A machine-learning based solution for chatter prediction in heavy-dutymilling machines

The main productivity constraints of milling operations are self-induced vibrations, especially regenerative chatter vibrations. Two key parameters are linked to these vibrations: the depth of cut achievable without vibrations and the chatter frequency. Both parameters are linked to the dynamics of machine component excitation and the milling operation parameters. Their identification in any cutting direction in milling machine operations requires complex analytical models and mechatronic simulations, usually only applied to identify the worst cutting conditions in operating machines. This work proposes the use of machine learning techniques with no need to calculate the two above-mentioned parameters by means of a 3-step strategy. The strategy combines: 1) experimental frequency responses collected at the tool center point; 2) analytical calculations of both parameters; and, 3) different machine learning techniques. The results of these calculations can then be used to predict chatter under different combinations of milling directions and machine positions. This strategy is validated with real experiments on a bridge milling machine performing concordance roughing operations on AISI 1045 steel with a 125 mm diameter mill fitted with nine cutters at 45°, the results of which have confirmed the high variability of both parameters along the working volume. The following regression techniques are tested: artificial neural networks, regression trees and Random Forest. The results show that Random Forest ensembles provided the highest accuracy with a statistical advantage over the other machine learning models; they achieved a final accuracy of 0.95 mm for the critical depth and 7.3 Hz for the chatter frequency (RMSE) in the whole working volume and in all feed directions, applying a 10 × 10 cross validation scheme. These RMSE values are acceptable from the industrial point of view, taking into account that the critical depth of this range varies between 0.68 mm and 19.20 mm and the chatter frequency between 1.14 Hz and 65.25 Hz. Besides, Random Forest ensembles are more easily optimized than artificial neural networks (1 parameter configuration versus 210 MLPs). Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer.Hidrodamp Project (IDI-20110453) of the Centre for Industrial Technological Development (CDTI

Prediction of Surface Roughness and Power in Turning Process Using Response Surface Method and ANN

This paper examines the influence of three cutting parameters (cutting speed, cutting depth and feed rate) on surface roughness and power in the longitudinal turning process of aluminium alloy. For the analysis of data gathered by experiments, two methods for prediction of responses were employed, namely Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The research has shown that the ANN gives a better prediction of surface roughness than the RSM. In the modelling of the power, the average error value obtained by the ANN does not differ significantly from its value obtained by the RSM. This research is conducted to reveal the rigidity of the machine tool in order to select an appropriate spindle motor for retrofit purpose. The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit. Therefore, the servo motor with smaller power than the original motor is selected which is cost-effective and it will not cause inappropriate strong vibrations that lead to the unexpected surface roughness and excessive noise inside the Learning Factory environment in which the machine tool is used

The critical raw materials in cutting tools for machining applications: a review

A variety of cutting tool materials are used for the contact mode mechanical machining of components under extreme conditions of stress, temperature and/or corrosion, including operations such as drilling, milling turning and so on. These demanding conditions impose a seriously high strain rate (an order of magnitude higher than forming), and this limits the useful life of cutting tools, especially single-point cutting tools. Tungsten carbide is the most popularly used cutting tool material, and unfortunately its main ingredients of W and Co are at high risk in terms of material supply and are listed among critical raw materials (CRMs) for EU, for which sustainable use should be addressed. This paper highlights the evolution and the trend of use of CRMs) in cutting tools for mechanical machining through a timely review. The focus of this review and its motivation was driven by the four following themes: (i) the discussion of newly emerging hybrid machining processes offering performance enhancements and longevity in terms of tool life (laser and cryogenic incorporation); (ii) the development and synthesis of new CRM substitutes to minimise the use of tungsten; (iii) the improvement of the recycling of worn tools; and (iv) the accelerated use of modelling and simulation to design long-lasting tools in the Industry-4.0 framework, circular economy and cyber secure manufacturing. It may be noted that the scope of this paper is not to represent a completely exhaustive document concerning cutting tools for mechanical processing, but to raise awareness and pave the way for innovative thinking on the use of critical materials in mechanical processing tools with the aim of developing smart, timely control strategies and mitigation measures to suppress the use of CRMs

GENETIC ALGORITHM GA TO OPTIMIZEMACHINING PARAMETERS INTURNING OPERATION: A REVIEW

The determination of optimal cutting parameters have significant importance for economic machining in minimizing of particularoperating mistakes like tool fraction,wear,and chatter. The evolutionary algorithm GA is used to improve many solutions of optimization complex problems in many applications. This paper reviewed the ideal selection of cutting parameters in turning operation using GA and its variants. This study deals with GA algorithm in different machining aspects in turning operation like surface roughness, production rate, tool life, production cost, machining time and cuttingtemperature. The survey showed that there aremany papers in the field of turning parameters optimization using GA, but there is a lack in studies in the field of cutting temperature optimization in turning operation which is very important problem in machining operation.In addition, there arerare papers that studied dry turning operations

