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

Reducing the uncertainty of thermal model calibration using on-machine probing and data fusion

Various sources of error hinder the possibility of achieving tight accuracy requirements for high-value manufacturing processes. These are often classified as: pseudo-static geometric errors; non-rigid body errors; thermal errors; and dynamic errors. It is comparatively complicated to obtain an accurate error map for the thermal errors because they are influenced by various factors with different materials, time constants, asymmetric heating sources and machining process, environmental effects, etc. Their transient nature and complex interaction mean that they are relatively difficult to compensate using pre-calibration methods. For error correction, the magnitude and sign of the error must first be measured or estimated. Pre-calibrated thermal compensation has been shown to be an effective means of improving accuracy. However, the time required to acquire the calibration data is prohibitive, reducing the uptake of this technology in industrial applications. Furthermore, changing conditions of the machine or factory environment are not adequately accommodated by pre-calibrated compensation, leading to degradation in performance. The supplementary use of on-machine probing, which is often installed for process control, can help to achieve better results. During the probing operation, the probe is carried by the machine tool axes. Therefore, the measurement data that it takes inevitably includes both the probing errors and those originating from the inaccuracies of a machine tool as well as any deviation in the part or artefact being measured. Each of these error sources must be understood and evaluated to be able to establish a measurement with a stated uncertainty. This is a vital preliminary step to ensure that the calibration parameters of the thermal model are not contaminated by other effects. This thesis investigates the various sources of measurement uncertainties for probing on a CNC machine tool and quantify their effects in the particular case where the on-machine probing is used to calibrate the thermal error model. Thermal errors constitute the largest uncertainty source for on-machine probing. The maximum observed thermal displacement error was approximately 220 μm for both X and Z-axis heating test at 100 % speed. To reduce the influence of this uncertainty source, sensor data fusion model using artificial neural network and principal component analysis was developed. The output of this model showed better than 90 % correlation to the measured thermal displacement. This data fusion model was developed for the temperature and FBG sensors. To facilitate the integration of the sensor and to ease the communication with machine tool controller, a modular machine tool structural monitoring system using LabVIEW environment was developed. Finally, to improve the performance of the data fusion model in order to reduce the thermal uncertainty, a novel photo-microsensor based sensing head for displacement measurement is presented and analysed in detail. This prototype sensor has measurement range of 20 μm and resolution of 21 nm

Performance characterisation of a new photo-microsensor based sensing head for displacement measurement

This paper presents a robust displacement sensor with nanometre-scale resolution over a micrometre range. It is composed of low cost commercially available slotted photo-microsensors (SPMs). The displacement sensor is designed with a particular arrangement of a compact array of SPMs with specially designed shutter assembly and signal processing to significantly reduce sensitivity to ambient light, input voltage variation, circuit electronics drift, etc. The sensor principle and the characterisation results are described in this paper. The proposed prototype sensor has a linear measurement range of 20 μm and resolution of 21 nm. This kind of sensor has several potential applications, including mechanical structural deformation monitoring system

Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model

This paper presents a new modelling methodology for compensation of the thermal errors on a gantry-type 5-axis CNC machine tool. The method uses a “Grey Neural Network Model with Convolution Integral” (GNNMCI(1, N)), which makes full use of the similarities and complementarity between Grey system models and artificial neural networks (ANNs) to overcome the disadvantage of applying either model in isolation. A Particle Swarm Optimisation (PSO) algorithm is also employed to optimise the proposed Grey neural network. The size of the data pairs is crucial when the generation of data is a costly affair, since the machine downtime necessary to acquire the data is often considered prohibitive. Under such circumstances, optimisation of the number of data pairs used for training is of prime concern for calibrating a physical model or training a black-box model. A Grey Accumulated Generating Operation (AGO), which is a basis of the Grey system theory, is used to transform the original data to a monotonic series of data, which has less randomness than the original series of data. 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 study, temperature measurement at key locations was supplemented by direct distortion measurement at accessible locations. This form of data fusion 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. The Z-axis heating test, C-axis heating test, and the combined (helical) movement are considered in this work. The compensation values, calculated by the GNNMCI(1, N) model were sent to the controller for live error compensation. Test results show that a 85% reduction in thermal errors was achieved after compensation

Development of modular machine tool structural monitoring system

Although designed to be structurally stiff, machine tool deformation takes place due to the various sources of errors such as shifting mass, component weight, temperature etc. In order to facilitate research activities and acquire further scientific insight on the deformation process, a computer-based on-line monitoring system has been developed. A variety of sensors can be used to capture data for numerous parameters like temperature, displacement, strain etc. This paper presents the design and implementation of a LabVIEW based multi-sensor data acquisition program. It was designed in a three layer modular structure. In addition to data acquisition, the program is also capable of data processing, logging and implementing various error reduction techniques using online communication between LabVIEW and the MATLAB run-time engine for computation purpose. These calculated compensation values are then transferred to the machine controller via Ethernet. This paper also describes an example of application of such a system for a 5-axis CNC machine tool

