Numerical control machining simulation a comprehensive survey

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International Journal of Computer Integrated
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Numerical control machining simulation: a
comprehensive survey
Yu Zhang a , Xun Xu b & Yongxian Liu a
a School of Mechanical Engineering and Automation, Northeastern University, Shenyang,
110004, PRC
b Department of Mechanical Engineering, School of Engineering, University of Auckland,
Auckland, 1010, New Zealand
Available online: 31 May 2011
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Numerical control machining simulation:a comprehensive survey
Yu Zhang a,b *,Xun Xu b and Yongxian Liu a
a
School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110004,PRC;b Department of Mechanical
Engineering,School of Engineering,University of Auckland,Auckland 1010,New Zealand
(Received 20July 2010;final version received 22February 2011)
Since the first numerical control (NC)machine tool was created at Massachusetts Institute of Technology in the 1950s,productivity and quality of machined parts have been increased through using NC and later computer numerical control (CNC)machine tools.Like other computer programs,errors may occur in a CNC program,which may lead to scraps or even accidents.Therefore,NC programs need to be verified before actual machining puter-based NC machining simulation is an economic and safe verification method.So far,much research effort concerning NC machining simulation has been made.This paper aims to provide a comprehensive review of such research work and a clear understanding of the direction in the field.First,the definition,common errors,programming approaches and verification methods of NC programs are introduced.Then,the definitions of geometric and physical NC machining simulation are presented.Four categories of NC machining simulation methods are discussed.They are solid-based,object space-based,image space-based and Web-based NC machining simulations.Finally,future trends and concluding remarks are presented.
Keywords:computer numerical control;CNC machining;machining simulation;NC program
1.Introduction
Over the years,technologies such as numerical control (NC),computer numerical control (CNC)and virtual manufacturing (VM)have changed the way products are made.These developments have improved machine tools and forever changed manufacturing processes,so that today it is possible to automatically produce high-quality products quickly,accurately and at lower cost than ever before (Krar et al.2002).
As one of these developments,VM technology refers broadly to the modelling of manufacturing systems and components with an effective use of audiovisual and/or other sensory features to simulate or design alternatives for a real manufacturing environment,mainly through computers.The motivation is to enhance our ability to predict potential problems and inefficiencies in product functionality and manufacturability before real manu-facturing occurs (Banerjee and Zetu 2001).NC machin-ing simulation constitutes an important part of VM technology.
Since the development of the first NC machine tools in 1952at Massachusetts Institute of Technology,NC machining has become a dominant manufacturing mode.NC machining denotes that the coded numerical information is used to control most of the machining actions such as spindle speed,feed rate and tool path while making the final workpiece.The various
approaches used to generate these NC codes may be classified into three groups:manual part programming,computer-assisted part programming and computer aided design (CAD)part programming.Simple pro-grams can be created manually,perhaps with the aid of a calculator,while more complex programs are usually created using a computer or an automatically pro-grammed tool.The manual method,while adequate for many simple point-to-point processes,requires the programmer to perform all calculations required to define the cutter-path geometry and can be time consuming.Errors made by the programmer are often not discovered until the program is tested graphically or on the machine tool.Error correction is cumber-some at a machine tool.In addition,because most machine tools have their own languages,the program-mer is required to work with different instruction sets,which further complicates part-program creation.The computer-assisted part-programming language ap-proach simplifies the process because the programmer uses the same language for each program,regardless the target machine tool.Moreover,translation of the program to NC code is made by a post-processor needed for each and every machine tool.These post-processors may not guarantee the correctness of a part program.Although the computer-assisted approach offers advantages over the manual approach,both approaches require the programmer to translate
*Corresponding author.Email:yzha540@
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593–609
ISSN 0951-192X print/ISSN 1362-3052online Ó2011Taylor &Francis
DOI:10.1080/0951192X.2011.566283
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geometric information from one form (usually an engineering drawing)into another,which can be error prone.Creation of NC programs from a CAD model provides yet another option by allowing the part programmer to access the computational capabilities of a computer via an interactive graphics display console.This allows geometry to be described in the form of points,lines,arcs,and so on,just as it is on an engineering drawing,rather than requiring a transla-tion to a text-oriented e of a graphics display terminal also allows the system to display the resulting cutter-path geometry,allowing earlier verifi-cation of a program,which can avoid costly machine setups for program testing (Chang et al.2006).
