正交切削加工过程仿真

为优化正交切削加工参数,采用弹-塑热耦合有限元方法,建立正交切削加工有限元模型.应用DEFORM软件,模拟出45号钢件切削过程中变形区内温度、应力、应变以及切削力的分布,采用自适应网格重划技术避免大塑性变形引起的网格畸变.该仿真结果能对切削加工参数的选择及实际的切削加工提供指导.     

Advance in detection system to improve the stability and capability of CNC turning process

This paper presents an advance in detection of cutting states of the previous work to increase the capability of CNC turning process. A proposed method has been improved and developed to monitor and detect the cutting states based on proposed pattern recognition technique for CNC turning process within the small data-processing time by utilizing the dynamic cutting forces. The proposed method introduces three parameters, which are obtained by taking the ratio of the average variances of the dynamic cutting forces, to classify the cutting states of the continuous chip, the broken chip, the mixed broken chip, the chatter and the chatter occurred with broken chip. Among those cutting states, the broken chip and the mixed broken chip are required to improve the stability and capability of turning process. The algorithm was developed to calculate the values of three parameters in order to obtain the three-dimensional reference feature spaces and the proper threshold values for identification of the cutting states. An improvement of the proposed method is proved by series of cutting tests that the states of cutting are well detected regardless of any cutting conditions. The broken chips are obtained easily by changing the cutting conditions during the process. Finally, the effect of cutting conditions on the morphology of chips is also discussed.     

一种求解圆形下料问题的快速算法

本文研究圆形件优化排样算法,目的是提高材料利用率。本文提出了一种新的放置算法(圆弧搜索算法,ASA),与文献中算法相比,ASA在较短的时间内产生了可以和排样领域著名的法国学者Hifi在SCI和EI检索刊物中提出的较复杂方法GA-BH在利用率方面相媲美的效果;对随机生成例题的计算结果表明,本文算法的计算时间可以满足一般实践应用的要求,所得排样方案的材料利用率较高。     

一种求解圆形下料问题的快速算法

本文研究圆形件优化排样算法,目的是提高材料利用率。本文提出了一种新的放置算法(圆弧搜索算法,ASA),与文献中算法相比,ASA在较短的时间内产生了可以和排样领域著名的法国学者Hifi在SCI和EI检索刊物中提出的较复杂方法GA-BH在利用率方面相媲美的效果;对随机生成例题的计算结果表明,本文算法的计算时间可以满足一般实践应用的要求,所得排样方案的材料利用率较高。     

Strips minimization in two-dimensional cutting stock of circular items

Circular items are often produced from stock plates using the cutting and stamping process that consists of two stages. A guillotine machine divides the plate into strips at the cutting stage, and then a press punches out the items from the strips at the stamping stage. The cutting cost at the first stage often increases with the number of strips in the cutting plan. An approach is presented for the two-dimensional cutting stock problem of the strips at the cutting stage. The objective is to minimize the sum of the material and the cutting costs. The approach formulates the problem as an integer linear programming, and uses a column generation method for generating the cutting patterns. The cutting patterns have the feature that each cut on the plate produces just one strip. The computational results indicate that the approach can greatly reduce the number of strips in the cutting plan.     

鱼糜斩拌机刀片优化设计的计算机仿真

斩拌机的刀片是斩拌机的重要零件，刀片的刃形和锋利程度直接影响到肉的切碎率和斩拌效果。目前，斩拌机刀片的刃形大多为圆弧形。由于圆弧形刀片在切割过程中，刀刃上各点的切割角度不同，影响了肉的切碎率，延长了斩拌机工作的时间，进而影响鱼糜的品质。论文分析了鱼糜斩拌机斩拌刀的运动规律以及斩拌时间与鱼糜品质的关系，建立了斩拌刀刃形优化曲线的数学模型，并且应用MATLAB数字仿真软件优化设计的刀刃刃形曲线进行了计算机仿真。     

面向可加工性的矩形件优化下料算法

针对目前矩形件优化下料算法侧重追求高材料利用率,而对实际切割成本考虑不足的现状,提出一种既维持高材料利用率,又使下料方案具有较低切割成本的矩形件优化下料算法。算法采用SVC框架和同质条带多级规范方式求解矩形件下料问题。利用条带共边排样的路径优化设计进行切割路径长度的计算,以生产成本（材料成本与切割成本之和）为优化目标得到高材料利用率、低切割成本的下料方案,最后通过实验证实该算法的可行性与有效性。     

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.     

