A comparative study of using spindle motor power and eddy current for the detection of tool conditions in milling processes

This paper investigates the use of the power of the driving motor of a CNC spindle in comparison to two perpendicular eddy current sensors for the detection of tool wear in milling processes. Monitoring the power through the current profile is a low cost system which has been utilised in this study as an attempt to detect the fluctuation in the motor load as a result of the conditions of the cutting tool. Eddy current sensors are dedicated sensors that are installed on the spindle to measure the vibration of the rotating spindle in two axes. Experimental work has been conducted using fresh and worn tools to investigate the effect of tool conditions on the two sensory systems. Time domain features are utilised to compare between the two sensors in relation to this application. The results indicate that Eddy current sensors are found to be more successful and sensitive, in general, than the power of the motor in detecting the changes of the cutting tools during the machining operation. However, the kurtosis value of the power of the spindle has been found to be successful in predicting the tool conditions with high sensitivity

ON THE STABILITY OF VARIABLE HELIX MILLING TOOLS

One of the main aims of the manufacturing industry has been to maximise the material removal rate of machining processes. However, this goal can be restricted by the appearance of regenerative chatter vibrations. In milling, one approach for regenerative chatter suppression is the implementation of variable-helix cutters. However, these tools can lead to isolated unstable regions in the stability diagram. Currently, variable-helix unstable islands have not been extensively researched in the literature. Therefore, the current thesis focuses on studying and experimentally validating these islands. For the validation, an experimental setup that scaled not only the structural dynamics but also the cutting force coefficients was proposed. Therefore, it was possible to attain larger axial depths of cut while assuming linear dynamics. The variable-helix process stability was modelled using the semi-discretization method and the multi-frequency approach. It was found that the variable helix tools can further stabilise a larger width of cut due to the distributed time delays that are a product of the tool geometry. Subsequently, a numerical study about the impact of structural damping on the variable-helix stability diagram revealed a strong relationship between the damping level and instability islands. The findings were validated by performing trials on the experimental setup, modified with constrained layer damping to recreate the simulated conditions. Additionally, a convergence analysis using the semi-discretization method (SDM) and the multi-frequency approach (MFA) revealed that these islands are sensitive to model convergence aspects. The analysis shows that the MFA provided converged solutions with a steep convergence rate, while the SDM struggled to converge. In this work, it is demonstrated that variable-helix instability islands only emerge at relatively high levels of structural damping and that they are particularly susceptible to model convergence effects. Meanwhile, the model predictions are compared to and validated against detailed experimental data that uses a specially designed configuration to minimise experimental error. To the authors' knowledge, this provides the first experimentally validated study of unstable islands in variable helix milling, while also demonstrating the importance of accurate damping estimates and convergence studies within the stability predictions

Modelling and real-time control of a high performance rotary wood planing machine

Rotary planing is one of the most valuable machining operations in the timber processing industry. It has been established that cutting tool inaccuracy and forced vibration during the machining process are the primary causes of surface quality degradation. The main aim of this thesis is to design a control architecture that is suitable for adaptive operation of a wood planing machining in order to improve the quality of its surface finish. In order to achieve the stated goal, thorough understanding of the effects of machine deficiencies on surface finish quality is required. Therefore, a generic simulation model for synthesising the surface profiles produced by wood planing process is first developed. The model is used to simulate the combined effects of machining parameters, vibration and cutting tool inaccuracy on the resultant surface profiles. It has been postulated that online monitoring of surface finish quality can be used to provide feedback information for a secondary control loop for the machining process, which will lead to the production of consistently high quality surface finishes. There is an existing vision-based wood surface profile measurement technique, but the application of the technique has been limited to static wood samples. This thesis extends the application of the technique to moving wood samples. It is shown experimentally that the method is suitable for in-process surface profile measurements. The current industrial wood planing machines do not have the capability of measuring and adjusting process parameters in real-time. Therefore, knowledge of the causes of surface finish degradation would enable the operators to optimise the mechanical structure of the machines offline. For this reason, two novel approaches for characterising defects on planed timber surfaces have been created in this thesis using synthetic data. The output of this work is a software tool that can assist machine operators in inferring the causes of defects based on the waviness components of the workpiece surface finish. The main achievement in this research is the design of a new active wood planing technique that combines real-time cutter path optimisation (cutting tool inaccuracy compensation) with vibration disturbance rejection. The technique is based on real-time vertical displacements of the machine spindle. Simulation and experimental results obtained from a smart wood planing machine show significant improvements in the dynamic performance of the machine and the produced surface finish quality. Potential areas for future research include application of the defects characterisation techniques to real data and full integration of the dynamic surface profile measurements with the smart wood planing machine

