Experimental characterization and finite element modeling of critical thrust force in cfrp drilling

Composite laminates are used in many applications in ae-rospace/defense industries due to their high strength-to-weight ratio and corrosion resistance properties. In general, composite materials are hard-to-machine materials which exhibit low drilling efficiency and drilling-induced delamination damage at exit. Hence, it is important to understand the drilling processes for composite materials. This article presents a comprehensive study involving experimental characterization of drilling process to understand the cutting mechanism and relative effect of cutting parameters on delamination during drilling of carbon fiber reinforced plastic (CFRP). Thrust force and torque data are acquired for analyzing the cutting mechanism, initiation and propagation of delamination, and identification of critical thrust force below which no damage occurs. An FE model for prediction of critical thrust force has been developed and validated with experimental results. A [0/90] composite laminate is modeled simulating the last two plies in exit condition and a thin interface layer is inserted in between the plies to capture delamination extent. The tool geometry is modeled as “rigid body” with geometric features of twist drill used in experiments. The tool is indented on the workpiece to simulated tool feeding action into the workpiece. The FE model predicts the critical thrust force within 5% of the experimentally determined mean value.     

C/E复合材料螺旋铣削制孔方法抑制缺陷产生的机理

传统钻削加工碳纤维/环氧树脂(Carbon/epoxy,C/E)复合材料时容易产生加工缺陷,而螺旋铣削作为一种新的制孔方法在航空材料的加工中逐渐受到关注。为分析螺旋铣削制孔方法抑制缺陷产生的机理,以传统钻削加工为参照,分别利用螺旋铣削及传统钻削两种方法对C/E复合材料进行制孔试验,并对螺旋铣削与传统钻削刀具的运动轨迹进行分析。在具有相同的加工效率及刀具切削速度的基础上,对两种加工方法的加工参数进行优化。进行制孔对比试验,并对制孔过程中的切削温度、切削力及加工质量进行检测与分析。结果表明,切削温度是影响C/E复合材料制孔质量的重要因素,且由于螺旋铣削制孔时的切削温度显著低于传统钻削制孔温度,因此螺旋铣削制孔质量明显优于传统钻削制孔质量。螺旋铣削制孔时的切削温度较传统钻削时降低69℃以上,降幅大于36%,因此有效避免了制孔出口处的撕裂及分层现象。     

Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a regression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number.     

A new model of the antifriction effect on wiredrawing with ultrasound

In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm optimization-based neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting conditions of Ti[C,N] mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced alumina-based ceramic cutting tool (CC670) on machining glass fibre-reinforced plastic (GFRP) composite. The objective considered was the minimization of unit production cost subjected to various machine constraints. An orthogonal design and analysis of variance was employed to determine the effective cutting parameters on the tool life. Neural network helps obtain a fairly accurate prediction, even when enough and adequate information is not available. The GA-NN and PSO-NN models were compared for their performance. Optimal cutting conditions obtained with the PSO-NN model are the best possible compromise compared with the GA-NN model during machining GFRP composite using alumina cutting tool. This model also proved that neural networks are capable of reducing uncertainties related to the optimization and estimation of unit production cost.     

Optimal Neural Network Model for Characterization of Process-Induced Damage in Drilling Carbon Fiber Reinforced Epoxy Composites

In this paper, a method for robust design of a neural network (NN) model for prediction of delamination (Da), damage width (Dw), and hole surface roughness (Ra) during drilling in carbon fiber reinforced epoxy (BMS 8-256) is presented. This method is based on a parametric analysis of neural network models using a design of experiments approach. The effects of number of neurons (N), hidden layers (L), activation function (AF), and learning algorithm (LA) on the mean square error (MSE) of model prediction are quantified. Using the aforementioned method, a robust NN model was developed that predicted process-induced damage with high accuracy.     

