Modeling of cutting geometry and forces for 5-axis sculptured surface machining

5-Axis sculptured surface machining is simulated using discrete geometric models of the tool and workpiece to determine the tool contact area, and a discrete mechanistic model to estimate the cutting forces. An extended Z-buffer model represents the workpiece, while a discrete axial slice model represents the cutting tool. Determination of the contact area for a given tool move requires a swept envelope (SWE) of the tool path. The SWE is used to find the intersections of the tool envelope with Z-buffer elements (ZDVs) representing the workpiece. A 3-axis approximation of the 5-axis tool movement is used to simplify the calculations while maintaining a desired level of accuracy. The intersection of the SWE with each ZDV yields segments which are used to find the contact area between the cutter and the workpiece for a given tool path. The contact area is subsequently used with the discrete force model to calculate the vector cutting force acting on the tool.     

Mechanistic modelling of the milling process using an adaptive depth buffer

A mechanistic model of the milling process based on an adaptive and local depth buffer is presented. This mechanistic model is needed for speedy computations of the cutting forces when machining surfaces on multi-axis milling machines. By adaptively orienting the depth buffer to match the current tool axis, the need for an extended Z-buffer is eliminated. This allows the mechanistic model to be implemented using standard graphics libraries, and gains the substantial benefit of hardware acceleration. Secondly, this method allows the depth buffer to be sized to the tool as opposed to the workpiece, and thus improves the depth buffer size to accuracy ratio drastically. The method calculates tangential and radial milling forces dependent on the in-process volume of material removed as determined by the rendering engine depth buffer. The method incorporates the effects of both cutting and edge forces and accounts for cutter runout. The simulated forces were verified with experimental data and found to agree closely. The error bounds of this process are also determined.     

Process planning for Floor machining of 2½D pockets based on a morphed spiral tool path pattern

This work proposes a process planning for machining of a Floor which is the most prominent elemental machining feature in a 2½D pocket. Traditionally, the process planning of 2½D pocket machining is posed as stand-alone problem involving either tool selection, tool path generation or machining parameter selection, resulting in sub-optimal plans. For this reason, the tool path generation and feed selection is proposed to be integrated with an objective of minimizing machining time under realistic cutting force constraints for given pocket geometry and cutting tool. A morphed spiral tool path consisting of G¹ continuous biarc and arc spline is proposed as a possible tool path generation strategy with the capability of handling islands in pocket geometry. Proposed tool path enables a constant feed rate and consistent cutting force during machining in typical commercial CNC machine tool. The constant feed selection is based on the tool path and cutting tool geometries as well as dynamic characteristics of mechanical structure of the machine tool to ensure optimal machining performance. The proposed tool path strategy is compared with those generated by commercial CAM software. The calculated tool path length and measured dry machining time show considerable advantage of the proposed tool path. For optimal machining parameter selection, the feed per tooth is iteratively optimized with a pre-calibrated cutting force model, under a cutting force constraint to avoid tool rupture. The optimization result shows around 32% and 40% potential improvement in productivity with one and two feed rate strategies respectively.     

Process planning for Floor machining of 2½D pockets based on a morphed spiral tool path pattern

This work proposes a process planning for machining of a Floor which is the most prominent elemental machining feature in a 2½D pocket. Traditionally, the process planning of 2½D pocket machining is posed as stand-alone problem involving either tool selection, tool path generation or machining parameter selection, resulting in sub-optimal plans. For this reason, the tool path generation and feed selection is proposed to be integrated with an objective of minimizing machining time under realistic cutting force constraints for given pocket geometry and cutting tool. A morphed spiral tool path consisting of G¹ continuous biarc and arc spline is proposed as a possible tool path generation strategy with the capability of handling islands in pocket geometry. Proposed tool path enables a constant feed rate and consistent cutting force during machining in typical commercial CNC machine tool. The constant feed selection is based on the tool path and cutting tool geometries as well as dynamic characteristics of mechanical structure of the machine tool to ensure optimal machining performance. The proposed tool path strategy is compared with those generated by commercial CAM software. The calculated tool path length and measured dry machining time show considerable advantage of the proposed tool path. For optimal machining parameter selection, the feed per tooth is iteratively optimized with a pre-calibrated cutting force model, under a cutting force constraint to avoid tool rupture. The optimization result shows around 32% and 40% potential improvement in productivity with one and two feed rate strategies respectively.     