Active chatter control in high-speed milling processes

In present day manufacturing industry, an increasing demand for highprecision products at a high productivity level is seen. High-speed milling is a manufacturing technique which is commonly exploited to produce highprecision parts at a high productivity level for the aeroplane, automotive and mould and dies industry. The performance of a manufacturing process such as high-speed milling, indicated by the material removal rate, is limited by the occurrence of a dynamic instability phenomenon called chatter. The occurrence of chatter results in an inferior workpiece quality due to heavy vibrations of the cutter. Moreover, a high level of noise is produced and the tool wears out rapidly. Although different types of chatter exist, regenerative chatter is recognised as the most prevalent type of chatter. The occurrence of (regenerative) chatter has such a devastating effect on workpiece quality and tool wear that it should be avoidedat all times. The occurrence of chatter can be visualised in so-called stability lobes diagrams (sld). In an sld the chatter stability boundary between a stable cut (i.e. without chatter) and an unstable cut (i.e. with chatter) is visualised in terms of spindle speed and depth of cut. Using the information gathered in a sld, the machinist can select a chatter free operating point. In this thesis two problems are tackled. Firstly, due to e.g. heating of the spindle, tool wear, etc., the sld may vary in time. Consequently, a stable working point that was originally chosen by the machinist may become unstable. This requires a (controlled) adaptation of process parameters such that stability of the milling process is ensured (i.e. chatter is avoided) even under such changing process conditions. Secondly, the ever increasing demand for high-precision products at a high productivity level requires dedicated shaping of the chatter stability boundary. Such shaping of the sld should render working points (in terms of spindle speed and depth of cut) of high productivity feasible, while avoiding chatter. These problems require the design of dedicated control strategies that ensure stable high-speed milling operations with increased performance. In this work, two chatter control strategies are developed that guarantee high-speed chatter-free machining operations. The goal of the two chatter control strategies is, however, different. The first chatter control strategy guarantees chatter-free high-speed milling operations by automatic adaptation of spindle speed and feed (i.e. the feed is not stopped during the spindle speed transition). In this way, the high-speed milling process will remain stable despite changes in the process, e.g. due to heating of the spindle, tool wear, etc. To do so, an accurate and fast chatter detection algorithm is presented which predicts the occurrence of chatter before chatter marks are visible on the workpiece. Once the onset of chatter is detected, the developed controller adapts the spindle speed and feed such that a new chatter-free working point is attained. Experimental results confirm that by using this control strategy chatter-free machining is ensured. It is also shown experimentally that the detection algorithm is able to detect chatter before it is fully developed. Furthermore, the control strategy ensures that chatter is avoided, thereby ensuring a robust machining operation and a high surface quality. The second chatter control strategy is developed to design controllers that guarantee chatter-free cutting operations in an a priori defined range of process parameters (spindle speed and depth of cut) such that a higher productivity can be attained. Current (active) chatter control strategies for the milling process cannot provide such a strong guarantee of a priori stability for a predefined range of working points. The methodology is based on a robust control approach using µ-synthesis, where the most important process parameters (spindle speed and depth of cut) are treated as uncertainties. The proposed methodology will allow the machinist to define a desired working range (in spindle speed and depth of cut) and lift the sld locally in a dedicated fashion. Finally, experiments have been performed to validate the working principle of the active chatter control strategy in practice. Hereto, a milling spindle with an integrated active magnetic bearing is considered. Based on the obtained experimental results, it can be stated that the active chatter control methodology, as presented in this thesis, can indeed be applied to design controllers, which alter the sld such that a pre-defined domain of working points is stabilised. Results from milling tests underline this conclusion. By using the active chatter controller working points with a higher material removal rate become feasible while avoiding chatter. To summarise, the control strategies developed in this thesis, ensure robust chatter-free high-speed milling operations where, by dedicated shaping of the chatter stability boundary, working points with a higher productivity are attained

