A novel laboratory blanking apparatus for the experimental identification of blanking parameters

High-quality blanking depends on several blanking parameters that are difficult to investigate with industrial blanking presses. In this research, a novel laboratory blanking apparatus that allows quasi-static and dynamic blanking at different blanking speeds is presented. With a clear focus on the dynamics of the blanking process, the inertial correction of the punch force, the surface velocity and the surface temperature of sheet-metal are researched. In addition, measurements of the punch–die misalignment and the punch–die inclination-angle are discussed. The apparatus is also able to perform a partial penetration of the punch to a selected depth with an accuracy of better than 10 μm. The apparatus was shown to offer us a new insight into the blanking process that can increase our understanding and therefore result in better numerical models and better products.     

Blanking tool wear modeling using the finite element method

One of the main objectives of the numerical process design in metal forming is to develop adequate tool design and establish process parameter in order to increase tool life and to improve part quality and complexity while reducing manufacturing cost. The prediction of tool wear in sheet metal blanking/punching processes is investigated in this paper using the finite element method. A wear prediction model has been implemented in a finite element code in which the tool wear is a function of the normal pressure and some material parameters. A damage model is used in order to describe crack initiation and propagation into the sheet. The distribution of the tool wear on the tool profile is obtained and compared to industrial observations. Furthermore, a numerical investigation has been carried out to study the effect of tool wear on the burr formation.     

Modelling tool wear in cemented-carbide machining alloy 718

Tool wear is a problem in turning of nickel-based superalloys, and it is thus of great importance to understand and quantitatively predict tool wear and tool life. In this paper, an empirical tool wear model has been implemented in a commercial finite element (FE) code to predict tool wear. The tool geometry is incrementally updated in the FE chip formation simulation in order to capture the continuous evolution of wear profile as pressure, temperature and relative velocities adapt to the change in geometry. Different friction and wear models have been analysed, as well as their impact on the predicted wear profile assessed. Analyses have shown that a more advanced friction model than Coulomb friction is necessary in order to get accurate wear predictions, by drastically improving the accuracy in predicting velocity, thus having a dramatic impact on the simulated wear profile. Excellent experimental agreement was achieved in wear simulation of cemented carbide tool machining alloy 718.     

The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel

The purpose of this paper is to develop a predictive model for the prediction of tool flank wear and an optimization model for the determination of optimum cutting conditions in machining 17-4PH stainless steel. The back-propagation neural network (BPN) was used to construct the predictive model. The genetic algorithm (GA) was used in the optimization model. The Taguchi method (TM) was used to find the optimum parameters for both models, respectively. Two steps of experiments have been carried out by machining 6 mm length and 90 mm length of the workpiece, respectively. The experimental scheme was arranged by using an orthogonal array of TM. It has been shown that the predictive model is capable of predicting the tool flank wear in an agreement behavior. The optimization model has also been proved that it is a convenient and efficient method to find the optimum cutting conditions associated with the maximum metal removal rate (MMRR) under different constraints. The constraint is the tool flank wear that can be determined from the predictive model. Furthermore, the systematic procedure to develop the models in this paper can be applied to the usage of the predictive or optimized problems in metal cutting.     

冲裁间隙变化对冲裁件断面质量影响的力学分析

分析了冲裁过程中断面光亮带、断裂带、圆角和毛刺形成阶段,运用塑性增量理论、应力莫尔圆方法和粘着磨损法则,来分析影响光亮带和断裂带、圆角和毛刺形成的力学因素,得出确定模具合理间隙值是获得高质量冲裁件的关键因素。     

Tool life determination based on the measurement of wear and tool force ratio variation

Non-linear regression analysis techniques are used to establish models for wear and tool life determination in terms of the variation of a ratio of force components acting at the tool tip. The ratio of the thrust component of force to the power, or vertical, force component has been used to develop models for (i) its initial value as a function of feed, (ii) wear, and (iii) tool lifetimes. Predictions of the latter model have been compared with the results of experiments, and with predictions of an extended Taylor model. In all cases, good predictive capability of the model has been demonstrated. It is argued that the models are suitable for use in adaptive control strategies for centre lathe turning.     

模具间隙对冲裁件质量的影响分析

文章简述了冲裁变形过程和冲裁间隙的概念。提出冲裁件质量评定方法。分析了冲裁间隙大小对冲裁件质量影响的因果关系,阐述了冲裁缺陷产生原因及其预防措施。     

Optimum selection of variable punch-die clearance to improve tool life in blanking non-symmetric shapes

Punch-die clearance is a well-known parameter affecting both tool life and edge quality of parts in blanking and piercing. Selecting the optimum or best punch-die clearance can give a significantly longer tool life by minimizing tool wear. Previous studies have shown the effect of punch-die clearance on various sheet materials and thicknesses during blanking of round parts while non-round geometries are more commonly found in industrial applications. Therefore, in this study, the effect of part geometry is considered to select the ‘best’ punch-die clearance to minimize tool wear. In blanking non-round geometries, the punch and die undergo non-uniform wear, with higher wear observed in areas with sharp radii and abrupt changes in geometry. In the present study, the effect of punch-die clearance on punch stress for blanking various shapes is investigated using Finite Element (FE) analysis. The punch-die clearance that gives the lowest value of the punch stress for the different part geometries is identified. A method is developed to select a geometry dependent variable punch-die clearance to obtain more uniform wear on the punch, thereby increasing the punch and die life.     