Comparing and combining off-line feedrate rescheduling strategies in free-form surface machining with feedrate acceleration and deceleration

Off-line rescheduling feedrates based on changing surface geometry in free-form surface machining could reduce machining time, increase tool life, and improve surface finish quality. Various feedrate rescheduling strategies have different feedrate rescheduling control parameters. These parameters could be chip thickness, metal removal rate (MRR), or resultant forces. The paper compares these feedrate rescheduling strategies in machining time and feedrate changes. Machining time could be reduced significantly if appropriate values are set for those control parameters. Further, various strategies are combined for better results in two ways: the minimum and the maximum feedrate combination. Machining time could be reduced with both feedrate combinations. The minimum feedrate combination could protect machining against excessive chip thickness, MRR, resultant force, or other conditions that may occur using only one of these rescheduling strategies. The maximum feedrate combination could further reduce machining time with a loose control on those conditions. The paper also points out that advantages of feedrate rescheduling may not been in real if distances between neighboring cutting locations are too small for the machine to arrive at rescheduled feedrates in sharply changing cutting directions and feedrates. The method to calculate real feedrates and machining time is provided in consideration with feedrate ac/deceleration.     

快速刀具伺服系统自抗扰控制的研究与实践

快速刀具伺服系统(fast tool servo, FTS)是实现非圆截面和非轴对称表面零件加工的关键部件. 加工过程中,FTS应克服时变切削力负载和自身参数的非线性, 驱动刀具完成高频高精度跟踪运动. 为了解决FTS的快速精密跟踪控制问题, 根据刀具运动参考轨迹已知的特点, 应用自抗扰控制原理和前馈控制策略, 针对基于剪应力和正应力电磁驱动的两种直线执行机构, 分别设计了采用线性和非线性扩张状态观测器的自抗扰控制器, 并利用传递函数和描述函数方法, 分析了线性控制器的跟踪精度和动态刚度特性, 探讨了非线性控制系统的极限环问题. 两种基于自抗扰控制的快速刀具伺服系统已应用于发动机椭圆截面活塞的精密车削和二维正弦微结构表面的超精密车削,满足了加工需求. 研究与应用结果表明: 自抗扰控制思想独特、算法易于工程实现, 具有很好的工程应用价值.     

Advanced adaptive feed control for CNC machining

In computer numerical control (CNC) machining, the tool feed rate is crucial for determining the machining time. It also affects the degree of tool wear and the final product quality. In a mass production line, the feed rate guides the production cycle. On the other hand, in single-time machining, such as for molds and dies, the tool wear and product quality are influenced by the length of machining time. Accordingly, optimizing the CNC program in terms of the feed rate is critical and should account for various factors, such as the cutting depth, width, spindle speed, and cutting oil. Determining the optimal tool feed rate, however, can be challenging given the various machine tools, machining paths, and cutting conditions involved. It is important to balance the machining load by equalizing the tool's load, reducing the machining time during no-load segments, and controlling the feed rate during high load segments. In this study, an advanced adaptive control method was designed that adjusts the tool feed rate in real time during rough machining. By predicting both the current and future machining load based on the tool position and time stamp, the proposed method combines reference load control curves and cutting characteristics, unlike existing passive adaptive control methods. Four different feed control methods were tested including conventional and proposed adaptive feed control. The results of the comparative analysis was presented with respect to the average machining load and tool wear, the machining time, and the average tool feed speed. When the proposed adaptive control method was used, the production time was reduced up to 12.8% in the test machining while the tool life was increased.     

A sensor fusion and support vector machine based approach for recognition of complex machining conditions

During the machining process of thin-walled parts, machine tool wear and work-piece deformation always co-exist, which make the recognition of machining conditions very difficult. Existing machining condition monitoring approaches usually consider only one single condition, i.e., either tool wear or work-piece deformation. In order to close this gap, a machining condition recognition approach based on multi-sensor fusion and support vector machine (SVM) is proposed. A dynamometer sensor and an acceleration sensor are used to collect cutting force signals and vibration signals respectively. Wavelet decomposition is utilized as a signal processing method for the extraction of signal characteristics including means and variances of a certain degree of the decomposed signals. SVM is used as a condition recognition method by using the means and variances of signals as well as cutting parameters as the input vector. Information fusion theory at the feature level is adopted to assist the machining condition recognition. Experiments are designed to demonstrate and validate the feasibility of the proposed approach. A condition recognition accuracy of about 90 % has been achieved during the experiments.     