NC program errors are mainly those related to NC language grammar and machining parameters.Because an NC machining program is used to control NC machine tools,NC program errors may cause workpiece undercut and overcut;collision between the workpiece and a cutter,fixture,and/or machine tool;and even machine damage and personnel injury.Hence,after an NC program is generated,it must be carefully verified before used in real machining.In general,there are three verification methods for an NC program,namely manual verification procedures,shop-floor verification on NC machine tools and NC computer-based machin-ing simulation.Manual verification involves reading and checking an NC program by an operator.This method can only be used to check simple and short NC programs or to correct some easy-to-find errors such as functional errors,grammatical errors and spindle speed errors.With the shop-floor verification method,NC programs are verified by the process of machining wooden,plastic,wax or soft metal workpieces instead of an actual workpiece on a machine tool.Although it is a reliable verification approach and a physical object can be obtained through the verification process,this approach is expensive and time consuming.In addition,it was reported more than a decade ago that the US industry spent $1.8billion each year to prove or verify NC machining programs (Meister 1988).Another verification method is computer-based machining simu-lation.Without consumption of actual material and occupation of machine tools,it can graphically reveal the real machining process,check collisions,evaluate machining parameters,and reveal and iron out the bugs in a computer aided manufacturing (CAM)system.Therefore,it is more intuitive,faster,safer and more cost-effective.In addition,it can also be used for training machine tool operators.
NC simulation started to make inroads into commercial systems some 20years ago.They come in three styles.Most of the current commercial CAD/CAM systems have their own NC simulation modules,e.g.Catia’s DELMIA NC machine tool simulation
tool,NX’s CAM Integrated Simulation and Verifica-tion software and Pro/E Wildfire’s Vericut.Most of the CAM tools (e.g.MasterCAM,GibbCAM and Smart-CAM)are also equipped with simulation options.The third type of NC simulation tools are more or less standalone tools,such as ICAM’s Virtual Machine,MachineWorks and ModuleWorks.All of these commercial simulation tools have limited functional-ities.This is the reason why research in the field of NC machining simulation is still ongoing.
The objective of this paper is to provide a technical review of the computer-based NC machining simula-tion,categorised as geometric and physical simulations.The remainder of this paper is organised as follows.Section 2,the main section,describes different methods of machining simulation,i.e.solid-based NC machining simulation,object space-based NC machining simula-tion,image space-based NC machining simulation and Web-based NC machining simulation.Discussions and future trends are presented in Sections 3and 4,respectively.Concluding remarks are given at the end.2.
Research of NC machining simulation
In this paper,machining simulations are divided into two categories,i.e.geometric simulation and physical simulation.As shown in Figure 1,geometric simulation is used to graphically check whether the cutters interfere with fixture,workpiece and machine tools,gouge the part,or leave excess stock behind.In addition,it can provide geometric information such as the entry and exit angle of the cutter to physical simulation.As the name implies,physical simulation of an NC machining process aims to reveal the physical aspects of a machining process,such as cutting force,vibration,surface rough-ness,machining temperature and tool wear.It is based on geometric simulation and conventional metal cutting research (Lorong et al.2006).Considering different methods of realising geometric and physical simulation,this section consists of five subsections,i.e.wireframe-based NC machining simulation,solid-based NC machining simulation,object space-based NC machin-ing simulation,image space-based NC machining simulation and Web-based NC machining simulation.Solid-based NC machining simulation is further classi-fied into constructive solid geometry (CSG)-based and boundary representation (B-rep)-based NC machining simulation.Object space-based NC machining simula-tion is categorised into Z-map-based,vector-based and octree-based NC machining simulation.