Condition-based maintenance in punching/blanking of sheet metal

This paper provides a review and proposal for condition-based maintenance (CBM) in blanking of sheet metal. To date, little research on this topic can be found in literature, which is probably due to the complex nature of the process. Previous statistical, artificial intelligence (AI) and model-based approaches are analysed. Special attention is given to inherent assumptions and other sources of inaccuracy. In addition, it is demonstrated how the signature of the force–displacement relation changes significantly with increasing tool wear in a typical configuration of sheet steel blanking. An analysis follows as to how such behaviour could be used in CBM. A practical implementation of CBM in sheet metal blanking is proposed based on a hybrid solution.     

The influence of the microstructure of hardened tool steel workpiece on the wear of PCBN cutting tools

Due to the recent developments of advanced cutting tool materials in the superbarasive family, such as cubic boron nitride (CBN) tools, the interest in cutting hardened steels has increased significantly. High flexibility and ability to manufacture complex workpiece geometry in one set up is the main advantage of hard turning compared to grinding. The focus of this study is to investigate the performance and wear behavior of CBN tools in finish, dry turning of four different hardened steels, treated to the same hardness R_c = 54. The following four materials were machined: X155CrMoV 12 cold work steel (AISI D2), X38CrMoV5 (AISI H11) hot work steel, 35NiCrMo16 hot work steel and 100Cr6 bearing steel (AISI 52100). A large variation in tool wear rate was observed in the machining of these steels. The tool flank grooves have been correlated to the microstructure of these steels, namely the presence of various carbides. The chip study reveals that there is presence of different amounts of white layers in machining these steels.     

Tool life and tool wear in the semi-finish milling of inclined surfaces

Functional die and mold components have complex geometries and are made of high hardness materials, which make them difficult to machine. This work contributes to a better understanding of this type of process and of the wear mechanisms of tools used in semi-finishing operations of hardened steels for dies and molds. Several milling experiments were carried out to cut AISI H13 steel with 50 HR_C of hardness using the high-speed milling technique. The main goal was to verify the influence of workpiece surface inclination and cutting conditions on tool life and tool wear mechanisms. The main conclusions were the inclination of the machined surface strongly influences tool life and tool wear involves different mechanisms. At the beginning of tool life, the wear was caused mainly by abrasion on the flank face plus diffusion and attrition on the rake face. At the end of tool life, the mechanisms were adhesions and microchipping at the cutting edge.     

Influence of machining errors on form errors of microlens arrays in ultra-precision turning

Ultra-precision turning is widely used in machining microlens arrays. Machining errors have an effect on the form accuracy of the whole microlens array, but they have not been fully studied, especially the effect on the optical performance. A machining error model of microlens arrays is built to analyse the coordinate distortions and form errors easily based on multi-body system theory and a homogeneous transformation matrix. The simulative and experimental results verified the influence of three major machining errors (tool alignment errors (∆x, ∆y), tool nose radius error (∆R) and squareness error (∆θ) from the proposed approach. Through the simulation and experimental approach, this study describes the distribution of the form errors as axisymmetric; the form error of the centre part of the row and column cells is minimal, whereas that of the diagonal cells is the maximum. The optical performance of the cells has the same correlation as the form errors. Based on the above study, a horizontal off-centring machining method is proposed to achieve high form accuracy and uniform optical performance.     

New starting points for the prediction of tool wear in hot forging

Formation of a sufficiently large database on tools for hot forging, which is necessary for successful prediction of wear at a given number of strokes, as well as for the prediction of the critical number of strokes when the acceptable tolerance of a forging is exceeded, is a relatively time-consuming process in the production practice. To overcome this problem, this article presents a starting point for quicker prediction of these quantities by means of conditional average estimator neural networks (CAE NN), namely by the so-called integral method and by the partial method. A comparison of the efficiency in prediction of these methods was carried out on the results of wear obtained in laboratory forging, which allowed a gradual and relatively quick tracing of wear contour progression on tools and thus the formation of a reliable database. The results presented show that in the case of a relatively small database, where, for instance, there are known data and wear parameters on at least three different tool steels, or, on differently heat treated steels, it is possible to effectively predict the wear of a fourth tool simply on the basis of the slightly perceivable wear profile of the tools. Here, the integral method gives better predictions. This conclusion is of great importance in practice: from intermediate control of gradual tool wear, we can predict its tool life.     