Correlation between machining direction,cutter geometry and step-over distance in 3-axis milling: Application to milling by zones

Computer-Aided Manufacturing (CAM) occupies an increasingly important role in engineering with all it has to offer in terms of new possibilities and improving designer/manufacturer productivity. The present study addresses machining of free-form surfaces on a 3-axis NC machine tool. There have recently been a large number of studies devoted to planning tool paths on free-form surfaces with various strategies being adopted. These strategies are intended to increase efficiency by reducing the overall length of machining. Often, the choice of the cutter is arbitrary and the work focuses on planning. In order to boost productivity, the present work offers assistance in choosing the cutting tool, the machining direction and cutting by surface zones, adopting a milling strategy by parallel planes. To do so, a comparison is made between milling using a spherical end milling cutter and a torus end milling cutter with the same outer radius. This comparison relates to the radius of curvature of the trace left by the cutter at the point of contact between the tool and the workpiece in relation to the direction of feed motion.     

Open architecture of CNC system research based on CAD graph-driven technology

Based on CAD graph-driven technology, a kind of novel open architecture CNC system is put forward and designed together with the key hardware “PC+PMAC controller”. The intelligent CNC system software including several function modules is developed under Visual C++6.0 environment. Graphic feature identification and geometric parameter extraction from CAD-part-drawing saved as DXF format are performed to control the relative motion between cutting tool and part. The ant-colony algorithm is applied to auto-optimize the cutting tool paths in machining process. The experiment results of a plane engraving machining example show that the proposed method is feasible, and the entire machining process no longer needs NC programming. The efficiency of CNC machining is improved greatly, and the true intelligent CNC machining can be realized when the advanced programming technologies are integrated in one system according to the proposed conception.     

A knowledge base model for complex forging die machining

Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometrical features. Technological data and topological relations are aggregated to a geometrical feature in order to create machining features. Technological data, such as material, surface roughness and form tolerance are defined during forging process and dies design. These data are used to choose cutting tools and machining strategies. Topological relations define relative positions between the surfaces of the die CAD model. After machining features identification cutting tools and machining strategies currently used in HSM of forging die, are associated to them in order to generate machining sequences. A machining process model is proposed to formalize the links between information imbedded in the machining features and the parameters of cutting tools and machining strategies. At last machining sequences are grouped and ordered to generate the complete die machining process. In this paper the identification of geometrical features is detailed. Geometrical features identification is based on machining knowledge formalization which is translated in the generation of maps from STL models. A map based on the contact area between cutting tools and die shape gives basic geometrical features which are connected or not according to the continuity maps. The proposed approach is illustrated by an application on an industrial study case which was accomplished as part of collaboration.     

Theoretical predictions and experimental validations on machining the Nimonic C-263 super alloy

The current paper presents the simulated 3D Finite Element Model (FEM) and experimental validation while turning the Nimonic C-263 super alloy using a cemented carbide cutting tool. FEM machining simulations was carried out using a Lagrangian finite element based machining model to predict the tangential cutting force, temperature distribution at tool tip and the effective stress and strain. All simulations were performed according to the cutting conditions designed, using the orthogonal array. The work piece was considered as perfectly plastic and its shape was taken as a curved model. An experimental validation of the cutting process was conducted in order to verify the simulated results of tangential cutting force and temperature at tool tip and the comparison shows that the percentage error 6% was observed and the shear friction factor 0.6 indicates good agreement between the simulated results and the experiment results. As the cutting speed is increased from 22 m/min to 54 m/min at higher feed rate, a larger strain to an extent of up to 6.55 mm/mm, a maximum value of 810 MPa stress and higher temperature localization to an extent of 620 °C at tool tip were observed.     

人工神经网络预测刀具磨损和切削力

刀具磨损和切削力预测与控制是切削加工过程中需要考虑的重要问题.本文介绍了利用人工神经网络模型预测刀具磨损和切削力的步骤并且针对产生误差的因素进行分析.首先将切削速度、切削深度、切削时间、主轴转速和不同频带的能量值通过归一化法处理,作为输入特征值,对改进的神经网络模型进行训练.然后利用训练完成的神经网络模型预测刀具磨损和切削力.结果表明:神经网络模型能够综合考虑加工过程中更多的影响因素,与经验公式结果对比,具有更高的预测精度.研究结果表明神经网络模型预测刀具磨损和切削力具有可行性和准确性,为刀具结构的优化及加工参数的选择提供了依据.     