Prediction of stable cutting depths in turning operation using soft computing methods

This article suggests soft computing methods to predict stable cutting depths in turning operations without chatter vibrations. Chatter vibrations cause poor surface finish. Therefore, preventing these vibrations is an important area of research. Predicting stable cutting depths is vital to determine the stable cutting region. In this study, a set of cutting experiments has been used and the stable cutting depths are predicted as a function of cutting, modal and tool-working material parameters. Regression analyses, artificial neural networks (ANN) decision trees and heuristic optimization models are used to develop the generalization models. The purpose of the models is to estimate stable cutting depths with minimum error. ANN produces better results compared to the other models. This study helps operators and engineers to perform turning operations in an appropriate cutting region without chatter vibrations. It also helps to take precautions against chatter.     

ForceNet: An offline cutting force prediction model based on neuro-physical learning approach

Accurate cutting force prediction serves as an important reference to the optimization of numerically controlled machining process. Traditional cutting force modeling via theoretical cutting mechanism hampers accurate prediction for actual machining process due to its highly suppressed modeling flexibility. On the other hand, machine learning based modeling approaches demand large amount of diversified labeled samples to achieve comparable prediction results, while collecting these samples can be tedious and costly because the cutter workpiece engagement (CWE) keeps changing during actual process. This paper presents a cutting force prediction model, named ForceNet, which incorporates elementary physical priori into structured neural networks to predict cutting force for end-milling process of complex CWE. The main idea is to use grayscale images to represent CWE geometry, providing a universal input to the ForceNet. Unlike traditional deep neural networks served as an unexplainable black box, the core of the ForceNet is constructed by the vector summation of directional primitive cutting force elements, which are approximated using elementary neural networks. Preliminary results indicate that ForceNet outperformed existing methods not only with greater prediction accuracy in unseen cutting situations, but also with less training data needed thanks to its inherent neuro-physical structure.     

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.     

Cutting on triangle mesh: local model-based haptic display for dental preparation surgery simulation

A new method to realize stable and realistic cutting simulation using an impedance display haptic device and microcomputer is presented in this paper. Material removal or cutting simulation is a critical task in dental preparation surgery simulation. In this paper, a piecewise contact force model is proposed to approximately describe the cutting process. Challenging issues of minimizing the difference between the cutting simulation and haptic contact simulation are analyzed. The proposed contact-based simulation method is developed for a one-dimensional cutting task and can be expanded to three-dimensional cases. Local model-based multirate simulation cutting architecture is proposed and force control of the haptic device is decoupled from the cutting simulation loop, which can both ensure high fidelity of dynamical simulation as well as maintain stability of the haptic device. The cutting operation is realized using spherical and cylindrical shaped tools. An experiment based on the Phantom desktop proves that fidelity in one-dimensional cutting can be realized and stability in three-dimensional cutting can be ensured using the force-filtering method.     

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification.     

Exact Solution of Cutting Stock Problems Using Column Generation and Branch-and-Bound

This paper describes an attempt to solve the one-dimensional cutting stock problem exactly, using column generation and branch-and-bound. A new formulation is introduced for the one-dimensional cutting stock problem that uses general integer variables, not restricted to be binary. It is an arc flow formulation with side constraints, whose linear programming relaxation provides a strong lower bound. In this model, a cutting pattern, which corresponds to a path, is decomposed into single arc variables. The decomposition serves the purpose of showing that it is possible to combine the branch-and-bound method with variable generation. Computational times are reported for one-dimensional cutting stock instances with a number of orders up to 30.     

