Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations

Machine tools are susceptible to exogenous influences, which mainly derive from varying environmental conditions such as the day and night or seasonal transitions during which large temperature swings can occur. Thermal gradients cause heat to flow through the machine structure and results in non-linear structural deformation whether the machine is in operation or in a static mode. These environmentally stimulated deformations combine with the effects of any internally generated heat and can result in significant error increase if a machine tool is operated for long term regimes. In most engineering industries, environmental testing is often avoided due to the associated extensive machine downtime required to map empirically the thermal relationship and the associated cost to production. This paper presents a novel offline thermal error modelling methodology using finite element analysis (FEA) which significantly reduces the machine downtime required to establish the thermal response. It also describes the strategies required to calibrate the model using efficient on-machine measurement strategies. The technique is to create an FEA model of the machine followed by the application of the proposed methodology in which initial thermal states of the real machine and the simulated machine model are matched. An added benefit is that the method determines the minimum experimental testing time required on a machine; production management is then fully informed of the cost-to-production of establishing this important accuracy parameter. The most significant contribution of this work is presented in a typical case study; thermal model calibration is reduced from a fortnight to a few hours. The validation work has been carried out over a period of over a year to establish robustness to overall seasonal changes and the distinctly different daily changes at varying times of year. Samples of this data are presented that show that the FEA-based method correlated well with the experimental results resulting in the residual errors of less than 12 μm

Towards obtaining robust boundary condition parameters to aid accuracy in FEA thermal error predictions

Finite Element Analysis (FEA) is used as a design tool within engineering industries due to the capability for rapid summative analysis accompanied by the visual aid. However, to represent realistic behaviour, FEA relies heavily on input parameters which must ideally be based on true figures such as data from experimental testing which sometimes requires time-consuming testing regimes. In the case of machine tool assemblies where complex structural joints and linkages are present, access to those areas can be a primary constraint to obtaining related boundary parameters such as heat flow across joints, for which, assumptions are incorporated to the FEA model which in effect increase the uncertainty in the FEA predictions. Similarly, in the case of thermal error modelling, simplifications are made when representing thermal boundary conditions such as the application of a uniform convection parameter to an assembly with parts assembled in both horizontal and vertical orientations. This research work aims to reduce the number of assumptions by providing experimentally obtained thermal boundary condition parameters. This work acknowledges experimental regimes that focus on obtaining thermal parameters related to the conduction across assembly joints (Thermal Contact Conductance-TCC) and measures the convection around areas such as belt drives and rotating parts to obtain convection parameters as inputs to the FEA. It provides TCC parameters for variable interfacial behaviour based on the varying contact pressure and the heat flow through dry and oiled contacts such as the conduction from spindle bearings to the surrounding housing and conduction from guideways into the associated assembly through carriages and contact bearings. It provides convection parameters across the test mandrel rotating at different speeds and around stationary structures such as convection parameters observed during TCC tests. It also provide details on the methods used to obtain all these parameters such as the use of thermal imaging, sensors placements and methods to obtain these boundary condition parameters. The significance of this work is to improve dramatically FEA thermal predictions, which are a critical part of engineering design. Although the focus is on machine tool design, the process and parameters can equally be applied to other areas of thermodynamic behaviour

Application of multi sensor data fusion based on Principal Component Analysis and Artificial Neural Network for machine tool thermal monitoring

Due to the various heat sources on a machine tool, there exists a complex temperature distribution across its structure. This causes an inherent thermal hysteresis which is undesirable as it affects the systematic tool –to-workpiece positioning capability. To monitor this, two physical quantities (temperature and strain) are measured at multiple locations. This article is concerned with the use of Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) to fuse this potentially large amount of data from multiple sources. PCA reduces the dimensionality of the data and thus reduces training time for the ANN which is being used for thermal modelling. This paper shows the effect of different levels of data compression and the application of rate of change of sensor values to reduce the effect of system hysteresis. This methodology has been successfully applied to the ram of a 5-axis gantry machine with 90 % correlation to the measured displacement

FEA-based design study for optimising non-rigid error detection on machine tools

Non-rigid-body behaviour can have a considerable effect on the overall accuracy performance of machine tools. These errors originate from bending of the machine structure due to change in distribution of its own weight or from movement of the workpiece and fixture. These effects should be reduced by good mechanical design, but residual errors can still be problematic due to realistic material and cost limitations. One method of compensation is to measure the deformation directly with sensors embedded in a metrology frame. This paper presents an FEA-based design study which assesses finite stiffness effects in both the machine structure and its foundation to optimise the sensitivity of the frame to the resulting errors. The study results show how a reference artefact, optimised by the FEA study, can be used to detect the distortion

