5 research outputs found
Feature Recognition for Interactive Applications: Exploiting Distributed Resources
The availability of low-cost computational power is a driving
force behind the growing sophistication of CAD software. Tools designed
to reduce time-consuming build-test-redesign iterations are essential
for increasing engineering quality and productivity. However, automation
of the design process poses many difficult computational problems. As
more downstream engineering activities are being considered during the
design phase, guaranteeing reasonable response times within design
systems becomes problematic. Design is an interactive process and speed
is a critical factor in systems that enable designers to explore and
experiment with alternative ideas during the design phase. Achieving
interactivity requires an increasingly sophisticated allocation
of computational resources in order to perform realistic design analyses
and generate feedback in real time.
This paper presents our initial efforts to develop techniques to
apply distributed algorithms to the problem of recognizing machining
features from solid models. Existing work on recognition of features has
focused exclusively on serial computer architectures. Our objective is to
show that distributed algorithms can be employed on realistic parts
with large numbers of features and many geometric and topological
entities to obtain significant improvements in computation time using
existing hardware and software tools. Migrating solid modeling applications
toward a distributed computing framework enables interconnection of many of
the autonomous and geographically diverse software tools used in the
modern manufacturing enterprise.
(Also cross-referenced as UMIACS-TR-94-126.1
An investigation of a pattern recognition system to analyse and classify dried fruit
Includes bibliographical references.Both the declining cost and increasing capabilities of specialised computer hardware for image processing have enabled computer vision systems to become a viable alternative to human visual inspection in industrial applications. In this thesis a vision system that will analyse and classify dried fruit is investigated. In human visual inspection of dried fruit, the colour of the fruit is often the main determinant of its grade; in specific cases the presence of blemishes and geometrical fault are also incorporated in order to determine the fruit grade. A colour model that would successfully represent the colour variations within dried fruit grades, was investigated. The selected colour feature space formed the basis of a classification system which automatically allocated a sample unit of dried fruit to one specific grade. Various classification methods were investigated, and that which suited the system data and parameters was selected and evaluated using test sets of three types of dried fruit. In order to successfully grade dried fruit, a number of additional problems had to be catered for: the red/brown coloured central core area of dried peaches had to be removed from the colour analysis, and Black blemishes upon dried pears had to be isolated and sized in order to supplement the colour classifier in the final classification of the pear. The core area of a dried peach was isolated using the Morphological Top-Hat transform, and Black blemishes upon pears were isolated using colour histogram thresholding techniques. The test results indicated that although colour classification was the major determinant in the grading of dried fruit, other characteristics of the fruit had to be incorporated to achieve successful final classification results; these characteristics may be different for different types of dried fruit, but in the case of dried apricots, dried peaches and dried pears, they include the: peach core area removal, fruit geometry validation, and dried pear blemish isolation and sizing
Manufacturing Feature Recognition With 2D Convolutional Neural Networks
Feature recognition is a critical sub-discipline of CAD/CAM that focuses on the design and implementation of algorithms for automated identification of manufacturing features. The development of feature recognition methods has been active for more than two decades for academic research. However, in this domain, there are still many drawbacks that hinder its practical applications, such as lack of robustness, inability to learn, limited domain of features, and computational complexity. The most critical one is the difficulty of recognizing interacting features, which arises from the fact that feature interactions change the boundaries that are indispensable for characterizing a feature. This research presents a feature recognition method based on 2D convolutional neural networks (CNNs). First, a novel feature representation scheme based on heat kernel signature is developed. Heat Kernel Signature (HKS) is a concise and efficient pointwise shape descriptor. It can present both the topology and geometry characteristics of a 3D model. Besides informative and unambiguity, it also has advantages like robustness of topology and geometry variations, translation, rotation and scale invariance. To be inputted into CNNs, CAD models are discretized by tessellation. Then, its heat persistence map is transformed into 2D histograms by the percentage similarity clustering and node embedding techniques. A large dataset of CAD models is built by randomly sampling for training the CNN models and validating the idea. The dataset includes ten different types of isolated v features and fifteen pairs of interacting features. The results of recognizing isolated features have shown that our method has better performance than any existing ANN based approaches. Our feature recognition framework offers the advantages of learning and generalization. It is independent of feature selection and could be extended to various features without any need to redesign the algorithm. The results of recognizing interacting features indicate that the HKS feature representation scheme is effective in handling the boundary loss caused by feature interactions. The state-of-the-art performance of interacting features recognition has been improved