Ultra-high precision machining of contact lens polymers

Contact lens manufacture requires a high level of accuracy and surface integrity in the range of a few nanometres. Amidst numerous optical manufacturing techniques, single-point diamond turning is widely employed in the making of contact lenses due to its capability of producing optical surfaces of complex shapes and nanometric accuracy. For process optimisation, it is ideal to assess the effects of various conditions and also establish their relationships with the surface finish. Presently, there is little information available on the performance of single point diamond turning when machining contact lens polymers. Therefore, the research work undertaken herewith is aimed at testing known facts in contact lens diamond turning and investigating the performance of ultra-high precision manufacturing of contact lens polymers. Experimental tests were conducted on Roflufocon E, which is a commercially available contact lens polymer and on Precitech Nanoform Ultra-grind 250 precision machining. Tests were performed at varying cutting feeds, speed and depth of cut. Initial experimental tests investigated the influence of process factors affecting surface finish in the UHPM of lenses. The acquired data were statistically analysed using Response Surface Method (RSM) to create a model of the process. Subsequently, a model which uses Runge-Kutta’s fourth order non-linear finite series scheme was developed and adapted to deduce the force occurring at the tool tip. These forces were also statistically analysed and modelled to also predict the effects process factors have on cutting force. Further experimental tests were aimed at establishing the presence of the triboelectric wear phenomena occurring during polymer machining and identifying the most influential process factors. Results indicate that feed rate is a significant factor in the generation of high optical surface quality. In addition, the depth of cut was identified as a significant factor in the generation of low surface roughness in lenses. The influence some of these process factors had was notably linked to triboelectric effects. This tribological effect was generated from the continuous rubbing action of magnetised chips on the cutting tool. This further stresses the presence of high static charging during cutting. Moderately humid cutting conditions presented an adequate means for static charge control and displayed improved surface finishes. In all experimental tests, the feed rate was identified as the most significant factor within the range of cutting parameters employed. Hence, the results validated the fact that feed rate had a high influence in polymer machining. The work also established the relationship on how surface roughness of an optical lens responded to monitoring signals and parameters such as force, feed, speed and depth of cut during machining and it generated models for prediction of surface finishes and appropriate selection of parameters. Furthermore, the study provides a molecular simulation analysis for validating observed conditions occurring at the nanometric scale in polymer machining. This is novel in molecular polymer modelling. The outcome of this research has contributed significantly to the body of knowledge and has provided basic information in the area of precision manufacturing of optical components of high surface integrity such as contact lenses. The application of the research findings presented here cuts across various fields such as medicine, semi-conductors, aerospace, defence, telecom, lasers, instrumentation and life sciences

Tool life prediction and management for an integrated tool selection system

In machining, it is often difficult to select appropriate tools (tool holder and insert), machining parameters (cutting speed, feed rate and depth of cut) and tool replacement times for all tools due to the wide variety of tooling options and the complexity of the machining operations. Of particular interest is the complex interrelationships between tool selection, cutting data calculation and tool life prediction and control. Numerous techniques and methods of measuring and modelling tool wear, particularly in turning operations were reviewed. The characteristics of these methods were analysed and it was found that most tool wear studies were self-contained without any obvious interface with tool selection. The work presented herein deals with the development of an integrated, off-line tool life control system (TLC). The tool life control system (TLC) predicts tool life for the various turning operations and for a wide variety of workpiece materials. TLC is a closed-loop system combining algorithms with feedback based on direct measurement of flank wear. TLC has been developed using Crystal, which is a rule-based shell and statistical techniques such as multiple regression and the least-squares method. TLC consists of five modules namely, the technical planning of the cutting operation (TPO), tool life prediction (TLP), tool life assessor (TLA), tool life management (TLM) and the tool wear balancing and requirement planning (TRP).The technical planning of the cutting operation (TPO) module contains a procedure to select tools and generate efficient machining parameters (cutting velocity, feed rate and depth of cut) for turning and boring operations. For any selected insert grade, material sub-class, type of cut (finishing, medium-roughing and roughing) and type of cutting fluid, the tool life prediction (TLP) module calculates the theoretical tool life value (T(_sugg)) based on tool life coefficients derived from tool manufacturers' data. For the selected operation, the tool life assessor (TLA) generates a dynamic multiple regression to calculate the approved tool life constants (InC, 1/a, 1/β) based on the real tool life data collected from experiments. These approved constants are used to calculate a modified tool life value (T(_mod)) for the given operation. The stochastic nature of tool life is taken into account, as well as the uncertainty of the available information by introducing a 95% confidence level for tool life. The tool life management module (TLM) studies the variations in tool life data predicted by TLP and TLA and the approved tool life data collected from the shop floor and provides feedback concerning the accuracy of tool life predictions. Finally, the tool life balancing and requirement planning (TRP) methods address the problem of controlling and balancing the wear rate of the cutting edge by the appropriate alteration of cutting conditions so that each one will machine the number of parts that optimize the overall tool changing strategy. Two new tool changing strategies were developed based on minimum production cost, with very encouraging results. Cutting experiments proved that the state of wear and the tool life can be predicted efficiently by the proposed model. The resulting software can be used by machine manufacturers, tool consultants or process planners to achieve the integrated planning and control of tool life as part of the tool selection and cutting data calculation activity

