermo-mechanical simulations were undertaken. A thermo-mechanical model for chip formation was also undertaken. Experimental tests for the orthogonal cutting of 42CrMo4 steel were used to validate the simulation. The temperature fields allow us to explain the reduction in the cutting force and the resulting residual stress fields in the workpiece.Contrat Plan Etat Région (CPER) Pays de la Loir