Optimal Neural Network Model for Characterization of Process‐Induced Damage in Drilling Carbon Fiber Reinforced Epoxy Composites

In this paper, a method for robust design of a neural network (NN) model for prediction of delamination (Da), damage width (Dw), and hole surface roughness (Ra) during drilling in carbon fiber reinforced epoxy (BMS 8‐256) is presented. This method is based on a parametric analysis of neural network models using a design of experiments approach. The effects of number of neurons (N), hidden layers (L), activation function (AF), and learning algorithm (LA) on the mean square error (MSE) of model prediction are quantified. Using the aforementioned method, a robust NN model was developed that predicted process‐induced damage with high accuracy.     

Examination and modification of equivalent delamination factor for assessment of high speed drilling

Composite components suffer delamination at the entrance and exit of drilled holes. Many measures have been suggested by different researchers to assess such delamination damage. These include delamination factor, two-dimensional delamination factor, damage ratio, adjusted delamination factor, refined delamination factor, equivalent delamination factor, and minimum delamination factor. Among all these various assessment factors, the equivalent delamination factor looks simple and able to take into account the different features of delamination. However, the method of calculation of the equivalent delamination factor may not provide accurate values for delamination resulting from high speed drilling. The aim of this paper is to evaluate the equivalent delamination factor in high speed drilling of a composite laminate using a twist drill and develop a new approach to determine equivalent delamination factor which can be used for both conventional and high speed drilling conditions. This new method is applied to calculate the equivalent delamination factor in trials of drilling composite specimens at different speeds and feed rates and is found suitable.

     

Effect of tool wear on delamination in drilling composite materials

Among all machining operations, drilling using twist drill is the most frequently applied for secondary machining of composite materials owing to the need for structure joining. Delamination is mostly considered as the principal failure model in drilling of composite materials. Drill wear is a serious concern in hole-making industry, as it is necessary to prevent damage of cutting tools, machine tools and workpieces. The industrial experience shows the worn drill causes more delamination. This paper presents a comprehensive analysis of delamination caused by the drill wear for twist drill in drilling carbon fiber-reinforced composite materials. The critical thrust force at the onset of delamination for worn drill is predicted and compared with that of ideal drill. The experimental results demonstrate that though the critical thrust force is higher with increasing wear ratio, the delamination becomes more liable to occur because the actual thrust force increases to larger extent, as the thrust factor (Z) illustrates. Compared to sharp drill, the worn twist drill allows for lower feed rate below which the delamination damage can be avoided.     

Delamination-free drilling of carbon fiber reinforced plastic with variable feed rate

A large number of drilling have been performed to assemble aircraft parts of carbon fiber reinforced plastic (CFRP). Although high quality is required in machining the holes with high productivity in terms of reliability of parts, delamination often occurs around the holes in drilling. This paper presents a novel drilling method with variable feed rate to machine the delamination-free holes at a high machining rate. In the drilling, the holes are machined at the standard feed rates when the chisel moves in material; and are finished with the negative thrust at higher feed rates after the chisel exits from the workpiece. Orthogonal cutting tests were conducted to measure the cutting forces and the friction angles for the uncut chip thicknesses and the rake angles. The negative thrusts were measured in large uncut chip thicknesses at large rake angles of the lips. Then, the drilling tests were conducted to verify the change in the cutting force in the variable feed rate drilling up to 100 holes. Negative thrust component appears consistently to raise the workpiece up in the exit process even though the tool wear progresses with repeating drillings. As a result, the variable feed rate drilling remarkably controls delamination compared to the constant feed rate drilling in the 100th drilling. The cutting process in the variable feed rate drilling is compared with the constant feed rate drilling in a cutting force model based on the minimum cutting energy. The negative thrust is verified when the friction angle becomes smaller than the effective rake angle with increasing the feed rate.     

CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION

A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel OCr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.     