The Application of ANN and ANFIS Prediction Models for Thermal Error Compensation on CNC Machine Tools

Thermal errors can have significant effects on Computer Numerical Control (CNC) machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This thesis first reviews different methods of designing thermal error models, before concentrating on employing Artificial Intelligence (AI) methods to design different thermal prediction models. In this research work the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as the backbone for thermal error modelling. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this thesis, temperature measurement was supplemented by direct distortion measurement at accessible locations. The location of temperature measurement must also provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this thesis, a new intelligent system for reducing thermal errors of machine tools using data obtained from thermography data is introduced. Different groups of key temperature points on a machine can be identified from thermal images using a novel schema based on a Grey system theory and Fuzzy C-Means (FCM) clustering method. This novel method simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. An Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means clustering (ANFIS-FCM) is then employed to design the thermal prediction model. In order to optimise the approach, a parametric study is carried out by changing the number of inputs and number of Membership Functions (MFs) to the ANFIS-FCM model, and comparing the relative robustness of the designs. The proposed approach has been validated on three different machine tools under different operation conditions. Thus the proposed system has been shown to be robust to different internal heat sources, ambient changes and is easily extensible to other CNC machine tools. Finally, the proposed method is shown to compare favourably against alternative approaches such as an Artificial Neural Network (ANN) model and different Grey models

Novel control of a high performance rotary wood planing machine

Rotary planing, and moulding, machining operations have been employed within the woodworking industry for a number of years. Due to the rotational nature of the machining process, cuttermarks, in the form of waves, are created on the machined timber surface. It is the nature of these cuttermarks that determine the surface quality of the machined timber. It has been established that cutting tool inaccuracies and vibrations are a prime factor in the form of the cuttermarks on the timber surface. A principal aim of this thesis is to create a control architecture that is suitable for the adaptive operation of a wood planing machine in order to improve the surface quality of the machined timber. In order to improve the surface quality, a thorough understanding of the principals of wood planing is required. These principals are stated within this thesis and the ability to manipulate the rotary wood planing process, in order to achieve a higher surface quality, is shown. An existing test rig facility is utilised within this thesis, however upgrades to facilitate higher cutting and feed speeds, as well as possible future implementations such as extended cutting regimes, the test rig has been modified and enlarged. This test rig allows for the dynamic positioning of the centre of rotation of the cutterhead during a cutting operation through the use of piezo electric actuators, with a displacement range of ±15μm. A new controller for the system has been generated. Within this controller are a number of tuneable parameters. It was found that these parameters were dependant on a high number external factors, such as operating speeds and run‐out of the cutting knives. A novel approach to the generation of these parameters has been developed and implemented within the overall system. Both cutterhead inaccuracies and vibrations can be overcome, to some degree, by the vertical displacement of the cutterhead. However a crucial information element is not known, the particular displacement profile. Therefore a novel approach, consisting of a subtle change to the displacement profile and then a pattern matching approach, has been implemented onto the test rig. Within the pattern matching approach the surface profiles are simplified to a basic form. This basic form allows for a much simplified approach to the pattern matching whilst producing a result suitable for the subtle change approach. In order to compress the data levels a Principal Component Analysis was performed on the measured surface data. Patterns were found to be present in the resultant data matrix and so investigations into defect classification techniques have been carried out using both K‐Nearest Neighbour techniques and Neural Networks. The application of these novel approaches has yielded a higher system performance, for no additional cost to the mechanical components of the wood planing machine, both in terms of wood throughput and machined timber surface quality