Quantification of tool wear using white light interferometry and three-dimensional computational metrology

Although several wear modes can result from machining, the most common tend to be what are referred to as flank wear and crater wear. Flank wear can be easily measured directly from images of a worn cutting tool, and this is the typical method used to quantify the condition of a tool. On the other hand, crater wear is difficult to quantify, and thus has typically been treated in a qualitative manner. The inability to characterize and compare the two wear modes in a quantitative way is an increasingly important problem as the precision of machining operations improves and cutting moves almost exclusively to the nose radius of cutting tools. This paper introduces a new approach to this problem by proposing a technique to quantify both wear modes for direct comparison. The technique measures the volumetric wear loss in the two regions by comparing three-dimensional wear data obtained by white light interferometry with ideal representations of unworn cutting tools. The resulting wear measurements are compared and related to changes in the cutting process, specifically increases in cutting forces and changes in the topography of machined surfaces.     

Abrasion wear resistance of arc-sprayed stainless steel and composite stainless steel coatings

The abrasion wear resistance of stainless steel and composite stainless steel/titanium boride coatings arc sprayed with air and argon was evaluated. Stainless steel coatings arc sprayed with air were found to be slightly more resistant than bulk stainless steel, whereas those sprayed with argon were slightly less resistant. The wear resistance of composite stainless steel/titanium diboride coatings was from two to four times greater than that of bulk stainless steel, depending on the cored wire constitution and the type of gas used for spraying. Microstructural analysis, microhardness measurements, and optical profilometry were used to characterize the coatings and wear damage. By considering both the wire constitution and the spraying conditions, it was possible to fabricate composite stainless steel coatings that showed a 400 % increase in wear resistance over bulk stainless steel.     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.     

斜齿圆柱齿轮旋转精冲过程模具磨损模拟分析

在Deform-3D软件平台上建立了斜齿圆柱齿轮旋转精冲成形三维刚塑性有限元模型.基于Archard磨损模型对精冲过程进行了凹模模具磨损分析,得到了模具工作表面各点的磨损分布,确定了最大磨损发生区域,并与直齿圆柱齿轮精冲模具磨损进行了对比分析.通过单因素变量法,研究了反顶力、压边力、冲裁速度、凸凹模间隙、凹模圆角半径以及凹模初始硬度对模具磨损的影响关系.研究结果表明,磨损模型能准确预测旋转精冲过程中主要工艺参数与磨损之间关系,并从工艺设计方面提出了减小模具磨损的措施.     

An approach based on current and sound signals for in-process tool wear monitoring

This paper presents a tool condition monitoring system (TCMS) for on-line tool wear monitoring in turning. The proposed TCMS was developed taking into account the necessary trade-off between cost and performance to be applicable in practice, in addition to a high success rate. The monitoring signals were the feed motor current and the sound signal. The former was used to estimate the feed cutting force using the least squares version of support vector machines (LS-SVM). Singular spectrum analysis (SSA) was used to extract information correlated with tool wear from the sound signal. The estimated feed cutting force and the SSA decomposition of the sound signal alone with the cutting conditions constitute the input data to the TCMS. Again LS-SVM was used to estimate tool condition and its reliability for on-line implementation was validated by experiments using AISI 1040 steel. The results showed that the proposed TCMS is fast and reliable for tool condition monitoring.     

Experimental evaluation of strains in the tension–compression using a new tool geometry, X-Die

Sheet-metal forming involves a complex distribution of strains throughout the part. The strains occur due to tension, compression and a mix of both. A geometry has been developed, the X-Die, in order to gain insight into the strain behavior of different materials. The X-Die enables strain paths far into the tension–compression region, thus creating the possibility to extend the experimental base both for definition and for further extrapolation of the forming limit curve (FLC) in the tension–compression region, as well as to evaluate FE-simulation results for the same region.

The experimental results show that the strain signature is impacted by material quality. In qualities such as extra high strength steel (EHSS) and aluminum the strains do not reach as far into the tension–compression region as the strains do in e.g. mild steel. This is due to failure in plane strain tension. Strain paths in materials such as mild steel and high strength steel (HSS) reach far into the tension–compression region before failure. Use of the X-Die provides possibilities to reach farther into the tension–compression region compared with traditional test methods for creating a forming limit diagram (FLD).

Use of the X-Die yields well-defined strain signatures. These clearly defined strain signatures are favorable for comparison with numerical simulations, especially for strain signatures in the tension–compression region.

Furthermore, the experiments using the X-Die indicate that a possible additional forming limit curve, which intersects the original forming limit curve (shear failure), exists so far into the tension–compression region that it is not applicable.

Even though the experiments indicate compression strains >100% (material DX56D), the experiments show potential for an experimentally determined extrapolation of the FLC up to 75% compression strain. The results of the experiments indicate that the X-Die geometry is suitable as a supplementary tool in identifying the strain behavior of different materials far into the tension–compression region and is also a good tool for verification of numerical results in the tension–compression region.