2.1.Wireframe-based NC machining simulation
In wireframe-based NC machining simulation,tool path and the shape of the machined workpiece are
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displayed in the form of wireframe.Because wireframe model has a simple data structure and fast data operation,it was widely applied to early NC machining simulations.However,this representation makes com-plex three-dimensional (3D)objects ambiguous and it does not provide actual solid geometric model and information.Hence,wireframe-based NC machining simulation can only be applied to a simple workpiece and produce simple geometric simulation.EMC2(2010)is such free software,which is a descendent of the original EMC software.2.2.
Solid-based NC machining simulation
Solid modelling is a much more complete 3D model-ling representation.They are useful for the geometric as well as physical simulation of machining process in which in-process workpiece,cutter and chip geometries can be accurately represented.This section discusses two types of solid-based machining simulations,CSG-based machining simulation and B-rep-based machin-ing simulation.
2.2.1.CSG-based NC machining simulation
In CSG-based NC machining simulation,parts are represented by a CSG model.An early piece of work was done by Hunt and Voelcker (1982),who used the Part and Assembly Description Language (PADL)modelling system for 2.5-axis machining ter on,through considering the physical aspects of machining operation,Spence et al.(1990)and Spence and Altintas (1994)developed a CSG-based process simulation system for 2.5-axis milling,which consisted of a geometric simulator and physical simulator.In this system,parts were described using a CSG solid model at first.Then along each path,after the cutter represented
by a semicircle was intersected with the individual geometric primitives describing the part,the cutter/part immersion geometry was generated.Finally,the cutter-part intersection data from cutter/part immersion geometry were abstracted.The physical simulator used the cutter-part intersection data and mechanistic models (Tlusty and MacNeil 1975)to carry out the cutting-force prediction.Applications in predicting cutting force in face-milling and end-milling operations confirmed the validity of the technique.However,this study is limited to 2.5-axis milling.In addition,based on CSG,two methods on collision detection were proposed (Su et al.1999,Ho et al.2001).Taking advantages of the CSG ‘divide-and-conquer’paradigm and distance-aided collision detection for convex bounding volumes,one of the methods realised efficient and precise collision detection.The other method adopted a heterogeneous representation in that CSG was used to represent the tool,and a cloud of over 10,000points was used to represent the workpiece for rapidly detecting collision and penetration depth.However,this method tends to lose efficiency and requires a substantial amount of memory as the number of sampled points increases.Furthermore,collisions between the tool and other static parts of the NC machine are not handled by the two above-mentioned methods.2.2.2.
B-rep-based NC machining simulation
The B-rep technique for solid modelling,where the surfaces,edges and vertices of an object are explicitly represented,has found wide applications in design and manufacturing (Requicha and Rossignac 1992).In an earlier work done by O’Connell and Jabolkow (1993),B-rep solid models of the machined part were constructed from NC programs in a Cutter Location (CL)data format for 3-axis milling
simulation.
Figure 1.General architecture of NC machining simulation.
