Plunge milling time optimization via mixed-integer nonlinear programming

International audiencePlunge milling is a recent and efficient production mean for machining deep workpieces, notably in aeronautics. This paper focuses on the minimization of the machining time by optimizing the values of the cutting parameters. Currently, neither Computer-Aided Manufacturing (CAM) software nor standard approaches take into account the tool path geometry and the control laws driving the tool displacements to propose optimal cutting parameter values, despite their significant impact. This paper contributes to plunge milling optimization through a Mixed-Integer NonLinear Programming (MINLP) approach, which enables us to determine optimal cutting parameter values that evolve along the tool path. It involves both continuous (cutting speed, feed per tooth) and, in contrast with standard approaches, integer (number of plunges) optimization variables, as well as nonlinear constraints. These constraints are related to the Computer Numerical Control (CNC) machine tool and to the cutting tool, taking into account the control laws. Computational results, validated on CNC machines and on representative test cases of engine housing, show that our methodology outperforms standard industrial engineering know-how approaches by up to 55% in terms of machining time

A model-based sustainable productivity concept for the best decision-making in rough milling operations

[EN]There is a need in manufacturing as in machining of being more productive. However, at the same time, workshops are also urged for lesser energy waste in cutting operations. Specially, rough milling of impellers and bladed integrated disks of aircraft engines need an efficient use of energy due to the long cycle times. Indeed, to avoid dramatic tool failures and idle times, cutting conditions and operations tend to be very conservative. This is a multivariable problem, where process engineers need to handle several aspects such as milling operation type, toolpath strategies, cutting conditions, or clamping systems. There is no criterion embracing productivity and power consumption. In this sense, this work proposes a methodology that meets productivity and sustainability by using a specific cutting energy or sustainable productivity gain (SPG) factor. Three rough milling operations-slot, plunge nad trochoidal milling-were modelled and verified. A bottom-up approach based on data from developed mechanistic force models evaluated and compared different alternatives for making a slot, which is a common operation in that king of workpieces. Experimental data confirmed that serrated end milling with the highest SPG value of 1 is the best milling operation in terms of power consumption and mass removal rate (MRR). In the case of plunge milling technique achieve an SPG < 0.51 while trochoidal milling produces a very low SPG value.The authors acknowledge the support from the Spanish Government (JANO, CIEN Project, 2019.0760) and Basque Government (ELKARTEK19/46, KK-2019/00004). This research was funded by Tecnologico de Monterrey through the Research Group of Nanotechnology for Devices Design, and by the Consejo Nacional de Ciencia y Tecnologia de Mexico (Conacyt), Project Number 296176, and National Lab in Additive Manufacturing, 3D Digitizing and Computed Tomography (MADiT) LN299129. The authors also acknowledge the support from Garikoitz Goikoetxea and fruitful discussions with Mr. Jon Mendez (Guhring (c)) and Endika Monge (Hoffmann Group (c))

Build-Up an Economical Tool for Machining Operations Cost Estimation

Currently, there is a lack of affordable and simple tools for the estimation of these costs, especially for machining operations. This is particularly true for manufacturing SMEs, in which the cost estimation of machined parts is usually performed based only on required material for part production, or involves a time-consuming, non-standardized technical analysis. Therefore, a cost estimation tool was developed, based on the calculated machining times and amount of required material, based on the final drawing of the requested workpiece. The tool was developed primarily for milling machines, considering milling, drilling, and boring/threading operations. Regarding the considered materials, these were primarily aluminum alloys. However, some polymer materials were also considered. The tool first estimates the required time for total part production and then calculates the total cost. The total production time is estimated based on the required machining operations, as well as drawing, programming, and machine setup time. A part complexity level was also introduced, based on the number of details and operations required for each workpiece, which will inflate the estimated times. The estimation tool was tested in a company setting, comparing the estimated operation time values with the real ones, for a wide variety of parts of differing complexity. An average error of 14% for machining operation times was registered, which is quite satisfactory, as this time is the most impactful in terms of machining cost. However, there are still some problems regarding the accuracy in estimating finishing operation timesinfo:eu-repo/semantics/publishedVersio

Analyse qualitative des paramètres influents pour la planification de trajectoires en tréflage sur alliages de titane

