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

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

Teaching children road safety through storybooks: an approach to child health literacy in Pakistan

Background: Road traffic injuries (RTIs) commonly affect the younger population in low- and-middle-income countries. School children may be educated about road safety using storybooks with colorful pictures, which tends to increase the child’s interest in the text. Therefore, this study assessed the use of bilingual pictorial storybooks to improve RTI prevention knowledge among school children.Methods: This pretest-posttest study was conducted in eight public and nine private schools of Karachi, Pakistan, between February to May 2015. Children in grades four and five were enrolled at baseline (n = 410). The intervention was an interactive discussion about RTI prevention using a bilingual (Urdu and English) pictorial storybook. A baseline test was conducted to assess children’s pre-existing knowledge about RTI prevention followed by administration of the intervention. Two posttests were conducted: first immediately after the intervention, and second after 2 months. Test scores were analyzed using McNemar test and paired sample t-test. Results: There were 57% girls and 55% public school students; age range 8–16 years. Compared to the overall baseline score (5.1 ± 1.4), the number of correct answers increased in both subsequent tests (5.9 ± 1.2 and 6.1 ± 1.1 respectively, p-value \u3c 0.001). Statistically significant improvement in mean scores was observed based on gender, grades and school type over time (p-value \u3c 0.001).Conclusion: Discussions using bilingual pictorial storybooks helped primary school children in Pakistan grasp knowledge of RTI prevention. RTI education sessions may be incorporated into school curricula using storybooks as teaching tools. Potential exists to create similar models for other developing countries by translating the storybooks into local languages