Thermal Error Modelling of a CNC Machine Tool Feed Drive System using FEA Method

Recirculating ball screw systems are commonly used in machine tools and are one of the major heat sources which cause considerable thermal drift in CNC machine tools. Finite Element Analysis (FEA) method has been used successfully in the past to model the thermal characteristics of machine tools with promising results. Since FEA predictions are highly dependent on the efficacy of numerical parameters including the surrounding Boundary Conditions (BC), this study emphasises on an efficient modelling method to obtain optimised numerical parameters for acquiring a qualitative response from the feed drive system model. This study was performed on a medium size Vertical Machining Centre (VMC) feed drive system in which two parameter dentification methods have been employed; the general prediction method based on formulae provided by OEMs, and the energy balance method. The parameters obtained from both methods were applied to the FEA model of the machine feed drive system and validated against experimental results. Correlation with which was increased from 70 % to 80 % using the energy balance method

Areal surface measurement using multidirectional laser line scanning

The overall quality of a machined component has an important association with the quality of its surface finish. To obtain adequate data for the surface metrology of machined components, areal scanners are often preferred over stylus based profile scanners due to their ability to acquire surface data over a relatively large area. To further improve efficiency, there is a desire to perform on-machine measurement, and recently, high-resolution areal surface scanners have been integrated as an on-machine measurement device. Due to the limited areal coverage, these scanners can require multiple scans to capture data from surfaces produced on machine tools which requires a sufficient amount of time to complete a full surface scan. In addition, since these scanners are very sensitive, scanning delays often cause areal scanners to capture data contaminated with noise which may arise from within the machining environment such as axes vibrations, temperature effects, dust, etc. These factors mean such instruments are typically used in metrology laboratories. This paper presents a new methodology referred to as multidirectional scanning (MDS) which is a technique that exploits characteristics of a 2D laser line scanner (profilometer). The device is used in two directions to scan the overall component surface ensuring the coverage of a wider surface area compared to typical areal scanners. Since the scanner is robust and integrated onto a machine tool, controlled axes feed rates in the orthogonal directions ensure high spatial resolution which in turn helps to identify and reduce the noise levels in the data. This methodology has been validated to be both accurate and rapid to scan the component surface, reducing the cost associated with machine downtime and also having a wider coverage of 6x6 mm2 for a single scan, compared to 1 mm2 for most conventional areal surface measurement instruments having comparable spatial and vertical resolution

Precision Core Temperature Measurement of Metals Using an Ultrasonic Phase-Shift Method

Temperature measurement is one of the most important aspects of manufacturing. There have been many temperature measuring techniques applied for obtaining workpiece temperature in different types of manufacturing processes. The main limitations of conventional sensors have been the inability to indicate the core temperature of workpieces and the low accuracy that may result due to the harsh nature of some manufacturing environments. The speed of sound is dependent on the temperature of the material through which it passes. This relationship can be used to obtain the temperature of the material provided that the speed of sound can be reliably obtained. This paper investigates the feasibility of creating a cost-effective solution suitable for precision applications that require the ability to resolve a better than 0.5 °C change in temperature with ±1 °C accuracy. To achieve these, simulations were performed in MATLAB using the k-wave toolbox to determine the most effective method. Based upon the simulation results, experiments were conducted using ultrasonic phase-shift method on a steel sample (type EN24T). The results show that the method gives reliable and repeatable readings. Based on the results from this paper, the same setup will be used in future work in the machining environment to determine the effect of the harsh environment on the phase-shift ultrasonic thermometry, in order to create a novel technique for in-process temperature measurement in subtractive manufacturing processes

