Monitoring of flank wear of coated tools in high speed machining with a neural network ART2

A monitoring system for classifying the levels of the tool flank wear of coated tools into some categories has been developed using an unsupervised and self-organizing artificial neural network, ART2. The input pattern used for the ART2 was an array of normalized mean wavelet coefficients of the feed force, which was affected by not only the flank wear but also the severe crater wear observed in high speed machining. The outputs of ART2 were classified into four or five categories of wear levels: the incipient stage, one or two intermediate stages, final stage and hazardous stage. For two apparently different series of input data obtained under the same cutting conditions, which are often experienced in the experiment, the ART2 neural network showed very similar classification of tool wear levels from the beginning to the end of cutting. Further study proved that this monitoring system detected the excessive wear in the hazardous stage for different cutting speeds 5–7 m/s and different feed rates 0.10–0.20 mm/rev.     

Inspection of micro-tools at high rotational speeds

Micro-tools have been widely used in industry, primarily by biomedical and electronic equipment manufacturers. The life of these cutting tools is extremely unrpedictable and much shorter than conventional tools. Also, these miniature tools, with a diameter of less than 1 mm, cannot be inspected by an operator without the aid of magnifying glass.

In this paper, evaluation of the intensity variation of a reflected laser light beam from the cutting tool surfaces is proposed as a method of estimating cutting tool surface conditions. Various encoding methods, including wavelet transformations, were proposed to obtain a small and meaningful set of data from the intensity variation readings of one tool rotation. The encoded data are classified using a simple threshold method, Restricted Coulomb Energy (RCE), and Adaptive Resonance Theory (ART2)-type neural networks. The proposed encoding and classification approaches were tested with over one hundred sets of data.

The threshold method detects only severe tool damage. The RCE neural networks and graphical presentation of the encoded sets demonstrated the feasibility of the proposed monitoring technique and encoding methods. The ART2-type neural networks were found to be the best candidate for tool condition monitoring because of their self learning capability. Wavelet transformation-based encoding and ART2-type neural networks were found to be sensitive enough to recognize wear at the cutting edge.     

A worn tool force model for three-dimensional cutting operations

Previous studies have shown that there is a region on the flank of a worn cutting tool where plastic flow of the workpiece material occurs. This paper presents experimental data which shows that in three-dimensional cutting operations in which the nose of the tool is engaged, the region of plastic flow grows linearly with increases in total wearland width. A piecewise linear model is developed for modeling the growth of the plastic flow region, and the model is shown to be independent of cutting conditions. A worn tool force model for three-dimensional cutting operations that uses this concept is presented. The model requires a minimal number of sharp tool tests and only one worn tool test. An integral part of the worn tool force model is a contact model that is used to obtain the magnitude of the stresses on the flank of the tool. The force model is validated through comparison to data obtained from wear tests conducted over a range of cutting conditions and workpiece materials. It is also shown that for a given tool and workpiece material combination, the incremental increases in the cutting forces due to tool flank wear are solely a function of the amount and nature of the wear and are independent of the cutting condition in which the tool wear was produced.     

Adaptive Resonance Theory-based Modular Networks for Incremental Learning of Hierarchical Clusterings

This paper introduces HART-S, a new modular neural network (NN) that can incrementally learn stable hierarchical clusterings of arbitrary sequences of input patterns by self-organization. The network is a cascade of adaptive resonance theory (ART) modules, in which each module learns to cluster the differences between the input pattern and the selected category prototype at the previous module. Input patterns are first classified into a few broad categories, and successive ART modules find increasingly specific categories until a threshold is reached, the level of which can be controlled by a global parameter called 'resolution'. The network thus essentially implements a divisive (or splitting) hierarchical clustering algorithm: hence the name HART-S (for 'hierarchical ART with splitting'). HART-S is also compared and contrasted with HART-J (for 'hierarchical ART with joining'), another variant that was proposed earlier by the first author. The network dynamics are specified and some useful properties of both networks are given and then proven. Experiments were carried out on benchmark data sets to demonstrate the representational and learning capabilities of both networks and to compare the developed clusterings with those of two classical methods and a conceptual clustering algorithm. A brief survey of related NN models is also provided.     