Le travail présenté dans ce mémoire s'intéresse au tréflage (ou fraisage en plongée) appliqué à l'usinage des pièces en alliage de titane Ti6Al4V. Ce sujet dérive de la problématique de l'absence des aides au choix des paramètres et des stratégies d'usinage pour ce procédé. L'approche proposée comporte principalement trois parties. La première partie traite l'influence des conditions de coupe et des paramètres géométriques de l'outil de coupe, à travers la réalisation de plusieurs essais expérimentaux avec différents outils et l'analyse des efforts de coupe générés. Les données obtenues ont permis la mise en place d'une modélisation des efforts de coupe intégrant l'ensemble des paramètres étudiés. La deuxième partie est dédiée à l'étude d'une problématique identifiée durant les essais expérimentaux, qui est l'augmentation des forces de coupe à la fin de la phase de plongée en tréflage. L'analyse de l'influence des paramètres géométriques et des conditions de coupe a permis de proposer des préconisations sur leurs choix et des modes opératoires permettant d'éviter ce problème. La dernière partie est consacrée à l'optimisation des stratégies de tréflage en FAO dans le cas de poches simples. La démarche proposée permet d'optimiser le choix du pas de plongée et du diamètre d'outil, pour arriver à minimiser le nombre de plongées nécessaires à l'ébauche de la pièce.The work presented in this paper deals with the plunge milling applied to the machining of workpieces made of titanium alloy Ti6Al4V. This topic derives from the problem of the absence of aids in the selection of parameters and machining strategies for this process. The proposed approach consists in three main steps. The first part deals with the influence of the cutting conditions and geometric parameters of the cutting tool, by carrying out several experimental tests with different tools and analysing the generated cutting forces. The data obtained allowed the development of a modelling of the cutting forces integrating all the studied parameters. The second part is dedicated to the study of a problem identified during the experimental tests, which is the increase of the cutting forces at the end of the plunging phase. The analysis of the influence of geometric parameters and cutting conditions has resulted in the proposal of recommendations regarding the choice of these parameters and the operating procedures in order to avoid this problem. The last part focuses on the optimization of plunge milling strategies in CAM in the case of simple pockets. The proposed approach allows to optimize the choice of the radial offset and the tool diameter, in order to minimize the number of plunges required in roughing the workpiece

Estimativa de tempos de maquinagem com base em redes neuronais

Um dos fatores mais importantes na produção de moldes para injeção de plástico é a estimativa do custo dos serviços de maquinagem, que representam uma parte significativa do preço final do molde. O custo destes serviços é habitualmente determinado em função do tempo de maquinagem, cujo cálculo é geralmente longo e dispendioso. Se for considerado que as peças dos moldes de injeção são todas diferentes, compreende-se que a correta e célere estimativa de tempos de maquinagem é de grande importância para o sucesso de uma empresa. Esta dissertação apresenta uma proposta de aplicação de redes neuronais artificiais na estimativa de tempos de maquinagem de peças standard de moldes de injeção de plástico. Para o efeito, foram simuladas peças e calculados os tempos de maquinagem para recolher dados suficientes para o treino das redes neuronais. Foi estudada a influência da arquitetura de rede, da quantidade de dados de entrada e das variáveis utilizadas no treino da rede, de forma a encontrar a rede neuronal com maior precisão. A aplicação de redes neuronais neste trabalho revelou-se uma forma célere e eficaz de calcular os tempos de corte, podendo dar um forte contributo a empresas do setor.One of the most important factors in the production of plastic injection molds is the cost estimation of machining services which represents a significant part of the final mold price. The cost of these services is commonly determined as a function of the machining time, which is usually long and expensive to calculate. If it is considered that the injection mold parts are all different, it is understood that the correct and quick estimation of machining times is of great importance for the success of a company. This dissertation presents a proposal for the application of artificial neural networks in machining time estimation for standard injection molds parts. For this purpose, parts were simulated and machining times were calculated to collect enough data for training the neural networks. The influence of the network architecture, the amount of input data and the variables used in the training of the network were studied in order to find the neural network with greater precision. The application of neural networks in this work proved to be a quick and efficient way to calculate cutting times, which can give a strong contribution to companies in the sector