An unusual eyelid mass of cysticercosis: A twist in the tale

Cysticercosis is a parasitic infestation caused by the larval form of the tapeworm, Taenia solium (T. solium). The common sites for cysticerosis include the brain, eyes, and skeletal muscle. Ocular or adnexal involvement is commonly seen with the commonest ophthalmic site being subretinal space and the vitreous cavity. However, only a handful of cases of eyelid cysticercosis have been reported in the past. We report a rare and unusual case of isolated eyelid cysticercosis in a middle-aged woman masquerading as an asymptomatic slowly growing subcutaneous painless mass in the left eyelid which was presumed to be a benign skin mass, a cyst of appendageal origin such as an epidermoid cyst. This case highlights the ubiquitous nature of cysticercosis in tropical countries and the need for a high degree of suspicion while surgically treating subcutaneous masses. We would additionally emphasize the need to rule out neurocysticercosis in such cases

Nontraumatic subperiosteal orbital hemorrhage following upper gastrointestinal endoscopy

Subperiosteal hemorrhages are typically the result of blunt orbital or facial trauma. Nontraumatic subperiosteal hemorrhages are uncommon and are usually attributed to increase in central venous pressure and bleeding disorders. Here, we report the case of a 38-year-old female who underwent an upper gastrointestinal (GI) endoscopy and developed bilateral nontraumatic subperiosteal hemorrhages that resolved with conservative treatment. Here, we discuss the source of bleeding and the mechanisms for the occurrence of orbital subperiosteal bleeds. GI surgeons and ophthalmologists should be sensitive to the possibility that orbital hemorrhage that can occur following endoscopy, especially when retching or gagging occurs during the procedure

Actinomycetes canaliculitis complicating congenital nasolacrimal duct obstruction in an infant

Actinomyces israelii is a Gram-positive anaerobic organism commonly associated with canaliculitis in adults. Pediatric canaliculitis is relatively rare, especially in infancy. We report the case of an 11-month-old boy who presented with co-existing canaliculitis and congenital nasolacrimal obstruction. The presenting signs included epiphora, discharge, conjunctival congestion, and matting of lashes. On examination, punctual pouting, regurgitation, and yellow canaliculiths were noted. A punctoplasty and canalicular curettage were performed along with nasolacrimal probing. Microbiological tests confirmed the organisms to be A. israelii. We discuss the clinical features and management of Actinomyces-associated canaliculitis and review the available literature on pediatric canaliculitis

Babies of South Asian and European ancestry show similar associations with genetic risk score for birth weight despite the smaller size of South Asian newborns

Size at birth is known to be influenced by various fetal and maternal factors, including genetic effects. South Asians have a high burden of low birth weight and cardiometabolic diseases, yet studies of common genetic variations underpinning these phenotypes are lacking. We generated independent, weighted fetal genetic scores (fGSs) and maternal genetic scores (mGSs) from 196 birth weight-associated variants identified in Europeans and conducted an association analysis with various fetal birth parameters and anthropometric and cardiometabolic traits measured at different follow-up stages (5-6-year intervals) from seven Indian and Bangladeshi cohorts of South Asian ancestry. The results from these cohorts were compared with South Asians in UK Biobank and the Exeter Family Study of Childhood Health, a European ancestry cohort. Birth weight increased by 50.7 g and 33.6 g per SD of fGS (P = 9.1 × 10-11) and mGS (P = 0.003), respectively, in South Asians. A relatively weaker mGS effect compared with Europeans indicates possible different intrauterine exposures between Europeans and South Asians. Birth weight was strongly associated with body size in both childhood and adolescence (P = 3 × 10-5 to 1.9 × 10-51); however, fGS was associated with body size in childhood only (P &lt; 0.01) and with head circumference, fasting glucose, and triglycerides in adults (P &lt; 0.01). The substantially smaller newborn size in South Asians with comparable fetal genetic effect to Europeans on birth weight suggests a significant role of factors related to fetal growth that were not captured by the present genetic scores. These factors may include different environmental exposures, maternal body size, health and nutritional status, etc. Persistent influence of genetic loci on size at birth and adult metabolic syndrome in our study supports a common genetic mechanism that partly explains associations between early development and later cardiometabolic health in various populations, despite marked differences in phenotypic and environmental factors in South Asians.</p