Design, control and error analysis of a fast tool positioning system for ultra-precision machining of freeform surfaces

This thesis was previously held under moratorium from 03/12/19 to 03/12/21Freeform surfaces are widely found in advanced imaging and illumination systems, orthopaedic implants, high-power beam shaping applications, and other high-end scientific instruments. They give the designers greater ability to cope with the performance limitations commonly encountered in simple-shape designs. However, the stringent requirements for surface roughness and form accuracy of freeform components pose significant challenges for current machining techniques—especially in the optical and display market where large surfaces with tens of thousands of micro features are to be machined. Such highly wavy surfaces require the machine tool cutter to move rapidly while keeping following errors small. Manufacturing efficiency has been a bottleneck in these applications. The rapidly changing cutting forces and inertial forces also contribute a great deal to the machining errors. The difficulty in maintaining good surface quality under conditions of high operational frequency suggests the need for an error analysis approach that can predict the dynamic errors. The machining requirements also impose great challenges on machine tool design and the control process. There has been a knowledge gap on how the mechanical structural design affects the achievable positioning stability. The goal of this study was to develop a tool positioning system capable of delivering fast motion with the required positioning accuracy and stiffness for ultra-precision freeform manufacturing. This goal is achieved through deterministic structural design, detailed error analysis, and novel control algorithms. Firstly, a novel stiff-support design was proposed to eliminate the structural and bearing compliances in the structural loop. To implement the concept, a fast positioning device was developed based on a new-type flat voice coil motor. Flexure bearing, magnet track, and motor coil parameters were designed and calculated in detail. A high-performance digital controller and a power amplifier were also built to meet the servo rate requirement of the closed-loop system. A thorough understanding was established of how signals propagated within the control system, which is fundamentally important in determining the loop performance of high-speed control. A systematic error analysis approach based on a detailed model of the system was proposed and verified for the first time that could reveal how disturbances contribute to the tool positioning errors. Each source of disturbance was treated as a stochastic process, and these disturbances were synthesised in the frequency domain. The differences between following error and real positioning error were discussed and clarified. The predicted spectrum of following errors agreed with the measured spectrum across the frequency range. It is found that the following errors read from the control software underestimated the real positioning errors at low frequencies and overestimated them at high frequencies. The error analysis approach thus successfully revealed the real tool positioning errors that are mingled with sensor noise. Approaches to suppress disturbances were discussed from the perspectives of both system design and control. A deterministic controller design approach was developed to preclude the uncertainty associated with controller tuning, resulting in a control law that can minimize positioning errors. The influences of mechanical parameters such as mass, damping, and stiffness were investigated within the closed-loop framework. Under a given disturbance condition, the optimal bearing stiffness and optimal damping coefficients were found. Experimental positioning tests showed that a larger moving mass helped to combat all disturbances but sensor noise. Because of power limits, the inertia of the fast tool positioning system could not be high. A control algorithm with an additional acceleration-feedback loop was then studied to enhance the dynamic stiffness of the cutting system without any need for large inertia. An analytical model of the dynamic stiffness of the system with acceleration feedback was established. The dynamic stiffness was tested by frequency response tests as well as by intermittent diamond-turning experiments. The following errors and the form errors of the machined surfaces were compared with the estimates provided by the model. It is found that the dynamic stiffness within the acceleration sensor bandwidth was proportionally improved. The additional acceleration sensor brought a new error source into the loop, and its contribution of errors increased with a larger acceleration gain. At a certain point, the error caused by the increased acceleration gain surpassed other disturbances and started to dominate, representing the practical upper limit of the acceleration gain. Finally, the developed positioning system was used to cut some typical freeform surfaces. A surface roughness of 1.2 nm (Ra) was achieved on a NiP alloy substrate in flat cutting experiments. Freeform surfaces—including beam integrator surface, sinusoidal surface, and arbitrary freeform surface—were successfully machined