基于多传感器的刀具状态监测系统

以铣削加工为对象,研究了多刃切削加工过程的刀具状态监测问题,从系统的角度分析了刀具状态的多传感器监测原理,并以此为依据确定了采用声发射（ＡＥ）传感器和动态切削力传感器可有效地监测加工过程。文中提出了一种多传感器信号的特征提取方法,该方法利用偏最小二乘法计算样本矩阵的本征值,根据置信因子确定特征维数。为验证该方法的有效性,建立了一个铣削加工实验系统,实验结果表明,该方法可在多种切削条件下获得较高的识别率。     

Prediction of cutting forces in 3-axes milling of sculptured surfaces directly from CAM tool path

In the present work a new approach for the modelling of milling is described. The cutting forces are calculated for milling operations directly from the tool path provided by a Computer Assisted Manufacturing program. The main idea consists in using tool position points coming from CAM data in order to calculate the local inclination angle of the generated surface and then the tool engagement in the machined material. A good approximation for global and local cutting forces can be obtained when an analytical model able to predict the cutting forces for 3-axes milling is used. Two approaches are proposed to calculate the local cutting forces to show the versatility of the method. The first method uses a thermomechanical approach using a Johnson & Cook constitutive law while the second is based on classical cutting coefficients. Some results are presented for wavelike form and free form machining tests and are compared with experimental data obtained in roughing and finishing of 42CrMo4 steel. Results are satisfactory and the capability of the method to predict the resultant surface roughness is shown.     

冲裁件条料最优四块剪切下料方案的生成算法

讨论冲裁件条料剪切下料方案的设计问题。下料方案由一组排样方式组成。首先构造一种生成条料最优四块排样方式的背包算法,然后采用基于列生成的线性规划算法迭代调用上述背包算法,每次都根据生产成本最小的原则改善目标函数并确定各种冲裁件的当前价值,按照当前价值生成一个新的排样方式,最后选择最优的一组排样方式组成下料方案。采用例题将该排样方式生成算法和文献中多段排样方式生成算法进行比较,实验计算结果表明,该算法得到的排样方式排样价值较高。最后通过文献中实例的下料方案求解,可以看出该算法解决实际下料问题是有效的。     

Modelling and simulation of whirling process based on equivalent cutting volume

The thread whirling is an efficient and precise machining process for manufacturing of screws. The shaping motion of whirling is complex and difficult to model. In this paper, a novel model basing on equivalent cutting volume is proposed. The cutting force and the chip morphology are investigated to validate the model. The simulation of cutting force is in good agreement with the experimental results with error less than 16.5%. A chip with saw-toothed edges is obtained from simulation and for experimental verification. A case study on the effect of the tool edge geometry on cutting forces is also presented. The simulation results show that the tool edge geometry greatly influences the cutting forces. The tool with round edge is a good choice for reducing the cutting forces. The ratio of a_c/R_e holds the balance in selecting the parameter of cutting conditions. The model is applicable for the simulation of whirling process and can be used for parameter optimisation of the cutting tool edge.     

Improved prediction of stability lobes in milling process using time series analysis

In this paper, a new method is presented for prediction of cutting forces, surface texture and stability lobes in end milling operation based on time series analysis. In the approach, an equivalent damping ratio is defined for the cutting zone while the damping ratio of non-cutting zone is determined by experimental modal analysis. Using correlation dimension criterion, the simulation and experimental force signals are compared to anticipate the value of process damping by assessing the variation of correlation dimension for both signals. The effect of cutter deflections and run out are taken into account. Moreover, the stability lobes are predicted by considering the variation of process damping with cutting conditions. The feasibility of the proposed algorithm is verified experimentally for machining of Aluminum 7075-T6. Comparison of experiment results against simulation results indicates that the improved model can accurately predict cutting forces, surface texture and stability lobes for low radial immersion.     

Using FEM simulations of cutting for evaluating the performance of different johnson cook parameter sets acquired with inverse methods

Material model parameters are the primary source of error in the finite element analysis (FEM) of cutting processes. Expensive and time consuming material testing is required in order to describe the material's behavior in high temperature and high strain rate conditions during cutting. An alternative approach has been suggested in research papers; inverse analysis using cutting experiments together with FE analysis or analytical models. The latest approach is to combine an analytical model together with a material model capable of describing flow stress in terms of strain, strain rate and temperature, and using cutting experiments to acquire input parameters for inverse analysis, from which the material model parameters can be solved. In this paper, performance evaluation is done for five different sets of Johnson Cook parameters for AISI 1045, acquired with materials testing, inverse analysis with FEM, and the proposed combined inverse analysis with an analytical model and cutting experiments. The performance is evaluated by running simulations with a wide range of cutting parameters and comparing the simulated results of cutting forces and temperature to known experimental results found in literature. It was found that the proposed inverse method produces better performing model parameters than those found in literature.