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A limitation in this study is that there is no considera-tion for physical simulation.To solve this limitation,El-Mounayri et al.(1997,1998,2002),Imani et al.(1998),Imani and Elbestawi (2001)and Bailey et al.(2002a,2002b)integrated geometric simulation with physical simulation.A good agreement between the simulated and experimentally measured results was obtained.El-Mounayri et al.(1997,1998)developed a generic solid modeller-based ball-end milling process simulation system for 3-axis milling.First,a part was described using a B-rep solid model,and the cutting edges of a cutter were fitted with cubic Bezier curves.Second,for every completed tool path,i.e.one NC block,the tool swept volume was generated and intersected with the part to give the corresponding removed volume,in-process parts and final part.Third,the tool cutting edges were intersected with removed volume to produce the tool-part immersion geometry.Finally,after the geometric information was extracted from the tool-part immersion geometry,cutting force was predicted through the cutting-force model for ball-end mills developed by Abrari et al.(1998).This model is based on the concept of equivalent orthogonal cutting conditions and empirical equations for computing shear angle,friction angle,and shear strength of ter on,El-Mounayri et al.(2002)improved this physical simulation using Artificial Neural Network (ANN)technology and realised an integration of prediction and optimisation uses the same ANN model (El-Mounayri and Deng 2010).Imani et al.(1998)did similar research.In contrast with El-Mounayri’s work mentioned above,they represented the cutting edge by the B-spline curves,which can also be used for the representation of any shape of a cutting edge and is better than cubic Bezier curves.In addition,a new three-component mechanistic force model was developed to calculate instantaneous ball-end milling cutting forces.This force model not only takes into account the geometry of cutting edge (i.e.rake and helix angles)but also considers the variations of the chip-flow angle and cutting coefficients in the axial direction.And then the geometric simulation module was extended to simulate the parts with free-form surfaces by using advanced sweeping/skinning techniques,and the phy-sical simulation module was extended to surface roughness prediction (Imani and Elbestawi 2001).Subsequently,Bailey et al.(2002a,2002b)extended Imani’s work by representing an arbitrary cutting edge design using non-uniform rational B-spline curves,which is better than B-spline curves.In addition,to further guard against process planning errors or unexpected factory floor events,geometric simulation and physical simulation were integrated with factory floor monitoring and control (Saturley and Spence 2000,Spence et al.2000).
Since B-rep-based machining simulation becomes more time consuming with increased part complexity,parallel processing techniques were used (Fleisig and Spence 2005).To solve the partial limitation and to improve computation efficiency,Spence et al.(1990),Spence and Altintas (1994),Yip-Hoi and Huang (2006)used a semi-cylinder to represent the cutter instead of a semicircle;the B-rep to model the part instead of CSG;and cutter engagement features to characterise cutter/workpiece engagement ter,Aras and Yip-Hoi (2008)and Ferry and Yip-Hoi (2008)extended Yip-Hoi and Huang’s work to 3-axis machining and 5-axis machining,respectively.B-rep-based collision detection for 5-axis NC machining was also reported by Ilushin et al.(2005)and Wein et al.(2005).2.3.
Object space-based NC machining simulation
In an object space-based machining simulation,parts are represented by a collection of discrete points (with vectors)or surfaces with vectors or certain volume elements.Since objects are discretised,Boolean operations between objects are two-dimensional,even one-dimensional so that simulation computation is improved.Up to now,there are three major decom-position methods for the models in object space-based NC machining simulation,which are Z-map method,vector method and octree method,respectively.These methods are described below.2.3.1.
Z-map method
The Z-map method is used to decompose the model of a part into many 3D histograms.Each 3D histogram starts with the value of the height of the stock.During the simulation process,each tool movement updates the heights of the 3D histograms it passes over if it cuts lower than the currently stored height.Therefore,Boolean operation in this kind of simulation is one dimensional,which means simulation speed is very fast.Anderson (1978)used a 3D histogram to approximate the billet and cutter assembly shape to detect collisions in NC machining.However,no provision is allowed for 4-or 5-axis machining when the cutter is not orthogonal to the cubes.Subsequently,this representation was called Z-map.To get better geometric accuracy,computation efficiency and gra-phical quality,many researchers have used different technologies to enhance the traditional Z-map model.For example,Hsu and Yang (1993)used isometric projection and raster display to enhance Z-map model for 3-axis milling processes.
In 2002,Lee and Ko (2002)and Kang et al.(2002)used the inclined sampling method to enhance the Z-map model.In the same year,Lee and Lee (2002)
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developed a local mesh decimation method to achieve a smoother rendering as well as a dynamic viewing capability of the Z-map model in a 3-axis milling simulation.The overall algorithm for the periodic local mesh decimation is as follows.
(1)The Z-map data of the workpiece are divided
into small regions,and meshes for each region are generated,decimated and stored;
(2)A tool movement command of the NC
program is read and the Z-map data are updated accordingly.The display mode of a region cut by the tool is set as the Z-map rendering mode;
(3)Meshes for regions are rendered by calling the
rendering functions of a graphics library,such as OpenGL.If a region is set in the Z-map rendering mode,a mesh is generated from the Z-coordinates of the grids in the region,and is passed to the rendering functions.Otherwise,the decimated mesh for the region is passed to the rendering functions;
(4)If the current iteration is coincident with the
decimation period,the meshes are generated and decimated for the regions in the Z-map rendering mode,then go to step (2).Otherwise,go to (3)directly.Because of its simple data structure and computa-tion efficiency,the Z-map method has been employed for physical simulation such as cutting-force prediction and surface roughness prediction.It was shown that the method could effectively predict cutting force and surface roughness.The cutting force in ball-end milling of sculptured surfaces was predicted by Kim et al.(2000).In this study,the cutter contact area could be obtained by comparing the Z-map data of the cutter with that of the machined surface.Then,after the cutting edge elements were calculated,the 3D contact area and the cutting edge elements were projected to the cutter plane,which was defined as a circular plane that was perpendicular to the cutter axis.By comparing cutting edge element positions with cutter contact area data on the cutter plane,the cutting edge elements that engaged in cutting process could be identified.Finally,cutting forces acting on the engaged cutting edge elements were calculated using a cutting-force ter,based on the cutting force,cutter deflection and form error was predicted accurately (Kim et al.2003).Instead of the semi-spherical surface used in the research by Kim et al.,Jung et al.(2001)proposed an exact chip engagement surface from cutting edge geometry to more accurately update the workpiece model and predict the cutting force.However,a simple cutting-force model with no consideration of the size
effect of the workpiece material was developed and used in this study so that the accuracy of cutting force was ter,Zhu et al.(2001)extended cutting-force prediction to a 5-axis ball-end milling process.In the above research,average cutting coefficients depend-ing on the cutting condition and traditional Z-map model were used to predict the cutting forces in transient cuts,which resulted in inaccurate and inefficient prediction for instantaneous cutting force,in particular at peak or valley points.To overcome these limitations,Ko et al.(2002)and Yun et al.(2002a,2002b)proposed a new method of calculating cutting-condition-independent coefficient considering the size effect and developed the moving edge node Z-map (ME Z-map).As shown in Figure 2,the fundamental idea in the ME Z-map is that the edge node,which refers to the node closest to the cutting edge,is moved towards the boundary of the cutter movement.Subsequently,physical simulation was extended to surface roughness prediction based on the traditional Z-map model (Liu et al.2006).
2.3.2.Vector method
The vector method means that surfaces of a part are approximated by a set of points with direction vectors that are normal and/or vertical to the surface at each point.A vector extends until it reaches the boundary of the original stock or intersects with another surface of the part.To simulate the cutting process,the intersec-tion of each vector with each tool movement’s envelope is calculated.The length of a vector is reduced if it intersects the envelope.An analogy can be made to mowing a field of grass.Each vector in the simulation corresponds to a blade of grass ‘growing’from the desired object.As the simulation progresses,the blades are ‘mowed down’.The lengths of the final vectors correspond to the amount of excess material (if above the surface)or the depth of the gouge (if below the surface)at that point (Jerard et al.
1989).
Figure 2.Fundamental notions for ME Z-map (Adapted from Yun et al.2002b).
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Therefore,this method is usually used to judge whether the part is undercut or overcut.