硬质合金刀片复杂刃口的柔性纤维辅助力流变钝化抛光试验研究

为实现硬质合金刀片复杂形状刃口的一致性钝化,提升刀片使用性能和寿命,采用柔性纤维辅助力流变抛光方法,利用非牛顿流体在剪切应力作用下的流变特性和柔性纤维的控流作用,对硬质合金刀片复杂形状刃口进行抛光。以刃口钝圆半径偏离值K为评价指标,用田口法分析抛光转速、纤维密度、纤维与刀片接触长度等工艺参数对刃口钝圆半径及其一致性的影响,并采用方差分析法评估各因素的权重,综合抛光参数对不同位置切削刃的影响,得到的最优工艺参数组合为纤维密度为200～250 根/cm2,接触长度为4 mm,抛光转速为55 r/min。在最优工艺参数组合下抛光10 min,7个切削刃的钝圆半径均能达到(50.0±5.0) μm的钝化要求,且其切削刃表面粗糙度R_a从(118.00 ± 10.00) nm降至(9.35 ± 0.75) nm,刃口完整无缺陷。      

Monitoring drill conditions with wavelet based encoding and neural networks

Encoding of thrust force signals of microdrilling operations with wavelet transformations and classification of estimated coefficients with adaptive resonance theory (ART2)-type neural networks are proposed for detection of severe tool damage just before complete tip breakage occurs. The coefficients of the wavelets were classified both directly and after a secondary encoding to reduce the humber of inputs. Direct classification of the wavelets was found to be more reliable in the sixty-one cases studied. The proposed approach was also tested with two sampling intervals. Large sampling intervals were used to inspect complete drilling cycles. Smaller sampling intervals were used to focus on thrust force variations during the motion of the machine tool table when it is driven by a stepping motor. It was found that the data collected at smaller sampling intervals were easier to classify to detect severe damage to the tool.     

Achieving Excellent Strength–Ductility and Impact Toughness Combination by Cyclic Quenching in Medium Mn TRIP-Aided Steel

A novel heat treatment process combined cyclic quenching (CQ) with austenite reversion treatment (ART) is proposed to obtain high strength–ductility and high-impact toughness combination in Fe-0.18C-8.92Mn-3.43Al (in mass%) steel. The process referred as CQ-ART was designed for accomplishing the following objectives: (i) refine the prior austenite grains during cyclic quenching process, (ii) further obtain the refined austenite–ferrite block and (iii) improve the stabilities of retained austenite with Mn/C enrichment during ART process. The outstanding product of tensile strength and total elongation of CQ-ART-treated steels was 41.53 and 37.39 GPa%, respectively, and higher than the ART steel of 27.45 GPa%. The highest Charpy impact toughness of CQ-ART steel can reach to 221 J, which is mainly attributed to the refined grains and discontinuous transformation-induced plasticity (TRIP) effect.     

On-line drilling chatter recognition and avoidance using an ART2—A neural network

In the paper, an adaptive resonance theory (ART2-A) neural network is applied to on-line recognition and avoidance of drilling chatter. It is shown that the ART2-A neural network can adaptively learn the features of the thrust force spectrum in a drilling process. As a result, drilling chatter can be automatically detected when a chatter feature starts to appear in the thrust force spectrum. Once chatter is detected, a spindle speed regulation method to suppress chatter is used. Experiments show that this new developed system can monitor and suppress drilling chatter efficiently even under varying cutting conditions.     

A new experimental methodology to analyse the friction behaviour at the tool-chip interface in metal cutting

This paper investigates a new test to analyse the friction behaviour of the tool-chip interface under conditions that usually appear in metal cutting. The developed test is basically an orthogonal cutting process, that was modified to a high speed forming and friction process by using an extreme negative rake angle and a very high feed. The negative rake angle suppresses chip formation and results in plastic metal flow on the tool rake face. Through the modified kinematics and in combination with a feed velocity that is five to ten times higher than in conventional metal cutting, the shear and normal stresses are only acting in a simple inclined plane, allowing to calculate the mean friction coefficient analytically. In addition, the test setup allows to obtain the coefficient of friction for different temperatures, forces and sliding velocities. Experiments showed, that the coefficient of friction is strongly dependent on the sliding velocity for the example workpiece/tool material combination of C45E+N (AISI 1045) and uncoated cemented carbide.     

Maximum cutting speed in laser cutting of fiber reinforced plastics

A simple one-parameter thermal model, predicting the maximum feed rate still resulting in a through cut as a function of beam power, focal spot diameter and thickness of worked material is presented and discussed for CO₂ laser cutting of composites. An extensive experimental program, including glass, carbon and aramide fabric reinforced polyester resin was carried out by varying all the parameters involved in the model. An excellent agreement is shown between experimental data and theoretical predictions. In addition, a criterion for cut quality classification, based on kerf geometry and heat affected zone size is formulated to help in selecting the optimum cutting conditions in order to obtain the best cut quality.     

