Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.

A novel non-intrusive sampling technique for CO2 laser on-line beam monitoring utilising a silicon mirror

The measurement of high speed laser beam parameters during processing is a topic that has seen growing attention over the last few years as quality assurance places greater demand on the monitoring of the manufacturing process. The targets for any monitoring system is to be non-intrusive, low cost, simple to operate, high speed and capable of operation in process. A new ISO compliant system is presented based on the integration of an imaging plate and camera located behind a proprietary mirror sampling device. The general layout of the device is presented along with the thermal and optical performance of the sampling optic. Diagnostic performance of the system is compared with industry standard devices, demonstrating the high quality high speed data which has been generated using this system.

Effects of a large fishing closure on benthic communities in the western Gulf of Maine: recovery from the effects of gillnets and otter trawls

The recovery of benthic communities inside the western Gulf of Maine fishing closure area was evaluated by comparing invertebrate assemblages at sites inside and outside of the closure four to six years after the closure was established. The major restriction imposed by the closure was a year-round prohibition of bottom gillnets and otter trawls. A total of 163 seafloor sites (~half inside and half outside the closure) within a 515-km2 study area were sampled with some combination of Shipek grab, Wildco box corer, or underwater video. Bottom types ranged from mud (silt and clay) to boulders, and the effects of the closure on univariate measures (total density, biomass, taxonomic richness) of benthos varied widely among sediment types. For sites with predominantly mud sediments, there were mixed effects on inside and outside infauna and no effect on epifauna. For sites with mainly sand sediments, there were higher density, biomass, and taxonomic richness for infauna inside the closure, but no significant effects on epifauna. For sites dominated by gravel (which included boulders in some areas), there were no effects on infauna but strong effects on epifaunal density and taxonomic richness. For fishing gear, the data indicated that infauna recovered in sand from the impacts of otter trawls operated inside the closure but that they did not recover in mud, and that epifauna recovered on gravel bottoms from the impact of gillnets used inside the closure. The magnitudes of impact and recovery, however, cannot be inferred directly from our data because of a confounding factor of different fishing intensities outside the closure for a direct comparison of preclosure and postclosure data. The overall negative impact of trawls is likely underestimated by our data, whereas the negative impact of gillnets is likely overestimated.

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed

On-line learning of mutually orthogonal subspaces for face recognition by image sets.

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.

The influence of climate on phytoplankton communities in the upper San Francisco estuary

EXTRACT (SEE PDF FOR FULL ABSTRACT): The 1977 climate shift was characterized by low chlorophyll a concentrations and a shift in phytoplankton community composition throughout the upper San Francisco Bay estuary. ... For climate to be a driving force in phytoplankton communities, it must affect mechanisms that control biomass and community composition. The influence of climate on environmental conditions and phytoplankton community composition among water-year types was examined using 19 years of physical, chemical, and phytoplankton data collected monthly at 15 stations throughout the estuary.

On-line topographic analysis in the SEM

A novel method for on-line topographic analysis of rough surfaces in the SEM has been investigated. It utilises a digital minicomputer configured to act as a programmable scan generator and automatic focusing unit. The computer is coupled to the microscope through digital-to-analogue converters which enable it to generate ramp waveforms allowing the beam to be scanned over a small sub-region of the field under program control. A further digital-to-analogue converter regulates the current supply to the objective lens of the microscope. The video signal is sampled by means of an analogue-to-digital converter and the resultant binary code stored in the computer's memory as an array of numbers describing relative image intensity. Computations based on the intensity gradient of the image allow the objective lens current to be found for the in-focus condition, which may be related to the working distance through a previous calibration experiment. The sensitivity of the method for detecting small height changes is theoretically of the order of 1 μm. In practice the operator specifies features of interest by means of a mobile spot cursor injected into the SEM display screen, or he may scan the specimen at sub-regions corresponding to pre-determined points on a regular grid defined by him. The operation then proceeds under program control. | A novel method for on-line topographic analysis of rough surfaces in the SEM has been investigated. It utilizes a digital minicomputer configured to act as a programmable scan generator and automatic focusing unit. A further digital-to-analog converter regulates the current supply to the objective lens of the microscope. The video signal is sampled by means of an analog-to-digital converter and the resultant binary code stored in the computer's memory as an array of numbers describing relative image intensity. The sensitivity of the method for detecting small height changes is theroretically of the order of 1 mu m.

ON-LINE STEREO PROCESSING FOR THE SEM.

A convenient system for the rapid extraction of three dimensional information from pairs of SEM images has been constructed, eliminating the need for time-consuming photography. Results are produced in a digestable form. Distortions inherent in the SEM record display and in the photographic system are not relevant to the system described; only those arising within the column and stage need be considered.

Impact of dead and sunken water hyacinth on biotic communities, the aquatic environment and socio-economic activities

The mobile water hyacinth, which was produced in growth zones, especially Murchison Bay, was mainly exported to three sheltered storage bays (Thruston, Hannington and Waiya). Between 1996 and May 1998, the mobile form of water hyacinth occupied about 800 ha in Thruston Bay, 750 ha in Hannington Bay and 140 ha in Waiya Bay). Biological control weevils and other factors, including localised nutrient depletion, weakened the weed that was confined to the bays and it sunk around October 1998. The settling to the bottom of such huge quantities of organic matter its subsequent decomposition and the debris from this mass was likely to have environmental impacts on biotic communities (e.g. fish and invertebrate), physico-chemical conditions (water quality), and on socio-economic activities (e.g. at fish landings, water abstraction, and hydro-power generation points). Sunken water. hyacinth debris could also affect nutrient levels in the water column and lead to reduction in the content of dissolved oxygen. The changes in nutrient dynamics and oxygen levels could affect algal productivity, invertebrate composition and fish communities. Socio-economic impacts of dead sunken weed were expected from debris deposited along the shoreline especially at fish landings, water abstraction and hydropower generation points. Therefore, environmental impact assessment studies were carried out between 1998 and 2002 in selected representative zones of Lake Victoria to identify the effects of the sunken water hyacinth biomass