基于几何特性的槽加工刀具选取算法

刀具选取是复杂零件数控加工编程的一项重要内容,为实现数控加工程序编制过程中自动选取加工刀具,提高数控加工及其编程效率和质量,结合飞机整体壁板数控加工编程及其粗加工特点,提出基于几何特性的槽加工刀具自动选取算法.在分析刀具与可切削区域间关系的基础上,对Voronoi Mountain定义域进行修正,建立切削轮廓45°拔模体;并给出刀具的可切削面积、残留不可切削面积,以及小刀具半径计算方法;最后根据整体加工时间最短的原则确定最优加工刀具.该算法已在"飞机壁板快速数控加工编程系统"项目中得以应用,结果证明了其是可行、有效的.     

基于VERICUT的数控加工程序切削参数优化

切削参数对加工效率和刀具的使用寿命影响极大,在笔者所在的陕西燎原航空机械制造公司,高强度钢,尤其是300M、30CrMnSiNi2A钢应用得最多.而300M、30CrMnSiNi2A钢的加工性能不是很好,为提高数控加工效率,并推广应用先进刀具就必须对切削参数进行优化.采用优化的切削参数可使刀具使用更为合理,从而提高数控加工效率和产品质量,减轻操作工的劳动强度,并能消除由于切削参数不当造成的零件过切超差.     

基于CATIA的高速铣削用数控编程方法

阐述了高速数控切削的加工机理,分析与普通数控加工的区别,对高速加工机床和刀具进行分析、选择,针对被加工材料的零件特点,合理制定工艺方案,探究最佳切削参数,应用三维CAM系统进行数控编程,探讨出数控加工CAM高速铣程序,编制高速加工的最佳工艺方案,确定切削参数、切削效率等的最优化的关键技术。     

数控高速切削的关键技术

本文阐述了高速数控切削的加工机理,分析了这种机理与普通数控加工的区别,对高速加工机床和刀具进行分析、选择,针对被加工材料的零件特点,合理制定工艺方案,探究最佳切削参数,应用三维CAM系统进行数控编程,探讨出数控加工CAM高速铣程序,编制高速加工的最佳工艺方案,确定切削参数、切削效率等的最优化的关键技术。     

嵌入式数控系统中3D加工仿真模块的实现

基于Cortex-A8和Android软硬件嵌入式数控平台,提出一种集成到中低端数控车床加工系统的仿真模块设计方法。在三角网格离散法建模的基础上,提出利用顶点平移算法计算三角片的顶点坐标值,大大减少复杂的三角函数计算量,并且基于强大的嵌入式三维图形Open GL-ES2.0接口绘制毛坯;基于NC代码特征解释译码,有效提取出刀具驱动数据信息;在刀具切削过程中,对扩展DDA圆弧插补算法进行改进,提高了切削精度。动态仿真测试表明:基于改进的算法进行加工仿真,3D效果逼真,插补点径向误差减小30%左右,加工仿真精度高。     

数控高速切削的关键技术

本文阐述了高速数控切削的加工机理,分析了这种机理与普通数控加工的区别,对高速加工机床和刀具进行分析、选择,针对被加工材料的零件特点,合理制定工艺方案,探究最佳切削参数,应用三维CAM系统进行数控编程,探讨出数控加工CAM高速铣程序,编制高速加工的最佳工艺方案,确定切削参数、切削效率等的最优化的关键技术。     

数控加工刀具变形误差补偿技术研究

在数控铣削加工的研究中,刀具变形引起的加工误差对工件的加工精度影响较大.研究了一种考虑切屑厚度影响的切削力模型,建立了由切削力引起的刀具变形加工误差的分析模型.为了提高加工精度,提出了一种线性迭代误差补偿算法,方法主要对由切削力引起的刀具变形产生的切削误差进行循环迭代补偿.通过应用数控加工中心,方案进行了验证,对补偿后...     

车床系统切削稳定性研究

切削系统是一个复杂的动态系统,其系统动态特性对加工质量、生产效率、生产成本乃至整个加工系统的安全寿命起着至关重要的影响和制约作用。对于普通机床而言,其切削过程中的抗振性和稳定性是最受用户关注的。本文以CA6136车床为研究对象,通过切削振动试验对机床进行了切削稳定性分析。设计并实施了切削振动试验,识别系统的颤振频率,并通过对刀具系统的模态锤击试验,确定了刀具系统为颤振的主动体。分析了切削加工中最常出现的再生型颤振的机理,建立了再生型颤振的动力学模型,并根据实验数据得到了该车床的切削稳定性叶瓣图。     

