Janet F. Poliakoff

An improved algorithm for automatic fairing of non-uniform parametric cubic splines

Abstract Kjellander has reported an algorithm for fairing (i.e. smoothing) parametric cubic splines, which is restricted to splines that are uniformly parametrized ( Comput.-Aided Des. Vol 15 No 3 (1983) pp 175–179). In many commercial systems, non-uniform splines are required and therefore Kjellanders method of fairing cannot be used. This paper presents a new algorithm which extends that of Kjellander to the fairing of non-uniformly parametrized cubic splines, thus allowing the method to be used in automatic fairing for a wider range of curves. As with Kjellanders method, the new algorithm can be applied to splines in three dimensions as well as in two.

Automatic self-calibration of an incremental motion encoder

Our recently developed patented instrument, the Incremental Motion Encoder (IME) is able to monitor the rotation of mechanical systems. Using the disc from an Incremental Shaft Encoder, but with three or more read heads instead of one, the IME is able to resolve small transverse movements of the shaft as well as angular position. However, the accuracy of the measurements is limited by small distortions in the grating lines of the disc, due either to damage or to manufacturing errors. This paper presents algorithms which use data from the IME to compensate for such errors in the disc, thus enabling automatic self-calibration of the IME.

Error Correction in Scanned Engineering Drawings Using 3-D Knowledge-Based Reconstruction

This paper describes an improved algorithm for the automatic detection and correction of errors in scanned engineering drawings. The drawings are of simple machined components and have three orthographic projections with associated dimensioning and other textual information. The computer interpretation of scanned drawings is often made more difficult because lines in the original drawing are faded or very thin and are not detected by the scanner. We recently reported a method based on 3-D reconstruction, which can detect many cases of such missing lines but occasionally causes a line to be found incorrectly. This paper describes the evolution of our algorithm to avoid the detection of such “phantom” lines while still retaining the ability to detect lines which are genuinely missing.

A Combined High and Low Level Approach to Interpreting Scanned Engineering Drawings

The effective computer interpretation of engineering drawing remains a desirable aim yet it continues to provide academic challenge. Much early work was concerned with the interpretation of low level vectorised data. For simple drawings, direct association and interpretation of the low level data often provides a very effective technique but drawing data, whether linework or higher level textual information, can be subject to inaccuracies and uncertainties of interpretation. Thus drawing errors and problems introduced by scanning are likely to introduce ambiguities which cannot be resolved directly from the low level data. The approach described in this paper combines features of a low level approach based on node and vertex association with a higher level interpretation of the textual content of the drawing. The textual description of dimensions, etc. has previously been used by the authors and by others, for the correction of drawing structures, in some cases using 3-D reconstruction as a means of validating the data association. The present work attempts to model an aspect of human drawing interpretation, whereby an ‘envelope of expectation’ is developed, through the interpretation of dimensioning and annotation information. This approach allows a link to be established between the highest level information on the drawing (such as the title block) and the low level vectors of the three elevations. It is thus no longer necessary to interpret obscure detail within the vector data directly. Separation of text on the drawing using OCR techniques allows the field of interpretation for the linework to be significantly narrowed.

Incremental motion encoder: a sensor for the integrated condition monitoring of rolling element bearings in machine tools

Modern industry increasingly demands that machine tools operate continuously as elements within computer integrated manufacturing cells. Accurate monitoring of machine condition is the key to predicting failures that would result in quality defects or costly unplanned production stoppages. This paper presents research into the use of a novel rotary motion sensor for the condition monitoring of rolling element bearings. This sensor, the incremental motion encoder (IME), is based upon a patented development of the optical encoder commonly used in machine tools for sensing angular position and rotational speed. The IME combines these measurements with that of shaft center position in two dimensions. This motion of the shaft center is directly related to the condition of the bearings supporting the shaft. To illustrate the IME principle experimental results showing the ability of the sensor to distinguish between bearing defects and external sources of vibration are presented. Measurement of shaft loading with the sensor is also described. Currently machine tool condition is most often measured by external sensors, such as accelerometers or acoustic emission transducers, which are not parts of the machine itself. The IME is ideally suited to being designed into a machine tool so as to integrate a condition monitoring facility into the computer control of the machine. The paper concludes by describing the current technology which allows sensors based on the IME principle to be integrated directly into rolling element bearings for this purpose.

Automated path segmentation for 2-dimensional vectorised data

Effective identification of breakpoints from a given set of vectorised data is a crucial step in converting data from a scanner for use with a CAD/CAM system. In this paper we present our spline-based breakpoint detection algorithm, which is used to segment the vectorised data into straight and curved sections prior to curve fitting. Initial tests show that very satisfactory representations are generated, even for complex shapes. Only two parameters are required namely the tolerance /spl delta/ for the deviation of the curve from the vectorised data and the maximum length L for a vector which may be included in a curve.