A new thermomechanical model of cutting applied to turning operations. Part I. Theory

In this paper, an analytical approach is used to model the thermomechanical process of chip formation in a turning operation. In order to study the effects of the cutting edge geometry, it is important to analyse its global and local effects such as the chip flow direction, the cutting forces and the temperature distribution at the rake face. To take into account the real cutting edge geometry, the engaged part in cutting of the rounded nose is decomposed into a set of cutting edge elements. Thus each elementary chip produced by a straight cutting edge element, is obtained from an oblique cutting process. The fact that the local chip flow is imposed by the global chip movement is accounted for by considering appropriate interactions between adjacent chip elements. Consequently, a modified version of the oblique cutting model of Moufki et al. [Int. J. Mech. Sci. 42 (2000) 1205; Int. J. Mach. Tools Manufact. 44 (9) (2004) 971] is developed and applied to each cutting edge element in order to obtain the cutting forces and the temperature distributions along the rake face. The material characteristics such as strain rate sensitivity, strain hardening and thermal softening, the thermomechanical coupling and the inertia effects are taken into account in the modelling. The model can be used to predict the cutting forces, the global chip flow direction, the surface contact between chip and tool and the temperature distribution at the rake face which affects strongly the tool wear. Part II of this work consists in a parametric study where the effects of cutting conditions, cutting edge geometry, and friction at the tool–chip interface are investigated. The tendencies predicted by the model are also compared qualitatively with the experimental trends founded in the literature.     

预置表面织构对硬态切削加工的影响分析

利用电火花穿孔技术预置表面织构,有效抑制硬态切削过程中较大切削力的产生,避免刀具磨损加剧,提高刀具使用寿命。使用CBN刀具硬态切削GCr15淬硬钢,设计关于切削深度、切削速度、进给量三因素无织构正交切削仿真模拟及试验,利用极差、方差及信噪比值法对仿真及试验数据进行分析,确定最佳切削参数组合,以及各参数对硬态切削过程中产生的切削力的影响程度。使用最佳切削参数组合,硬态切削预置表面织构的GCr15淬硬钢,观测刀具磨损情况,测量切削力,并与无织构等同切削条件下的结果进行对比分析,验证预置表面织构能够有效降低刀具磨损,提高刀具使用寿命。     

不同冷却喷雾场下硬质涂层刀具切削蠕墨铸铁性能研究

研究在复合喷雾油膜附水滴冷却时,3种不同结构喷嘴的喷雾场及在硬质涂层刀具切削蠕墨铸铁过程中的切屑流向,并分析刀具和喷射位置对切削力的影响。研究表明:与普通圆柱型和扁平型喷嘴相比,尖嘴型喷嘴的雾化效果更佳,无大液滴现象,喷雾集中,连续雾化稳定。在硬质涂层刀具切削加工蠕墨铸铁过程中,当喷雾场只作用于刀具前刀面时,普通圆柱型喷嘴和尖嘴型喷嘴有良好的断屑作用;而尖嘴型喷嘴和扁平型喷嘴对切屑流具有更好的导向性。相对干切削和冷风辅助切削,尖嘴型喷嘴与外冷复合喷雾条件下切削蠕墨铸铁时,切削力可减小30～60 N,其中喷雾场喷射在刀具前刀面时的切削力最低。不同硬质涂层刀具需要与外冷复合喷雾的喷射位置相互匹配以达到最小的切削力。      

Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools

Productivity and quality in the finish turning of hardened steels can be improved by utilizing predicted performance of the cutting tools. This paper combines predictive machining approach with neural network modeling of tool flank wear in order to estimate performance of chamfered and honed Cubic Boron Nitride (CBN) tools for a variety of cutting conditions. Experimental work has been performed in orthogonal cutting of hardened H-13 type tool steel using CBN tools. At the selected cutting conditions the forces have been measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge has been monitored by using a tool makers microscope. The experimental force and wear data were utilized to train the developed simulation environment based on back propagation neural network modeling. A trained neural network system was used in predicting flank wear for various different cutting conditions. The developed prediction system was found to be capable of accurate tool wear classification for the range it had been trained.     

Performance of iterative tomographic algorithms applied to non-destructive evaluation with limited data

Iterative tomographic algorithms have been applied to the reconstruction of a two-dimensional object with internal defects from its projections. Nine distinct algorithms with varying numbers of projections and projection angles have been considered. Each projection of the solid object is interpreted as a path integral of the light-sensitive property of the object in the appropriate direction. The integrals are evaluated numerically and are assumed to represent exact data. Errors in reconstruction are defined as the statistics of difference between original and reconstructed objects and are used to compare one algorithm with respect to another. The algorithms used in this work can be classified broadly into three groups, namely the additive algebraic reconstruction technique (ART), the multiplicative algebraic reconstruction technique (MART) and the maximization reconstruction technique (MRT). Additive ART shows a systematic convergence with respect to the number of projections and the value of the relaxation parameter. MART algorithms produce less error at convergence compared to additive ART but converge only at small values of the relaxation parameter. The MRT algorithm shows an intermediate performance when compared to ART and MART. An increasing noise level in the projection data increases the error in the reconstructed field. The maximum and RMS errors are highest in ART and lowest in MART for given projection data. Increasing noise levels in the projection data decrease the convergence rates. For all algorithms, a 20% noise level is seen as an upper limit, beyond which the reconstructed field is barely recognizable.     