with optical-grade quality. Ideas for future improvements were proposed in the end of this thesis.Freeform surfaces are widely found in advanced imaging and illumination systems, orthopaedic implants, high-power beam shaping applications, and other high-end scientific instruments. They give the designers greater ability to cope with the performance limitations commonly encountered in simple-shape designs. However, the stringent requirements for surface roughness and form accuracy of freeform components pose significant challenges for current machining techniques—especially in the optical and display market where large surfaces with tens of thousands of micro features are to be machined. Such highly wavy surfaces require the machine tool cutter to move rapidly while keeping following errors small. Manufacturing efficiency has been a bottleneck in these applications. The rapidly changing cutting forces and inertial forces also contribute a great deal to the machining errors. The difficulty in maintaining good surface quality under conditions of high operational frequency suggests the need for an error analysis approach that can predict the dynamic errors. The machining requirements also impose great challenges on machine tool design and the control process. There has been a knowledge gap on how the mechanical structural design affects the achievable positioning stability. The goal of this study was to develop a tool positioning system capable of delivering fast motion with the required positioning accuracy and stiffness for ultra-precision freeform manufacturing. This goal is achieved through deterministic structural design, detailed error analysis, and novel control algorithms. Firstly, a novel stiff-support design was proposed to eliminate the structural and bearing compliances in the structural loop. To implement the concept, a fast positioning device was developed based on a new-type flat voice coil motor. Flexure bearing, magnet track, and motor coil parameters were designed and calculated in detail. A high-performance digital controller and a power amplifier were also built to meet the servo rate requirement of the closed-loop system. A thorough understanding was established of how signals propagated within the control system, which is fundamentally important in determining the loop performance of high-speed control. A systematic error analysis approach based on a detailed model of the system was proposed and verified for the first time that could reveal how disturbances contribute to the tool positioning errors. Each source of disturbance was treated as a stochastic process, and these disturbances were synthesised in the frequency domain. The differences between following error and real positioning error were discussed and clarified. The predicted spectrum of following errors agreed with the measured spectrum across the frequency range. It is found that the following errors read from the control software underestimated the real positioning errors at low frequencies and overestimated them at high frequencies. The error analysis approach thus successfully revealed the real tool positioning errors that are mingled with sensor noise. Approaches to suppress disturbances were discussed from the perspectives of both system design and control. A deterministic controller design approach was developed to preclude the uncertainty associated with controller tuning, resulting in a control law that can minimize positioning errors. The influences of mechanical parameters such as mass, damping, and stiffness were investigated within the closed-loop framework. Under a given disturbance condition, the optimal bearing stiffness and optimal damping coefficients were found. Experimental positioning tests showed that a larger moving mass helped to combat all disturbances but sensor noise. Because of power limits, the inertia of the fast tool positioning system could not be high. A control algorithm with an additional acceleration-feedback loop was then studied to enhance the dynamic stiffness of the cutting system without any need for large inertia. An analytical model of the dynamic stiffness of the system with acceleration feedback was established. The dynamic stiffness was tested by frequency response tests as well as by intermittent diamond-turning experiments. The following errors and the form errors of the machined surfaces were compared with the estimates provided by the model. It is found that the dynamic stiffness within the acceleration sensor bandwidth was proportionally improved. The additional acceleration sensor brought a new error source into the loop, and its contribution of errors increased with a larger acceleration gain. At a certain point, the error caused by the increased acceleration gain surpassed other disturbances and started to dominate, representing the practical upper limit of the acceleration gain. Finally, the developed positioning system was used to cut some typical freeform surfaces. A surface roughness of 1.2 nm (Ra) was achieved on a NiP alloy substrate in flat cutting experiments. Freeform surfaces—including beam integrator surface, sinusoidal surface, and arbitrary freeform surface—were successfully machined with optical-grade quality. Ideas for future improvements were proposed in the end of this thesis