The significance of air pockets for modelling thermal errors of machine tools

It is well known, especially with the prevalence of compensation for geometric errors, that thermal error represents the most significant proportion of the total volumetric error of the machine tool. Thermal error in machine tools originates from changes to internal and external heat sources that vary the structural temperature of the machine tool resulting in the non-linear deformation of the machine structure. The ambient conditions inside and around the machine vicinity are varied not only by the external heat sources but, equally importantly but less well understood, by the machine itself when local air pockets are warmed inside the voids of the machine during the machining process. Air pockets are areas within the machine structure where the localized heat convection rate is reduced by the heat confined within them causing the temperature to vary slowly relative to the other places of the machine. This results in a relatively slower response of the associated structure. Consideration for this effect is an important, yet often ignored element of thermal modelling which deteriorates the prediction capability of many thermal models. This paper presents a case study where FEA (Finite Element Analysis) is used for the thermal modelling of a machine tool and the issue of air pockets is addressed by measuring and considering the temperature in voids. It was found that the consideration of the most significant air pockets improved the prediction capability of the FEA thermal model in the Z-axis direction from 50% to 62% when compared with the experimental results Z-axis. This paper highlights the significance of air pockets with regard to the thermal modelling and it is believed that the consideration of the temperature measurement inside voids of the machine structure and inclusion of their effect may significantly improve the performance of any thermal model

An efficient offline method for determining the thermally sensitive points of a machine tool structure

Whether from internal sources or arising from environmental sources, thermal error in most machine tools is inexorable. Out of several thermal error control methods, electronic compensation can be an easy-to-implement and cost effective solution. However, analytically locating the optimal thermally sensitive points within the machine structure for compensation has been a challenging task. This is especially true when complex structural deformations arising from the heat generated internally as well as long term environmental temperature fluctuations can only be controlled with a limited number of temperature inputs. This paper presents some case study results confirming the sensitivity to sensor location and a new efficient offline method for determining localized thermally sensitive points within the machine structure using finite element method (FEA) and Matlab software. Compared to the empirical and complex analytical methods, this software based method allows efficient and rapid optimization for detecting the most effective location(s) including practicality of installation. These sensitive points will contribute to the development and enhancement of new and existing thermal error compensation models respectively by updating them with the location information. The method is shown to provide significant benefits in the correlation of a simple thermal control model and comments are made on the efficiency with which this method could be practically applied

Efficient machine tool thermal error modelling strategy for accurate offline assessment

The requirement for improved dimensional accuracy to achieve ever tightening tolerances in manufactured parts increases the need for high precision machine tools. Machine tool accuracy is affected by various errors from which thermal errors have been identified as one of the largest contributors. These are primarily caused by heat generated by the machine as it operates and exogenous influences, mainly in the form of varying environmental temperature, that result in deformation of the machine structure. There is a complex interaction between the structural components having different heat sources, thermal time constants and thermal expansions and therefore the combined effect on tool position accuracy is often non-linear and difficult to correct easily. There has been considerable research effort to model this behaviour, usually based on temperature information, to compensate the induced errors. The methods and techniques have proved their capabilities with excellent thermal prediction and compensation results but they often require significant effort for effective implementation constraints for complexity, robustness, cost and time consumption. One of the most significant resources required is thermal testing on the machine and can be the main obstacle for the implementation of many of such methods for industries where production machine availability cannot be compromised. This research provides a method where the machine downtime can be reduced significantly using offline simulation techniques for extended and complex real world machine operations. In this research FEA is used to simulate the thermal behaviour of the entire structure of a small milling machine using Abaqus/CAE Standard FEA software. In order to ensure accurate simulations, heat source parameters need to be obtained for which an efficient methodology was created to calculate body heat flux values from a short test. Additionally, a study was conducted to understand the heat flow mechanism across structural joints requiring Thermal Contact Conductance (TCC) values. This research contributes experimentally obtained, and therefore accurate, TCC values for structural interface conditions compatible with CNC machine tool joints not previously available. This was followed by the investigation of the thermal behaviour of the machine due to both internal heat and external environmental fluctuations. A broad range of operating and static stability tests were conducted to validate the FEA modelling strategy for simulating the thermal behaviour of the machine for internal heating and environmental temperature fluctuations. The simulated and experimental movement of the tool matched by more than 60% in all cases; and by more than 70% in most cases. The most significant cost benefits from this project may result from understanding behaviour during the long and very long term simulations that are impractical or unfeasible to complete experimentally. This information facilitates capability assessment and model development. Within this research, simple linear models compatible with existing compensation capabilities in modern NC systems was targeted. Extracted FEA data is used to identify temperature-displacement sensitive areas within the full machine structure. The identification method locates structural nodes whose temperature change correlates with error at the tool to effectively install temperature sensors permanently at those positions for simple linearly correlated thermal error compensation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo