Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches

Thin-wall parts are common in the aeronautical sector. However, their machining presents serious challenges such as vibrations and part deflections. To deal with these challenges, di erent approaches have been followed in recent years. This work presents the state of the art of thin-wall light-alloy machining, analyzing the problems related to each type of thin-wall parts, exposing the causes of both instability and deformation through analytical models, summarizing the computational techniques used, and presenting the solutions proposed by di erent authors from an industrial point of view. Finally, some further research lines are proposed

Eco-efficient process based on conventional machining as an alternative technology to chemical milling of aeronautical metal skin panels

El fresado químico es un proceso diseñado para la reducción de peso de pieles metálicas que, a pesar de los problemas medioambientales asociados, se utiliza en la industria aeronáutica desde los años 50. Entre sus ventajas figuran el cumplimiento de las estrictas tolerancias de diseño de piezas aeroespaciales y que pese a ser un proceso de mecanizado, no induce tensiones residuales. Sin embargo, el fresado químico es una tecnología contaminante y costosa que tiende a ser sustituida. Gracias a los avances realizados en el mecanizado, la tecnología de fresado convencional permite alcanzar las tolerancias requeridas siempre y cuando se consigan evitar las vibraciones y la flexión de la pieza, ambas relacionadas con los parámetros del proceso y con los sistemas de utillaje empleados. Esta tesis analiza las causas de la inestabilidad del corte y la deformación de las piezas a través de una revisión bibliográfica que cubre los modelos analíticos, las técnicas computacionales y las soluciones industriales en estudio actualmente. En ella, se aprecia cómo los modelos analíticos y las soluciones computacionales y de simulación se centran principalmente en la predicción off-line de vibraciones y de posibles flexiones de la pieza. Sin embargo, un enfoque más industrial ha llevado al diseño de sistemas de fijación, utillajes, amortiguadores basados en actuadores, sistemas de rigidez y controles adaptativos apoyados en simulaciones o en la selección estadística de parámetros. Además se han desarrollado distintas soluciones CAM basadas en la aplicación de gemelos virtuales. En la revisión bibliográfica se han encontrado pocos documentos relativos a pieles y suelos delgados por lo que se ha estudiado experimentalmente el efecto de los parámetros de corte en su mecanizado. Este conjunto de experimentos ha demostrado que, pese a usar un sistema que aseguraba la rigidez de la pieza, las pieles se comportaban de forma diferente a un sólido rígido en términos de fuerzas de mecanizado cuando se utilizaban velocidades de corte cercanas a la alta velocidad. También se ha verificado que todas las muestras mecanizadas entraban dentro de tolerancia en cuanto a la rugosidad de la pieza. Paralelamente, se ha comprobado que la correcta selección de parámetros de mecanizado puede reducir las fuerzas de corte y las tolerancias del proceso hasta un 20% y un 40%, respectivamente. Estos datos pueden tener aplicación industrial en la simplificación de los sistemas de amarre o en el incremento de la eficiencia del proceso. Este proceso también puede mejorarse incrementando la vida de la herramienta al utilizar fluidos de corte. Una correcta lubricación puede reducir la temperatura del proceso y las tensiones residuales inducidas a la pieza. Con este objetivo, se han desarrollado diferentes lubricantes, basados en el uso de líquidos iónicos (IL) y se han comparado con el comportamiento tribológico del par de contacto en seco y con una taladrina comercial. Los resultados obtenidos utilizando 1 wt% de los líquidos iónicos en un tribómetro tipo pin-on-disk demuestran que el IL no halogenado reduce significativamente el desgaste y la fricción entre el aluminio, material a mecanizar, y el carburo de tungsteno, material de la herramienta, eliminando casi toda la adhesión del aluminio sobre el pin, lo que puede incrementar considerablemente la vida de la herramienta.Chemical milling is a process designed to reduce the weight of metals skin panels. This process has been used since 1950s in the aerospace industry despite its environmental concern. Among its advantages, chemical milling does not induce residual stress and parts meet the required tolerances. However, this process is a pollutant and costly technology. Thanks to the last advances in conventional milling, machining processes can achieve similar quality results meanwhile vibration and part deflection are avoided. Both problems are usually related to the cutting parameters and the workholding. This thesis analyses the causes of the cutting instability and part deformation through a literature review that covers analytical models, computational techniques and industrial solutions. Analytics and computational solutions are mainly focused on chatter and deflection prediction and industrial approaches are focused on the design of workholdings, fixtures, damping actuators, stiffening devices, adaptive control systems based on simulations and the statistical parameters selection, and CAM solutions combined with the use of virtual twins applications. In this literature review, few research works about thin-plates and thin-floors is found so the effect of the cutting parameters is also studied experimentally. These experiments confirm that even using rigid workholdings, the behavior of the part is different to a rigid body at high speed machining. On the one hand, roughness values meet the required tolerances under every set of the tested parameters. On the other hand, a proper parameter selection reduces the cutting forces and process tolerances by up to 20% and 40%, respectively. This fact can be industrially used to simplify workholding and increase the machine efficiency. Another way to improve the process efficiency is to increase tool life by using cutting fluids. Their use can also decrease the temperature of the process and the induced stresses. For this purpose, different water-based lubricants containing three types of Ionic Liquids (IL) are compared to dry and commercial cutting fluid conditions by studying their tribological behavior. Pin on disk tests prove that just 1wt% of one of the halogen-free ILs significantly reduces wear and friction between both materials, aluminum and tungsten carbide. In fact, no wear scar is noticed on the ball when one of the ILs is used, which, therefore, could considerably increase tool life

Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc) for high accuracy. However it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper an approach for surface profile pre-evaluation in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and free rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. 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. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy

A neural network approach for chatter prediction in turning

[EN] Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit self-excited vibration. In this paper, an artificial neural network (ANN) is applied to model turning stability. The analytical stability limit is used to generate a data set that trains the ANN. It is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training.The authors gratefully acknowledge financial support from the UNC ROI program. Elena Perez-Bernabeu and Miguel Selles also acknowledge support from Universitat Politenica de Valencia (PAID-00-17). Additionally, some of the neural net figures and the 10-fold cross validation figures are based on the TikZ codes provided on StackExchange-TeX by various users. Harish Cherukuri would like to thank them for their valuable advice.Cherukuri, H.; Pérez Bernabeu, E.; Sellés, M.; Schmitz, TL. (2019). A neural network approach for chatter prediction in turning. Procedia Manufacturing. 34:885-892. https://doi.org/10.1016/j.promfg.2019.06.1598858923

Recent advances in modelling and simulation of surface integrity in machining - A review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted

Recent advances in modelling and simulation of surface integrity in machining - A review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted

EXPERIMENTAL INVESTIGATION OF TOOL WEAR AND INDUCED VIBRATION IN TURNING HIGH HARDNESS AISI52100 STEEL USING CUTTING PARAMETERS AND TOOL ACCELERATION

In machining of high hardness steel, vibration of cutting tool increases tool wear which reduces its life. Tool wear is catastrophic in nature and hence investigation of its assessment is important. This study investigates experimentally induced vibration during turning of hardened AISI52100 steel of hardness 54±2 HRC using coated carbide insert. In this context, cutting tool acceleration is measured and used to develop a novel mathematical model based on acquired real time acceleration signals of cutting tool. The obtained model is validated as R2= 0.93 while its residuals values closely follow the straight line. The predictions are confirmed by conducting conformity test which revealed a close degree of agreement with respect to the experimental values. The Artificial Neural Network (ANN) examination is performed to determine the model regression value. The study shows that the examined reports forecasts of ANN are more exact than regression analysis. The future directon of this investigation is towards developing a low-cost microcontroller-based hardware unit for in-process tool wear monitoring which could be beneficial for small scale industries

Surfaces functionality of precision machined components: Modelling, simulation, optimization and control

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 23/07/2008.This research develops an analytical scientific approach for investigating the high precision surface generation and the quantitative analysis of the effects of direct factors in precision machining. The research focuses on 3D surface characterization with particular reference to the turning process and associated surface generation. The most important issue for this research is surface functionality which is becoming important in the current engineering industry. The surface functionality should match with the characterization parameters of the machined surface, which can be expressed in formula form as proposed in chapter 4. Modelling and simulation are extensively used in the research. The modelling approach integrates the cutting forces model, thermal mode% vibration model, tool wear model, machining system response model and surface topography model. All of those models are integrated as a whole model. The physical model with such as direct inputs is formed. The major inputs to the model are tooling geometry and the process variables. The outputs from the modelling approach are cutting force, surface texture parameters, dimensional errors, residual stress and material removal rate. MATLAB and Simulink are used as tools to implement the modelling and simulation. According to the simulation results, it is found that the feed rate has the most profound effect on in surface generation. The influence of the vibrations between the cutting tool and the workpiece on the surface roughness may be minimised by the small feed rate and large tool nose radius. Surface functionality simulation has been developed to model and simulate the surface generation in precision turning. The surface functionality simulation model covers the material and tool wear as well. It shows that chip formation is resulted from cutting forces. Cutting trials are conducted to validate the modelling and simulation developed. There are positive results that show the agreement between the simulation and experimental results. The analysis of the results of turning trials and simulations are conducted in order to find out the effects of process variables and tooling characteristics on surface texture and topography and machining instability. From the research, it can be concluded that the investigation on modelling and simulation of precision surfaces generation in precision turning is performed well against the research objectives as proposed. Recommendations for future work are to improve the model parameters identification, including comprehensive tool wear, chip formation and using Neural Networks modelling in the engineering surface construction system.Universiti Teknologi Tun Hussien Orin (UTHM

In-process pokayoke development in multiple automatic manufacturing processes

In this dissertation, three in-process pokayoke systems were developed to prevent defects from occurring, so as to ensure product quality for three automated manufacturing processes.;The first pokayoke development resulted in an in-process, gap-caused flash monitoring (IGFM) system for injection-molding machines. An accelerometer sensor was integrated in the proposed system to detect the difference of the vibration signals between flash and non-flash products. By sub-grouping every two consecutive molded parts with the vibration signal, the online statistical process control (OLSPC) was able to monitor 100% of the molded products. The threshold of this system established by the SPC approach can determine if flash occurred when the machine was in process. The testing results indicated that the accuracy of this IGFM system was 94.7% when flash is caused by a mold-closing gap.;The second pokayoke development led to an in-process surface roughness adaptive control (ISRAC) system for CNC end milling operations. A multiple linear regression algorithm was successfully employed to generate the models for predicting surface roughness and adaptive feed rate change in real time. Not only were the machining parameters included in the ISRAC pokayoke system, but also the cutting force signals collected by a dynamometer sensor. The testing results showed this proposed ISRAC system was able to predict surface roughness in real time with an accuracy of 91.5%, and could successfully implement adaptive control 100% of the time during milling operations.;The third pokayoke development brought an in-process surface roughness adaptive control (ISRAC) system in CNC turning operations. This system employed a back-propagation (BP) neural network algorithm to train the models for in-process surface roughness prediction and adaptive parameter control. In addition to the machining parameters, vibration signals in the Z direction used as an input variable to the neural network system were included for training. The test runs showed this pokayoke system was able to predict surface roughness in real time with an accuracy of 92.5%. The 100% success rate for adaptive control proved that this proposed system could be implemented to adaptively control surface roughness during turning operations