Modelling machining processes with a view to their optimization and to the adaptive control of metal cutting machine tools

This paper describes an orthogonal machining theory which can be used to determine the stresses, temperatures etc. involved in chip formation from a knowledge of the work material flow stress and thermal properties and cutting conditions. It is shown how these can be used to predict machinability factors such as power consumption, built-up edge range, tool wear rates (tool life) and those cutting conditions which cause plastic deformation of the cutting edge. An oblique machining theory which is more representative of practical machining processes than the orthogonal theory is then described, taking into account machining on more than one cutting edge as in bar turning. Throughout the paper comparisons are made between predicted and experimental results.     

Optimization of multi-task turning operations under minimal tool waste consideration

Most of the literatures on machining economics problems tend to focus on single cutting operations. However, in reality most parts that need to be machined require more than one operation. In addition, machining technology has been developed to the point that a single computer numerical control (CNC) machine is capable of performing multiple operations, even simultaneously, employing multiple spindles and cutting tools. When several operations are performed on a CNC turning machine, various tools are required for the cutting operations. Determining the life of these cutting tools under different machining conditions is an arduous task for the operators. They usually replace the tools based on their experience or according to the specific cutting tool handbook. Frequent tool replacements may result in wasted tools and tool utilization, while infrequent tool replacements may result in poorly machined parts. In this study we propose a mathematical model in which several different turning operations (turning, drilling, and parting) with proper constraints are performed. The issue of tool replacement is taken into account in the proposed cutting model. In addition, an evolutionary strategy (ES)-based optimization approach is developed to optimize the cutting conditions of the multiple turning-related operations while taking into account the minimizing unit cost criteria under the economical tool replacement strategy.     

Neural control strategy of constant cutting force system in end milling

This paper discusses the application of neural adaptive control strategy to the problem of cutting force control in high speed end milling operations. The research is concerned with integrating adaptive control and a standard computer numerical controller (CNC) for optimizing a metal-cutting process. It is designed to adaptively maximize the feed rate subject to allowable cutting force on the tool, which is very beneficial for a time consuming complex shape machining. The purpose is to present a reliable, robust neural controller aimed at adaptively adjusting feed rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate. Numerous simulations and experiments are conducted to confirm the efficiency of this architecture.     

Model-based tool condition prognosis using power consumption and scarce surface roughness measurements

In machining processes, underusing and overusing cutting tools directly affect part quality, entailing economic and environmental impacts. In this paper, we propose and compare different strategies for tool replacement before processed parts exceed surface roughness specifications without underusing the tool. The proposed strategies are based on an online part quality monitoring system and apply a model-based algorithm that updates their parameters using adaptive recursive least squares (ARLS) over polynomial models whose generalization capabilities have been validated after generating a dataset using theoretical models from the bibliography. These strategies assume that there is a continuous measurement of power consumption and a periodic measurement of surface roughness from the quality department (scarce measurements). The proposed strategies are compared with other straightforward tool replacement strategies in terms of required previous experimentation, algorithm simplicity and self-adaptability to disturbances (such as changes in machining conditions). Furthermore, the cost of each strategy is analyzed for a given benchmark and with a given batch size in terms of needed tools, consumed energy and parts out of specifications (i.e., rejected). Among the analyzed strategies, the proposed model-based algorithm that detects in real-time the optimal instant for tool change presents the best results.     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

Calibration-based tool condition monitoring for repetitive machining operations

Reliable tool condition monitoring (TCM) system is essential for any machining process in mass production to control the part quality as well as reduce the machine tool downtime and maintenance costs. However, while various research studies have proposed their TCM systems, the complexity in setups with advanced decision-making algorithms and specificity in application to limited cutting conditions continue to complicate the implementation of these systems into practical scenarios. This study develops a very simple and flexible TCM system for repetitive machining operations. The proposed monitoring approach reduces the complexity of monitoring model by considering the important characteristic of repeatability in process which has been commonly found in the mass production scenario and implements the calibration procedure to improve the flexibility of the model application to actual machining processes with complex toolpath designs and variable cutting conditions. The selected cutting tools with specific tool conditions are used in the calibration phase to generate reference signals. In actual repetitive production, the collected signal generated by the cutting tool in each operation is compared with reference signals to identify the most similar condition of the reference tool through the proposed similarity analysis. To validate the performance, the current study demonstrates the application of proposed monitoring approach to monitor the tool wear in repetitive milling operations with complex toolpath, and the predicted tool wear progression is found to be in good agreement with experimental measurements during the machining of multiple parts over the entire tool life.