Gaynor Taylor

Genetic Algorithm Coding Methods for Leather Nesting

The problem of placing a number of specific shapes in order to minimise waste is commonly encountered in the sheet metal, clothing and shoe-making industries. The paper presents genetic algorithm coding methodologies for the leather nesting problem which involves cutting shoe upper components from hides so as to maximise material utilisation. Algorithmic methods for computer-aided nesting can be either packing or connectivity driven. The paper discusses approaches to how both types of method can be realised using a local placement strategy whereby one shape at a time is placed on the surface. In each case the underlying coding method is based on the use of the no-fit polygon (NFP) that allows the genetic algorithm to evolve non-overlapping configurations. The packing approach requires that a local space utilisation measure is developed. The connectivity approach is based on an adaptive graph method. Coding techniques for dealing with some of the more intractable aspects of the leather nesting problem such as directionality constraints and surface grading quality constraints are also discussed. The benefits and drawbacks of the two approaches are presented.

Built-in self test of S 2 I switched current circuits

This article presents a new concept for built-in self test of switched current circuits based on S2I memory cells. From the spectrum of possible transistor defects reported in CMOS processes [1] [2], five different fault-situations were modelled and the ability to detect the various failures was studied. This was accomplished by simulating a simple switched-current integrator in which all the different failures were introduced sequentially in all transistors. The fault coverage was derived and the result shows that a powerful system for detection of transistor faults in an analogue sampled-data system can be readily realised with a minimum of additional overhead circuitry.

Mixed current/voltage observation towards effective testing of analog and mixed-signal circuits

Power supply current monitoring is a promising technique for the development of new test methodologies for analog and mixed-signal circuits. The advantages of efficiency and reduced test time have already been recognized in the digital domain. This paper shows that improved test efficiency and detection confidence are obtained when both output voltage and power supply current are observed. Results found from the simulation of two circuits are presented comparing fault detection ratios and the amplitude of faulty response deviations when observing each one of these signals.Cross-correlation between output voltage and power supply current is presented as a means to perform a single mixed current/voltage testing operation, providing at the same time increased efficiency. It allows also for simplifications on test circuitry and processing, as reductions on the sampling rates and amplitude resolutions used to acquire the signals provide the same fault coverage levels obtained using higher fidelity measurements.

Concurrent self test of switched current circuits based on the S/sup 2/I-technique

This article presents a new concept for concurrent self test of switched current circuits based on S/sup 2/I cells as basic building blocks. Six different transistor faults were modelled and the ability to detect the various fault-situations was studied. The fault detection system presented was applied to a simple switched-current integrator in which all the different faults were introduced sequentially in all transistors. The fault coverage was derived and the result shows that a powerful system for detection of transistor faults in an analogue sampled-data system can be realised with a minimum of additional overhead circuitry.

Genetic algorithms applied to leather lay plan material utilization

Abstract This paper presents a genetic algorithm method for the leather-nesting problem that involves cutting shoe upper components from hides so as to maximize material utilization. A significant proportion of the manufacturing cost of a pair of shoes is invested in the natural raw material, and so the efficient utilization of this resource is of prime importance. Consequently, the part nesting and cutting process is one of the most important stages in the manufacture of leather shoes. The genetic algorithm method presented for leather lay-planning is capable of handling some of the more intractable aspects of the problem, namely multiple non-convex shapes, irregularly shaped hides, directionality constraints and surface grading quality issues. The underlying encoding method is based on the use of the no-fit polygon (NFP), lay angles and directionality angle constraints. The NFP allows the genetic algorithm to evolve non-overlapping configurations. Lay-plan results are presented using standard shoe component shapes and scanned hide input data conforming to a grading scale commonly used in shoemaking.

Use of power supply current and output voltage observation for testing large mixed-signal devices

Power supply current monitoring is a promising technique for the development of new test methodologies for analogue and mixed-signal circuits, following the success attained in the digital domain. This paper shows that improved test efficiency and confidence is obtained when both functional output voltage and power supply current signals are observed. Results obtained from simulation of a number of circuits are presented comparing Fault Coverage, Normalised Deviation, and the Coefficient of Variation of faulty responses deviation amplitudes. Also, different test stimuli are compared to evaluate their suitability for testing.

High performance trajectory control using a neural network cross-coupling gain scheduler

Cross-coupling control is an accepted methodology for improving contouring performance in multiaxial motion systems where axis interaction exists. This paper describes a new approach based on the use of neural networks for scheduling optimal cross-coupling gains for linear contours as the angle subtended with the x axis varies. The procedure for obtaining the optimal cross-coupling gains involves finding a minimum in a measured performance index. The experimental results for a biaxial system show that the proposed approach reduces contouring errors at test angles as compared to conventional uncoupled control of the axes. Measured performance indices are compared with and without cross-coupling at representative-angle to indicate the performance improvements that can be obtained with this approach.

Genetic Algorithm Optimisation of Part Placement Using a Connection-Based Coding Method

The problem of placing a number of specific shapes in order to minimise material waste is commonly encountered in the sheet metal, clothing and shoe-making industries. It is driven by the demand to find a layout of non-overlapping parts in a set area in order to maximise material utilisation. A corresponding problem is one of compaction, which is to minimise the area that a set number of shapes can be placed without overlapping. This paper presents a novel connectivity based approach to leather part compaction using the no-fit polygon (NFP). The NFP is computed using an image processing method as the boundary of the Minkowski sum, which is the convolution between two shapes at given orientations. These orientations along with shape order and placement selection constitute the chromosome structure.

Edge inspection in automatic stitching

The paper describes work relating to the laser line triangulation technique which has been used to inspect the edges of overlapping shoe components prior to the sewing operation. The laser line triangulation technique involves projecting a laser line on to a surface which can be viewed using an area camera. A surface height transition (edge) causes a discontinuity in the observed laser line. Different approaches for extracting the edge positions in the image co‐ordinate system have been investigated based on the Hough transform, the spatial histogram, polynomial regression and the discrete first derivative. These edge detection algorithms are compared in terms of speed and precision performance. Three‐dimensional scans of typical shoe component parts are presented.

Neural network cross-coupling gain controller for a bi-axial contouring system

An approach for improving the contouring performance of a computer controlled x-y table by using a neural network to interpolate between measured optimal cross-coupling gain values has been developed. The paper discusses the implementation of the proposed control structure for circular and other curved trajectories. Experimental results show that for a circular trajectory contour error is reduced as compared to the conventional uncoupled control of the two axes.