As shown in Figure 3,Chappel (1983)proposed the ‘point-vector’method in which part surface was approximated by a set of points with vector to simulate the material removal process.However,he did not mention how to select the ter,Oliver and Goodman (1986)developed a system similar to Chappel’s,which used a computer graphics image of the desired surface to select the points (Figure 4).This system was considered as the first module of an NC verification technique.Subsequently,another two modules of this NC verification technique were developed to get an efficient simulation.The second module provided a means of extracting a subset of eligible points (Oliver and Goodman 1990).The third module realised the intersection of normal vectors with swept-volume models (Oliver 1992).Later on,as Figure 5illustrates,Jerard et al.(1989)proposed an object-based surface discretisation modelling method that not only shared the characteristics of the methods of Chappel (1983)and Oliver and Goodman (1986)but also contained features that improved simulation efficiency.To implement highly accurate NC
machining simulation for mould and die parts,Park et al.(2005)divided discrete vector model into discrete normal vector (DNV)and discrete vertical vector (DVV),and hybridly used DNV and DVV in the machining simulation.As shown in Figure 6,the strategy for modelling a shape that consists of the ‘features’and the ‘others’(i.e.smooth and non-steep areas)can be described as follows:
(1)Vertical wall,sharp edge and overhang features
on the input design surfaces are identified,as shown Figure 6(a);
(2)As shown in Figure 6(b),all feature areas are
converted into a set of DNV elements;
(3)As shown in Figure 6(c),the whole surface area
is converted into a single DVV model;
(4)DNV and DVV models are hybridly used to
compensate for each other’s shortcomings.Figure 6(d)shows the conceptual hybrid model,where the DNV and DVV models are super-imposed for better understanding.2.3.3.
Octree-based method
As illustrated in Figure 7,octree schema represents parts in a tree structure.The nodes of a tree are cuboids and are recursively subdivided into eight mutually disjoint child nodes until all nodes contain no parts of the modelled object,or the desired accuracy to the object is reached.That is,each node is checked to see whether it is fully,partially or not (empty)occupied.If a node is empty or full occupied,it does not need to be subdivided.If it is partially occupied,the node is subdivided further.The subdivision process is repeated until all nodes are either full or not occupied,or until geometric accuracy has been reached (Dyllong and Grimm
2008).
Figure 3.Point-vector model (Adapted from Chappel
1983).
Figure 4.Image-based point-vector model (Adapted from Karunakaran et al.2010).(a)Discrete model with outward vectors,(b)discrete model with vertical vectors.
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Hierarchical octree representations offer an attrac-tive alternative for NC machining simulation because its Boolean operation is simple,and the spatial ordering of the data structure maintains this simplicity even when the local cutting region becomes complex.Karunakaran and Shringi (2007)developed a machin-ing simulation system in which the part was repre-sented by a traditional octree for model creation and modification,and then was represented by B-rep for those downstream applications such as animated display,verification and optimisation.To this end,an algorithm to convert the octree model of the instanta-neous workpiece into B-rep model was presented.This algorithm essentially decomposed the octree model into three quadtree models that store the geometry along the three principal directions (Karunakaran and Shringi 2007).Subsequently,this system was extended to physical simulation for cutting-force prediction using the material removal rate-based average cut-ting-force model (Karunakaran and Shringi 2008).
However,using the cutting-force model is inherently incapable of determining the instantaneous cutting force that is essential for optimised cutting and for arriving at optimal values of machining parameters such as feed rate.Therefore,a general instantaneous cutting-force model developed by Altintas and Lee (1996)was used to predict the cutting force (Karuna-karan et al.2010).It was shown that the estimated cutting force agreed well with the experimental results.Traditional octree-based machining simulation demands a large memory and often results in inexact geometric representations.It has been shown that maintenance of a tolerance of 0.01mm over a volume in the order of 100mm per side requires 108octree nodes (Liu et al.1996).Therefore,to decrease memory and improve geometric representation,a number of methods have been proposed.Brunet and Navazo (1990)developed an extended octree model to more accurately represent 3D objects.In the extended octree model,boundary nodes contain
additional
Figure 5.Discrete models with vectors (Adapted from Jerard et al.
1989).
Figure 6.Feature shapes and conceptual hybrid models.(a)Feature shapes,(b)DNV model,(c)DVV model,(d)conceptual
hybrid model (Adapted from Park et al.2005).
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