A fuzzy pulse discriminating system for electrical discharge machining

In this paper, the use of fuzzy set theory to construct a new pulse discriminator in electrical discharge machining (EDM) is reported. The classification of various discharge pulses in EDM is based on the features of the measured gap voltage and gap current. To obtain optimal classification performance, a machine learning method based on a simulated annealing algorithm is adopted to automatically synthesize the membership functions of the fuzzy pulse discriminator. Experimental results have shown that EDM discharge pulses can be not only correctly but also quickly classified under varying cutting conditions using this approach.     

模糊田口法在重切削制程中切削参数最佳化设计

为了快速有效获得重切削时良好的切削性能参数,以田口法与模糊逻辑相结合,对侧面铣削SUS304不锈钢重切削制程时的切削参数进行最佳化设计。由于评估重切削制程的刀具寿命与金属移除率两项主要切削性能,受到主轴转速、每刃进给、轴向切深与径向切深的影响,由此将4个切削参数设置为可控制因子。经过田口法将各品质特性转化为S/N比,通过模糊逻辑运算,采用多重品质特性指标(MPCI)求得切削参数最佳水准组合。试验结果表明:以模糊田口法获得的切削参数最佳水准组合,能够有效改善侧面重切削制程时的切削性能,为刀具制造厂或刀具使用者寻求最佳切削条件提供参考。     

变双曲圆弧齿线齿轮切削仿真及参数优化

变双曲圆弧齿线圆柱齿轮（VH-CATT）是一种将圆弧齿线运用于齿轮齿线上的新型齿轮。由于缺少专用的加工机床,导致加工时其切削参数的调整较为复杂。为解决这个问题,根据其啮合原理及加工过程,利用ABAQUS对其切削加工过程进行模拟仿真;利用得到的数据,根据正交试验方法建立切削力的预测模型。利用鲸鱼算法,建立以加工效率与较小切削力为目标的函数优化模型,并通过加权求和法与归一化处理将它转化为单目标函数优化模型,通过鲸鱼算法得到优化后的切削参数。结果表明：所提出的单目标函数优化模型能够很好地对切削参数进行选定优化,以得到更好的加工效果;优化后的切削参数为主轴转速n=189.3 r/min,每齿进给量fz=0.046 mm,切削深度ap=1.89 mm。     

Static rigid force model for 3-axis ball-end milling of sculptured surfaces

Static rigid force model is used to estimate cutting forces of sculptured surface in a straightforward way, without considering tool deflection, machine tool dynamic behavior and any vibration effects. Two programs were used for calculations, “ACIS” the 3-D geometric modeler and “VISUAL BASIC”. Two programs were edited and used to perform the calculations, the scheme program to model the work piece, tool and cutting edge and to obtain the geometric data and the VISUAL BASIC program design to use ACIS geometric data to calculate the cutting forces. The engaged part of the cutting edge and work piece is divided into small differential oblique cutting edge segments. Friction, shear angles and shear stresses are identified from orthogonal cutting database available in literature. The cutting force components, for each tool rotational position, are calculated by summing up the differential cutting forces. Laboratory tests were conducted to verify the predictions of the model. The work pieces were prepared from CK45 steel using an insert-type ball-end cutter. No coolant was used in any of the experimental works. The cutting forces predicted have shown good agreement with experimental results.     

Improving the laser cutting process design by machine learning techniques

In the field of manufacturing engineering, process designers conduct numerical simulation experiments to observe the impact of varying input parameters on certain outputs of the production process. The disadvantage of these simulations is that they are very time consuming and their results do not help to fully understand the underlying process. For instance, a common problem in planning processes is the choice of an appropriate machine parameter set that results in desirable process outputs. One way to overcome this problem is to use data mining techniques that extract previously unknown but valuable knowledge from simulation results. This paper presents a hybrid machine learning approach for applying clustering and classification techniques in a laser cutting planning process. In a first step, a clustering algorithm is used to divide large parts of the simulation data into groups of similar performance values and select those groups that are of major interest (e.g. high cut quality results). Next, classification trees are used to identify regions in the multidimensional parameter space that are related to the found groups. The evaluation shows that the models accurately identify multidimensional relationships between the input parameters and the output values of the process. In addition to that, a combination of appropriate visualization techniques for clustering with interpretable classification trees allows designers to gain valuable insights into the laser cutting process with the aim of optimizing it through visual exploration.