An investigation of mechanics in nanomachining of Gallium Arsenide

The first two decades of the 21st Century have seen a wide exploitation of Gallium Arsenide (GaAs) in photoemitter device, microwave devices, hall element, solar cell, wireless communication as well as quantum computation device due to its superior material properties, such as higher temperature resistance, higher electronic mobility and energy gap that outperforms silicon. Ultra-precision multiplex two dimensional (2D) or three dimensional (3D) free-form nanostructures are often required on GaAs-based devices, such as radio frequency power amplifiers and switches used in the 5G smart mobile wireless communication. However, GaAs is extremely difficult to machine as its elastic modulus, Knoop hardness and fracture toughness are lower than other semiconductor materials such as silicon and germanium. This PhD thesis investigated the mechanics of nanomachining of GaAs through molecular dynamics (MD) simulation combined with single point diamond turning (SPDT) and atomic force microscope (AFM) based experimental characterization in order to realise ductile-regime nanomachining of GaAs, which is the most important motivation behind this thesis. The investigation of mechanics of nanomachining of GaAs included studies on cutting temperature, cutting forces, origin ductile plasticity, atomic scale friction, formation mechanism of sub-surface damage, wear mechanism of diamond cutting tool. Machinability of GaAs at elevated temperature was also studied in order to develop thermally-assisted nanomachining process in the future to facilitate plastic material deformation and removal. This thesis contributed to address the knowledge gaps such as what is the incipient plasticity, how does the sub-surface damage form and how does the diamond cutting tool wear during nanomachining of GaAs. Firstly, this thesis investigated the cutting zone temperature, cutting forces and origin of plasticity of GaAs material, including single crystal GaAs and polycrystalline GaAs during SPDT process. The experimental and MD simulation study showed GaAs has a strong anisotropic machinability. The simulation results indicated that the deformation of polycrystalline GaAs is accompanied by dislocation nucleation in the grain boundaries (GBs) leading to the initiation of plastic deformation. Furthermore, the 1/2 is the main type of dislocation responsible for ductile plasticity in polycrystalline GaAs. A phenomenon of fluctuation from wave crests to wave troughs in the cutting forces was only observed during cutting of polycrystalline GaAs, not for single-crystal GaAs. Secondly, this thesis studied the atomic scale friction during AFM-based nanomachining process. a strong size effect was observed when the scratch depths are below 2 nm in MD simulations and 15 nm from the AFM experiments respectively. A strong quantitative corroboration was obtained between the MD simulations and the AFM experiments in the specific scratch energy and more qualitative corroboration with the pile up and the kinetic coefficient of friction. This conclusion suggested that the specific scratch energy is insensitive to the tool geometry and the speed of scratch used in this investigation but the pile up and kinetic coefficient of friction are dependent on the geometry of the tool tip. Thirdly, this thesis investigated formation mechanism of sub-surface damage and wear mechanism of diamond cutting tool during nanomachining of GaAs. Transmission Electron Microscope (TEM) measurement of sub-surface of machined nanogrooves on GaAs and MD simulation of dislocation movement indicated the dual slip mechanisms i.e. shuffle-set slip mechanism and glide-set slip mechanism, and the creation of dislocation loops, multi dislocation nodes, and dislocation junctions governed the formation mechanism of sub-surface damage of GaAs during nanomachining process. Elastic-plastic deformation at the apex of the diamond tip was observed in MD simulations. Meanwhile, a transition of the diamond tip from its initial cubic diamond lattice structure sp3 hybridization to graphite lattice structure sp2 hybridization was revealed. Graphitization was, therefore, found to be the dominant wear mechanism of the diamond tip during nanometric cutting of single crystal GaAs. Finally, in MD simulations study of cutting performance at elevated temperature, hotter conditions resulted in the reduction of cutting forces by 25% however, the kinetic coefficient of friction went up by about 8%. While material removal rate was found to increase with the increase of the substrate temperature, it was accompanied by an increase of the sub-surface damage in the substrate. Moreover, a phenomenon of chip densification was found to occur during hot cutting which referred to the fact that the amorphous cutting chips obtained from cutting at low temperature will have lower density than the chips obtained