An integrated approach to tool life management

Tool wear is a complex phenomenon occurring in all metal cutting processes. It reduces dimensional accuracy, impairs the surface integrity of the component and can have profound effects on the overall quality of the machined workpiece. Tool condition monitoring methods can be broadly split based on the source of signals collected by sensors into direct and indirect methods. In real life, it is not simple to model or predict. This thesis considers current shortcomings in applied approaches to tool management to demonstrate the need for more accurate assessment of tool condition and particularly remaining tool life. In this study, two kinds of indirect acquisition methods were used to estimate the tool wear. The post process method utilises the measurement of component geometry using a Coordinate Measure Machine. The in-process method utilises the acquisition and analysis of the applied spindle load from which tool wear can be estimated. A series of tests were conducted based upon the machining of a set of cylindrical holes. Two different diameter tools, 10 mm and 16 mm end mills, were used. The CMM acquired component geometry was used to calculate the tool wear indirectly. The method was proved to provide a good indication of the tool wear behaviour. In particular the approach is shown to be helpful for identifying the important change in the rate of tool wear. The developed online monitoring system, using the spindle motor load signal, is introduced in this thesis. It provides a practical method for detecting the progression of flank wear during machining. The results concluded that the signal amplitudes are increased when the flank wear increases. High cutting speed cause the flank wear to form quickly and shorten the tool life. This is an efficient and low-cost method that, with further development and testing, can be used in the real machining industry to predict the actual wear in the cutting tool

Development of a Closed Loop Control System for Vibratory Milling

Manufacturing makes ever-increasing demands for higher machining speeds. This is particularly true in car and aircraft production, but also for cutting tools. Vibration is used in various technological processes to improve the performance of the machines by intelligently exploiting the synergy of the oscillations. Vibration provides several benefits for various technologies, such as manufacturing, medical, communications, transport, industries, etc. Vibration assisted machining techniques have recently become an area of interest for many engineering applications. In machining processes, vibration can lead to improvements when applied in a controlled manner. Vibration assisted machining is a technique in which a certain frequency of vibration is applied to the cutting tool or the Workpiece to achieve better cutting performance The aim of this project is to apply vibration to the work-piece during milling process in order to improve the machining performance. In this project, a theoretical modelling and experimental implementation of vibratory milling process are presented and explored in depth. The modelling focused on the control system which tracked and regulated the vibration amplitude in the cutting zone during machining. Here, hardware and software of advanced technology of LabVIEW applications were used to develop implement and optimise the control system. The machine tool static, dynamic and compliance characteristics were investigated in terms of static analysis, natural frequencies and dynamic stiffness, using harmonic excitation, hammer impact test and the application of external forces. Preliminary studies were undertaken, where, the effect of cutting parameters were evaluated and the optimal cutting conditions were determined. Series of machining tests were undertaken, with the aim of recording process performance data in terms of cutting forces that were used for the development of the control system. A closed loop PID controller was developed using advanced Field-Programming-Gate-Array (FPGA) and Real-time Labview applications, using a non-interrupted real time target PC. An innovative and unique combination of FPGA and target PC allow the control system to have a very fast response in keeping the set amplitude of the vibration whilst recording simultaneously the machining data for further analysis. Aluminium and mild Steel were using in this investigation, along with a comparative study between conventional and vibratory milling and between open loop and closed loop control systems. The results of this investigation show the benefits of the superimposed vibration. The outperformance of the vibratory machining over the conventional milling provides a very promising outlook for the application of subsonic vibration into machining as an alternative to ultrasonic process