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The evolution of high-performance computing facilitates the simulation of manufacturing processes. The prediction accuracy of a numerical model of the cutting process is closely associated with the selection of constitutive and friction models. The reliability and the accuracy of these models highly depend on the value of the parameters involved in the definition of the cutting process. Direct of inverse methods are used to determine these model parameters. However, these identification procedures often neglect the link between the parameters of the material and the friction models. This article introduces a novel approach to inversely identify the best parameters value for both models at the same time and by taking into account multiple cutting conditions in the optimization routine. An artificial intelligence (AI) framework that combines the finite element modeling with an adaptive Bayesian multi-objective evolutionary algorithm (AB-MOEA) is developed, where the objective is to minimize the deviation between the experimental and the numerical results. The arbitrary Lagrangian-Eulerian (ALE) formulation and the Ti6Al4V alloy are selected to demonstrate its applicability. The investigation shows that the developed AI platform can identify the best parameters values with low computational time and resources. The identified parameters values predicted the cutting and feed forces within a deviation of less than 4% from the experiments for all the cutting conditions considered in this work. [ABSTRACT FROM AUTHOR]

The finite element approach is often adopted to study the machining process. The Lagrangian and Eulerian formulations or even Arbitrary Eulerian-Lagrangian (ALE), one of their combinations, are the most employed in the current literature; each having their pros and cons. One of the most challenging issue in finite element modelling is the large strains during the cutting process that induce high deformation levels in the elements of the mesh. Remeshing contributes to decreasing mesh deformation but the criterion adopted to control it influences the results. The Coupled Eulerian-Lagrangian (CEL) method proposes to combine the Lagrangian and Eulerian formalisms without any element deformation problem. This paper studies its implementation in Ti6Al4 V orthogonal cutting. The results are then compared to an experimental reference, as well as more standard models: an ALE model developed with Abaqus, an implicit Lagrangian model developed with Deform and an explicit Lagrangian model developed with AdvantEdge. The comparison is mainly based on the cutting forces and the chip morphology. It shows that the CEL formulation is a competitive alternative to the more standard models. [ABSTRACT FROM AUTHOR]

Material Extrusion (MEX), particularly Fused Filament Fabrication (FFF), is the most widespread among the additive manufacturing (AM) technologies. To further its development, understanding the influence of the various printing parameters on the manufactured parts is required. The effects of varying the infill percentage, the number of layers of the top and bottom surfaces and the number of layers of the side surfaces on the tensile properties of the printed parts were studied by using a full factorial design. The tensile test results allowed a direct comparison of each of the three parameters' influence on the tensile properties of the parts to be conducted. Yield strength appears to be the most affected by the number of layers of the top and bottom surfaces, which has twice the impact of the number of layers of the side surfaces, which is already twice as impactful as the infill percentage. Young's modulus is the most influenced by the number of layers of the top and bottom surfaces, then by the infill percentage and finally by the number of layers of the side surfaces. Two mathematical models were considered in this work. The first one was a polynomial model, which allowed the yield strength to be calculated as a function of the three parameters mentioned previously. The coefficients of this model were obtained by performing tensile tests on nine groups of printed samples, each with different printing parameters. Each group consisted of three samples. A second simplified model was devised, replacing the numbers of layers on the side and top/bottom surfaces with their fractions of the cross-section surface area of the specimen. This model provided results with a better correlation with the experimental results. Further tests inside and outside the parameter ranges initially chosen for the model were performed. The experimental results aligned well with the predictions and made it possible to assess the accuracy of the model, indicating the latter to be sufficient and reliable. The accuracy of the model was assessed through the R2 value obtained, R 2 = 92.47%. This was improved to R 2 = 97.32% when discarding material infill as an input parameter.

The degradation of the cutting tool and its optimal replacement is a major problem in machining given the variability in this degradation even under constant cutting conditions. Therefore, monitoring the degradation of cutting tools is an important part of the process in order to replace the tool at the optimal time and thus reduce operating costs. In this paper, a cutting tool degradation monitoring technique is proposed using bootstrap-based artificial neural networks. Different indicators from the turning operation are used as input to the approach: the RMS value of the cutting force and torque, the machining duration, and the total machined length. They are used by the approach to estimate the size of the flank wear (VB). Different neural networks are tested but the best results are achieved with an architecture containing two hidden layers: the first one containing six neurons with a Tanh activation function and the second one containing six neurons with an ReLu activation function. The novelty of the approach makes it possible, by using the bootstrap approach, to determine a confidence interval around the prediction. The results show that the networks are able to accurately track the degradation and detect the end of life of the cutting tools in a timely manner, but also that the confidence interval allows an estimate of the possible variation of the prediction to be made, thus helping in the decision for optimal tool replacement policies.

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 ( v c = 502 m/min and v f = 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.