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

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

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

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

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

Optimising temperature sensor placement for machine tool thermal error compensation

In this article, results of thermal error assessments are evaluated from a range of modern machine tools operating with active thermal compensation. The standard models assume a linear relationship between temperature and displacement and implementations address only a limited subset of error sources. However, significant residual errors were found on the analysed machines. The aim of this work is to improve the accuracy and increase the scope of compensated errors, without introducing onerous complexity, by using optimised linear correlation models applied to existing controllers

Towards semantic software engineering environments

Software tools processing partially common set of data should share an understanding of what these data mean. Since ontologies have been used to express formally a shared understanding of information, we argue that they are a way towards Semantic SEEs. In this paper we discuss an ontology-based approach to tool integration and present ODE, an ontology-based SEE

Fostering resilience-oriented thinking in engineering practice

Round tables discussing the resilience of critical infrastructure systems held in the UK, the USA and New Zealand have provided insight into how organisations are changing the basis of planning and investment decisions to enhance resilience. The events convened stakeholders to explore how resilience is embraced in their sectors and to identify how to advance practice. The overarching premise was to convene a diverse group who would not typically have an opportunity to engage with each other, to share their perspectives on putting resilience thinking into practice. The round tables identified that early-adopting organisations are implementing approaches to decision making that embrace resilience thinking, but such approaches are not yet embedded in common practice across organisations that are responsible for planning and managing critical infrastructure. The findings emphasise that multi-agency coordination and collaboration is required to advance resilience thinking in professional practice and to move beyond traditional risk-based paradigms. Governance and policy interventions will help encourage cross-sector information sharing and enforce responsibility and transparency surrounding exposure to potential shocks and stresses. It is recommended that such interventions could expand on principles and practice in existing emergency-management efforts, on the basis that such efforts are founded on coordinating various groups. </jats:p

Intercontinental transport of pollution and dust aerosols: implications for regional air quality

International audienceWe use the global model GOCART to examine the impact of pollution and dust aerosols emitted from their major sources on surface fine particulate matter concentrations at regional and hemispheric scales. Focusing on the North America region in 2001, we use measurements from the IMPROVE network in the United States to evaluate the model-simulated surface concentrations of the "reconstructed fine mass" (RCFM) and its components of ammonium sulfate, black carbon (BC), organic matter (OM), and fine mode dust. We then quantify the RCFM budget in terms of the RCFM composition, type, and origin to find that in the eastern U.S., ammonium sulfate is the dominant RCFM component (~60%) whereas in the western U.S., dust and OM are just as important as sulfate but have considerable seasonal variations, especially in the NW. On an annual average, North America regional pollution accounts for nearly 30?40% of the surface RCFM in the western U.S., and for a much higher proportion of 65?70% in the eastern U.S. By contrast, pollution from outside of North America contributes to just 2?6% (~0.2 ?g m?3) of the total RCFM over the U.S. on an annual average. In comparison, long-range transport of dust is more efficient than that of pollution, which brings 3 to 4 times more fine particles to the U.S. (0.5?0.8 ?g m?3 on an annual average) with a maximum influence in spring and over the NW. Of the major pollution regions, Europe has the largest potential to affect the surface aerosol concentrations in other continents due to its shorter distance from receptor continents and its larger fraction of sulfate-producing precursor gas in the outflow. With the IPCC emission scenario for the year 2000, we find that European emissions increase levels of ammonium sulfate by 1?5 ?g m?3 over the surface of northern Africa and western Asia, and its contribution to eastern Asia (?0.2 ?g m?3) is twice as much as the Asian contribution to North America. Asia and North America pollution emissions exert strong impacts on their neighboring oceans, but their influences on other continents are relatively small (?10%) due to the long traveling distance across the oceans and efficient removal during transport. Among the major dust source regions, Asia displays a significant influence over large areas in the Northern Hemisphere except over the North Atlantic and the tropics, where African dust dominates. The trans-Pacific transport of Asian dust is much more efficient than that of Asian pollution because of the higher elevation and lower removal rate of dust. We notice that the African dust can travel eastward through a pathway spanning across Asia and North Pacific to settle down over the surface of western North America. African dust transported through such a pathway is difficult to detect because it usually merges and travels together with the Asian dust

RNA-Seq reveals a xenobiotic stress response in the soybean aphid, Aphis glycines, when fed aphid-resistant soybean

Partial funding for Open Access provided by The Ohio State University Open Access Fund.Background: While much recent research has expanded our understanding of the molecular interactions between aphids and their host plants, it is lacking for the soybean aphid, Aphis glycines. Since its North American invasion, A. glycines has become one of the most damaging insect pests on this important crop. Five soybean genes for host plant resistance to A. glycines have been identified, but populations of A. glycines have already adapted to overcome these resistance genes. Understanding the molecular interactions between resistant soybean and A. glycines can provide clues to its adaptation mechanisms. Here, we used RNA-Sequencing to compare and contrast A. glycines gene expression when fed resistant (Rag1) and susceptible soybean. Results: Combining results from a previous A. glycines transcriptome, we generated 64,860 high quality transcripts, totaling 41,151,086 bases. Statistical analysis revealed 914 genes with significant differential expression. Most genes with higher expression in A. glycines on resistant plants (N = 352) were related to stress and detoxification such as cytochrome P450s, glutathione-S-transferases, carboxyesterases, and ABC transporters. A total of 562 genes showed lower transcript abundance in A. glycines on resistant plants. From our extensive transcriptome data, we also identified genes encoding for putative salivary effector proteins (N = 73). Among these, 6 effector genes have lower transcript abundance in A. glycines feeding on resistant soybean. Conclusions: Overall, A. glycines exhibited a pattern typical of xenobiotic challenge, thereby validating antibiosis in Rag1, presumably mediated through toxic secondary metabolites. Additionally, this study identified many A. glycines genes and gene families at the forefront of its molecular interaction with soybean. Further investigation of these genes in other biotypes may reveal adaptation mechanisms to resistant plants