基于人工神经网络的高锰钢钻削力预测模型研究

对制造铁路道岔用ZGMn13高锰钢这种难加工材料进行了钻削实验,依据多因素正交试验数据样本,通过MATLAB人工神经网络建立了高锰钢钻削力和扭矩的预测模型。在模型中输入刀具直径、进给量和切削速度等参数,可得到相应的钻削轴向力和扭矩。采用该模型能够大量节约实验成本,为高锰钢的钻削机理研究提供新的手段和依据。     

Construction of a surface roughness prediction model for high speed machining

In manufacturing environment prediction of surface roughness is very important for product quality and production time. For this purpose, the finite element method and neural network is coupled to construct a surface roughness prediction model for high-speed machining. A finite element method based code is utilized to simulate the high-speed machining in which the cutting tool is incrementally advanced forward step by step during the cutting processes under various conditions of tool geometries (rake angle, edge radius) and cutting parameters (yielding strength, cutting speed, feed rate). The influences of the above cutting conditions on surface roughness variations are thus investigated. Moreover, the abductive neural networks are applied to synthesize the data sets obtained from the numerical calculations. Consequently, a quantitative prediction model is established for the relationship between the cutting variables and surface roughness in the process of high-speed machining. The surface roughness obtained from the calculations is compared with the experimental results conducted in the laboratory and with other research studies. Their agreements are quite well and the accuracy of the developed methodology may be verified accordingly. The simulation results also show that feed rate is the most important cutting variable dominating the surface roughness state.     

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 (prediction) 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 feed 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.     

THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM UNDER CHANGING CUTTING CONDITIONS

The process of metal cutting is a complex phenomenon that has been researched for many years but the aim of practical cutting tool condition monitoring has yet to be achieved. Previous work by the current authors using two neural networks (to classify acquired data) moderated by an Expert System (based on Taylor's tool life equation) has shown that it is possible to accurately monitor tool wear with a single machine/tool/material/cutting condition combination and to identify any inconsistencies between the predictions of the neural networks and engineering practice. This paper investigates the effects that minor inconsistencies in cutting conditions might have on such a system by determining the ‘zone of influence’ of this working system by systematically varying the cutting conditions whilst keeping all other variables fixed. The investigation has found that the zone of influence is small but usable, and an approach to the utilisation of the system in a machine shop is suggested.     

Analytical models of composite material drilling

Drilling of composite material structures is widely used for aeronautical assemblies. When drilling, damage to the composite laminate is directly related to the cutter geometry and the cutting conditions. Delamination of the composite materials at the hole exit as directly related to the axial force (F _Z) of the cutter is considered to be the major such defect. To address this issue, an orthotropic analytical model is developed in order to calculate the critical force of delamination during drilling and a number of hypotheses for loading are proposed. This critical axial load is related to the delamination conditions (propagation of cracks in the last layers) and the mechanical characteristics of the composite material machined. A numerical model is also drawn up to allow for numerical validation of the analytical approach. A comparison between these analytical and numerical modellings and experimental results from quasi-static punch tests led to the choice of the loading hypothesis closest to the experimental conditions. The selection of corresponding load permits to model the drilling critical thrust force on delamination and then to optimise the cutting conditions. The dimensions and geometrical shape of the cutter are of considerable importance when it comes to choosing this load. The present article focuses on the case of the twist drill, which is commonly used to drill thick plates. However, this work can be adapted to different cutter geometries.     

Edge chipping mechanism and failure time prediction on carbide cemented tool during drilling of CFRP/Ti stack

To investigate the edge chipping during drilling of the CFRP/Ti stack with carbide cemented tools, a drilling experiment was carried out and a tool failure model was proposed. Thrust force, drilling temperature, and tool wear were analyzed. A tool stressing model and a tool failure model of edge chipping were constructed respectively. On the basis of these, the prediction model on the edge chipping was established to forecast the failure time. Drilling temperature, Vickers hardness, and cutting speed were considered during the prediction model building. The results demonstrate that adhesive wear has a great influence on the edge chipping. The damage of adhesive wear for tool rake face leads to the load variation on rake face and the initial crack. Under the action of shear stress, the crack starts at rake face and then expands to the flank face, resulting in tool edge chipping. The affinity interaction (between titanium alloy with carbide cemented) and the thermal residual stress are two critical factors for tool edge chipping. Tear easily occurs inside the binding phase or at the boundary between hard phase and binder phase. As the drilling temperature increases, the hardness of the carbide cemented will gradually decrease. The prediction result of failure time is similar to the experimental result, and the effectiveness of the prediction model is verified.     

采用小波神经网络的刀具故障诊断

为了有效的进行刀具状态监测，采用小波神经网络的松散型结合对刀具进行故障诊断。通过小波变换提取刀具磨损声发射（AE）信号的特征．即对AE信号进行小波分解，提取了5个频段的均方根值作为神经网络的输入，来识别刀具磨损状态。试验表明，均方根值完全可以作为刀具磨损过程中产生AE信号的特征向量。仿真结果表明，基于小波神经网络的刀具故障诊断对刀具磨损状态的识别效率高．该方法是有效的。     

Measurement and analysis of thrust force in drilling of particle board (PB) composite panels

Particleboard is a wood based composite extensively used in wood working. Drilling is the most commonly used machining process in furniture industries. The surface characteristics and the damage free drilling are significantly influenced by the machining parameters. The thrust force developed during drilling play a major role in gaining the surface quality and minimizing the delamination tendency. The objective of this study is to measure and analyze the cutting conditions which influences the thrust force in drilling of particle board panels. The parameters considered are spindle speed, feed rate and point angle. The drilling experiments are performed based on Taguchi’s design of experiments and a response surface methodology (RSM) based mathematical model is developed to predict the influence of cutting parameters on thrust force. The results showed that high spindle speed with low feed rate combination minimizes the thrust force in drilling of pre-laminated particle board (PB) panels.     

基于CFRP切削过程仿真的面下损伤形成分析

由于碳纤维增强树脂基复合材料(Carbon fiber reinforced plastic,CFRP)宏观上呈现非均质、各向异性,细观上表现为纤维和树脂的特殊混合形态,导致其制件加工过程中极易产生分层、开裂等损伤,严重影响其制件的加工精度及承载性能。研究CFPR加工损伤产生机理并以此降低加工损伤是提高其加工质量的关键。基于宏观各向异性本构、Hashin失效起始准则及损伤演化,建立了可实现任意纤维角度单向板连续动态切削过程仿真分析的直角切削有限元模型,分析了任意纤维角度CFRP单向板连续切削过程面下损伤,得到了纤维角度、切削参数、刀具结构对面下损伤深度的影响规律。具体结果:纤维角度为影响面下损伤的主要因素,随纤维角度增大,切削力增大同时面下损伤深度也明显增加;面下损伤的主要原因为切削力过大导致的基体破坏及扩展;对于135°单向板面下损伤深度随刀具前角增大呈先增大后减小的趋势。     

模具钢铣削中刀具磨损的试验研究

以面铣刀刀片磨损为研究对象,结合类神经网络系统建构高速数控铣削加工的预测模型。以加工参数为模型输入条件,刀腹磨耗为输出条件。采用多因素试验方法,选择切削速度、进给速度、切削深度三个试验参数,利用直交表式的试验计划法设计试验点。依照试验点铣削工件后再测量刀具加工后的刀腹磨耗量,进而求得倒传递网络所需的36组训练范例与11组验证数据。刀腹磨耗预测模式是利用类神经网络中的倒传递网络原理,以田口法求得倒传递网络参数的最优值。试验结果显示,刀腹磨耗随着切削速度、进给速度、切削深度增加而上升。铣削模具钢后,刀具磨耗预测值的平均误差为4.72%,最大误差为11.43%,最小误差为0.31%。整体而言,类神经网络对于铣削加工可进行有效预测。