车床系统切削稳定性研究

切削系统是一个复杂的动态系统,其系统动态特性对加工质量、生产效率、生产成本乃至整个加工系统的安全寿命起着至关重要的影响和制约作用。对于普通机床而言,其切削过程中的抗振性和稳定性是最受用户关注的。本文以CA6136车床为研究对象,通过切削振动试验对机床进行了切削稳定性分析。设计并实施了切削振动试验,识别系统的颤振频率,并通过对刀具系统的模态锤击试验,确定了刀具系统为颤振的主动体。分析了切削加工中最常出现的再生型颤振的机理,建立了再生型颤振的动力学模型,并根据实验数据得到了该车床的切削稳定性叶瓣图。     

数控皮革裁剪机裁割运动控制系统研究及其实现

针对当前数控单层皮革裁剪机不适用于多层皮革的自动裁割,且效率较低,难以推广等问题,提出了多层数控皮革裁剪机裁割运动控制系统的软硬件总体结构设计方案;该方案根据多层数控皮革裁剪机的工作原理及其裁刀的运动特点,采用切向智能跟随运动控制插补算法,并基于嗣高运动控制器的控制平台,以VC++为软件开发工具,实现了其运动控制系统软件的开发,该运动控制系统在多层皮革裁剪机上试运行中,刀具切割皮革平滑,速度快,系统软件和硬件设计合理可行,具有一定的工程应用价值.     

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.     

Condition monitoring of CNC machining using adaptive control

In this work, an adaptive control constraint system has been developed for computer numerical control (CNC) turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint (ACC) machining systems for turning. AISI4140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.     

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.     

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.     

虚拟数控加工过程物理仿真模型的建立

该文首先阐述了虚拟数控加工过程物理仿真的研究内容,其次重点讨论了所建立的切削力仿真,刀具磨损仿真与变形仿真,加工误差仿真,振动仿真和切屑形成过程仿真的数学模型。最后,提出了物理仿真今后研究的方向。     

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.     

Advanced CNC system with in-process feed-rate optimisation

Tight quality requirements and stringent customer demands are the main thrust behind the development of new generation machine tool controllers that are more universal, adaptable and interoperable. The development of some international standards such as STEP and STEP-NC presents a vision for intelligent CNC machining. Implementation of STEP-NC enabled Machine Condition Monitoring (MCM) is presented in this paper. The system allows optimisation during machining in order to shorten machining time and increase product quality. In the system, an optiSTEP-NC, an AECopt controller and a Knowledge-Based Evaluation (KBE) module have been developed. The aim of the optiSTEP-NC system is to perform initial feed-rate optimisation based on STEP-NC data to assist process planners in assigning appropriate machining parameters. AECopt acts as a connector between the process planner and machining environment with the intention to provide adaptive and automatic in-process machining optimisation. KBE based-MTConnect is responsible for obtaining machining know-how. Optimisation is performed before, during or after machining operations, based on the data collected and monitored such as machining vibration, acceleration and jerk, cutting power and feed-rate.     

Intelligent Programming of CNC Turning Operations using Genetic Algorithm

CAD/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and generation of CNC programs for machine tools. The aim of our research is the design of a computer-aided, intelligent and genetic algorithm(GA) based programming system for CNC cutting tools selection, tool sequences planning and optimisation of cutting conditions. The first step is geometrical feature recognition and classification. On the basis of recognised features the module for GA-based determination of technological data determine cutting tools, cutting parameters (according to work piece material and cutting tool material) and detailed tool sequence planning. Material, which will be removed, is split into several cuts, each consisting of a number of basic tool movements. In the next step, GA operations such as reproduction, crossover and mutation are applied. The process of GA-based optimisation runs in cycles in which new generations of individuals are created with increased average fitness of a population. During the evaluation of calculated results (generated NC programmes) several rules and constraints like rapid and cutting tool movement, collision, clamping and minimum machining time, which represent the fitness function, were taken into account. A case study was made for the turning operation of a rotational part. The results show that the GA-based programming has a higher efficiency. The total machining time was reduced by 16%. The demand for a high skilled worker on CAD/CAM systems and CNC machine tools was also reduced. Received: September 2004 / Accepted: September 2005     

Characterization of closed-loop measurement accuracy in precision CNC milling

This study investigates the closed-loop measurement error in computer numerical controlled (CNC) milling as they relate to the different inspection techniques. The on-line inspection of machining accuracy using a spindle probe has an inherent shortcoming because the same machine-produced parts are used for inspection. In order to use the spindle probe measurement as a means of correcting deviations in machining, the magnitude of measurement errors needs to be quantified. The empirical verification was made by conducting three sets of cutting experiments, followed by a design of experiment with three levels and three factors on a state-of-the-art CNC machining center. Three different material types and parameter settings were selected to simulate a diverse cutting condition. During the cutting, the cutting force and spindle vibration sensor signals were collected and a tool wear was recorded using a computer vision system. The bore tolerance was gauged by a spindle probe as well as a coordinate-measuring machine. The difference between the two measurements was defined as a closed-loop measurement error and the subsequent analysis was performed to determine the significant factors affecting the errors. The analysis results showed the potential of improving production efficiency and improved part quality.