from cutting at higher temperatures.The first two decades of the 21st Century have seen a wide exploitation of Gallium Arsenide (GaAs) in photoemitter device, microwave devices, hall element, solar cell, wireless communication as well as quantum computation device due to its superior material properties, such as higher temperature resistance, higher electronic mobility and energy gap that outperforms silicon. Ultra-precision multiplex two dimensional (2D) or three dimensional (3D) free-form nanostructures are often required on GaAs-based devices, such as radio frequency power amplifiers and switches used in the 5G smart mobile wireless communication. However, GaAs is extremely difficult to machine as its elastic modulus, Knoop hardness and fracture toughness are lower than other semiconductor materials such as silicon and germanium. This PhD thesis investigated the mechanics of nanomachining of GaAs through molecular dynamics (MD) simulation combined with single point diamond turning (SPDT) and atomic force microscope (AFM) based experimental characterization in order to realise ductile-regime nanomachining of GaAs, which is the most important motivation behind this thesis. The investigation of mechanics of nanomachining of GaAs included studies on cutting temperature, cutting forces, origin ductile plasticity, atomic scale friction, formation mechanism of sub-surface damage, wear mechanism of diamond cutting tool. Machinability of GaAs at elevated temperature was also studied in order to develop thermally-assisted nanomachining process in the future to facilitate plastic material deformation and removal. This thesis contributed to address the knowledge gaps such as what is the incipient plasticity, how does the sub-surface damage form and how does the diamond cutting tool wear during nanomachining of GaAs. Firstly, this thesis investigated the cutting zone temperature, cutting forces and origin of plasticity of GaAs material, including single crystal GaAs and polycrystalline GaAs during SPDT process. The experimental and MD simulation study showed GaAs has a strong anisotropic machinability. The simulation results indicated that the deformation of polycrystalline GaAs is accompanied by dislocation nucleation in the grain boundaries (GBs) leading to the initiation of plastic deformation. Furthermore, the 1/2 is the main type of dislocation responsible for ductile plasticity in polycrystalline GaAs. A phenomenon of fluctuation from wave crests to wave troughs in the cutting forces was only observed during cutting of polycrystalline GaAs, not for single-crystal GaAs. Secondly, this thesis studied the atomic scale friction during AFM-based nanomachining process. a strong size effect was observed when the scratch depths are below 2 nm in MD simulations and 15 nm from the AFM experiments respectively. A strong quantitative corroboration was obtained between the MD simulations and the AFM experiments in the specific scratch energy and more qualitative corroboration with the pile up and the kinetic coefficient of friction. This conclusion suggested that the specific scratch energy is insensitive to the tool geometry and the speed of scratch used in this investigation but the pile up and kinetic coefficient of friction are dependent on the geometry of the tool tip. Thirdly, this thesis investigated formation mechanism of sub-surface damage and wear mechanism of diamond cutting tool during nanomachining of GaAs. Transmission Electron Microscope (TEM) measurement of sub-surface of machined nanogrooves on GaAs and MD simulation of dislocation movement indicated the dual slip mechanisms i.e. shuffle-set slip mechanism and glide-set slip mechanism, and the creation of dislocation loops, multi dislocation nodes, and dislocation junctions governed the formation mechanism of sub-surface damage of GaAs during nanomachining process. Elastic-plastic deformation at the apex of the diamond tip was observed in MD simulations. Meanwhile, a transition of the diamond tip from its initial cubic diamond lattice structure sp3 hybridization to graphite lattice structure sp2 hybridization was revealed. Graphitization was, therefore, found to be the dominant wear mechanism of the diamond tip during nanometric cutting of single crystal GaAs. Finally, in MD simulations study of cutting performance at elevated temperature, hotter conditions resulted in the reduction of cutting forces by 25% however, the kinetic coefficient of friction went up by about 8%. While material removal rate was found to increase with the increase of the substrate temperature, it was accompanied by an increase of the sub-surface damage in the substrate. Moreover, a phenomenon of chip densification was found to occur during hot cutting which referred to the fact that the amorphous cutting chips obtained from cutting at low temperature will have lower density than the chips obtained from cutting at higher temperatures

Prediction of the Wear & Evolution of Cutting Tools in a Carbide / Ti-6Al-4V Machining Tribosystem by Volumetric Tool Wear Characterization & Modeling

The objective of this research work is to create a comprehensive microstructural wear mechanism-based predictive model of tool wear in the tungsten carbide / Ti-6Al-4V machining tribosystem, and to develop a new topology characterization method for worn cutting tools in order to validate the model predictions. This is accomplished by blending first principle wear mechanism models using a weighting scheme derived from scanning electron microscopy (SEM) imaging and energy dispersive x-ray spectroscopy (EDS) analysis of tools worn under different operational conditions. In addition, the topology of worn tools is characterized through scanning by white light interferometry (WLI), and then application of an algorithm to stitch and solidify data sets to calculate the volume of the tool worn away. The motivation for this work is two-fold. First, the evolving dominance of different wear mechanisms with time, as well as with significant tool and process factors has been characterized only in a limited fashion for this tribosystem. Traditional modeling of tool wear treats wear mechanisms individually. Hence, quantifying the mechanism-dominance at different operational conditions through a comprehensive approach of combining and weighting wear mechanisms is essential for understanding wear. Second is the critical need for better quantifying the wear itself. Wear is a 3D phenomenon. However, machining tool wear has historically been measured only in 1D which is inadequate to capture the true tool wear status, even with standardization. The methodology was to first combine and weight dominant microstructural wear mechanism models, to be able to effectively predict the tool volume worn away. Then, by developing a new metrology method for accurately quantifying the bulk-3D wear, the model-predicted wear was validated against worn tool volumes obtained from corresponding machining experiments. The changing dominance of different microstructural wear mechanisms was captured by formulating mechanism-weighting-factors from SEM imaging and EDS analysis. These were formulated for each of the three speed-regimes, which then fed into a multi-mechanistic volumetric wear rate model. On comparing this model-predicted wear to the actual tool volume worn away, prediction on the order of the observed wear was achieved, with better prediction at low and medium surface speeds - this was quantified by sum-of-squares computations. On analyzing worn crater faces using SEM/EDS, adhesion was found dominant at lower surface speeds, while dissolution wear dominated with increasing speeds - this is in conformance with the lower relative surface speed requirement for micro welds to form and rupture, essentially defining the mechanical load limit of the tool material. It also conforms to the known dominance of high temperature-controlled wear mechanisms with increasing surface speed, which is known to exponentially increase temperatures especially when machining Ti-6Al-4V due to its low thermal conductivity. Thus, straight tungsten carbide wear when machining Ti-6Al-4V is mechanically-driven at low surface speeds and thermally-driven at high surface speeds. Further, at high surface speeds, craters were formed due to carbon diffusing to the tool surface and being carried away by the rubbing action of the chips - this left behind a smooth crater surface predominantly of tungsten and cobalt as observed from EDS analysis. Also, at high surface speeds, carbon from the tool was found diffused into the adhered titanium layer to form a titanium carbide (TiC) boundary layer - this was observed as instances of TiC build-up on the tool edge from EDS analysis. A complex wear mechanism interaction was thus observed, i.e., titanium adhered on top of an earlier worn out crater trough, additional carbon diffused into this adhered titanium layer to create a more stable boundary layer (which could limit diffusion-rates on saturation), and then all were further worn away by dissolution wear as temperatures increased. At low and medium feeds, notch discoloration was observed - this was detected to be carbon from EDS analysis, suggesting that it was deposited from the edges of the passing chips. Mapping the dominant wear mechanisms showed the increasing dominance of dissolution wear relative to adhesion, with increasing grain size - this is because a 13% larger sub-micron grain results in a larger surface area of cobalt exposed to chemical action. On the macro-scale, wear quantification through topology characterization elevated wear from a 1D to 3D concept. From investigation, a second order dependence of volumetric tool wear (VTW) and VTW rate with the material removal rate (MRR) emerged, suggesting that MRR is a more consistent wear-controlling factor instead of the traditionally used cutting speed. A predictive model for VTW was developed which showed its exponential dependence with workpiece stock volume removed. Also, both VTW and VTW rate were found to be dependent on the accumulated cumulative wear on the tool. Further, a ratio metric of stock material removed to tool volume lost is now possible as a tool efficiency quantifier and energy-based productivity parameter, which was found to inversely depend on MRR - this led to a more comprehensive tool wear definition based on cutting tool efficiency

Effect of the relative position of the face milling tooltowards the workpiece on machined surfaceroughness and milling dynamics

In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter's diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given95sem informaçãosem informaçã