基于模块的机床CAD软件开发与实现

与传统的机床设计过程相比,计算机辅助设计很好地解决了计算的工作量,同时也缩短了设计的周期.基于模块的机床CAD软件,集成了机床设计的整个过程,该软件通过模块之间的相互通信,使软件具有强大的交互能力.本文主要介绍了软件各模块的组成与实现,以及各模块在实现与通信过程中使用的一些关键技术.     

Optimization of measuring points for machine tool thermal error based on grey system theory

Optimization of thermal sensors’ placement on machine tools based on grey correlation model of grey system theory is studied. After optimization, the temperature variables in the thermal error’ model are reduced from 16 to 4. It greatly reduces the time for variable searching and modelling and meanwhile it eliminates the coupling problems among temperature variables, so the robustness of the model could be increased and the predicting precision of the model is enhanced. Consequently, the real-time error compensation would be more effective and convenient.     

Progressive development of an absolute sensorless compensation system for cutting force-induced error

Cutting forces in traditional machining processes solely originates from the contact points on the cutting tool and workpiece. Therefore comprehensive mechanistic modeling of the machining process offers a means for realizing a sensorless cutting force monitoring system. This paper presents the progressive development of a sensorless compensation system for cutting force-induced error, whereby a learning and intelligent computer system is established, based on machining mechanics modeling and a reference compensation system. Experiences from normal machining sessions of new cutting tools and workpieces are modeled progressively and incorporated into the system. Finally with ample experience available, a full-fledged sensorless system is developed as a stand-alone solution. The sensorless system is economical, convenient, reliable and efficient. Administered on a CNC face milling machine, the model demonstrated exceptional performance and robustness.     

四轴数控机床误差综合建模原理及分析

详细分析了机床运动副的误差运动，利用基于齐次坐标变换的方法分析并给出了一台既包含移动副又包含转动副的四轴数控机床的误差综合数学模型，此模型中不仅包含了机床的几何误差且包含了热误差共计46个误差元素。本数学模型的建立方法可为其它类型的四轴及各种五轴数控机床的误差综合建模分析及误差补偿提供参考。     

Prediction of cutting force for self-propelled rotary tool using artificial neural networks

In this paper, a cutting force model for self-propelled rotary tool (SPRT) cutting force prediction using artificial neural networks (ANN) has been introduced. The basis of this approach is to train and test the ANN model with cutting force samples of SPRT, from which their neurons relations are gradually extracted out. Then, ANN cutting force model is achieved by obtaining all weights for each layer. The inputs to the model consist of cutting velocity V, feed rate f, depth of cut a_p and tool inclination angle λ, while the outputs are composed of thrust force F_x, radial force F_y and main cutting force F_z. It significantly reduces the complexity of modeling for SPRT cutting force, and employs non-structure operator parameters more conveniently. Considering the disadvantages of back propagation (BP) such as the convergence to local minima in the error space, developments have been achieved by applying hybrid of genetic algorithm (GA) and BP algorithm hence improve the performance of the ANN model. Validity and efficiency of the model were verified through a variety of SPRT cutting samples from our experiment tested in the cutting force model. The performance of the hybrid of GA–BP cutting force model is fairly satisfactory.