Tatyana N. Baidyk

Development of micromachine tool prototypes for microfactories

At present, many areas of industry have strong tendencies towards miniaturization of products. Mechanical components of these products as a rule are manufactured using conventional large-scale equipment or micromechanical equipment based on microelectronic technology (MEMS). The first method has some drawbacks because conventional large-scale equipment consumes much energy, space and material. The second method seems to be more advanced but has some limitations, for example, two-dimensional (2D) or 2.5-dimensional shapes of components and materials compatible with silicon technology. In this paper, we consider an alternative technology of micromechanical device production. This technology is based on micromachine tools (MMT) and microassembly devices, which can be produced as sequential generations of microequipment. The first generation can be produced by conventional large-scale equipment. The machine tools of this generation can have overall sizes of 100–200 mm. Using microequipment of this generation, second generation microequipment having smaller overall sizes can be produced. This process can be repeated to produce generations of micromachine tools having overall sizes of some millimetres. In this paper we describe the efforts and some results of first generation microequipment prototyping. A micromachining centre having an overall size of 130 × 160 × 85 mm3 was produced and characterized. This centre has allowed us to manufacture micromechanical details having sizes from 50 µm to 5 mm. These details have complex three-dimensional shapes (for example, screw, gear, graduated shaft, conic details, etc), and are made from different materials, such as brass, steel, different plastics etc. We have started to investigate and to make prototypes of the assembly microdevices controlled by a computer vision system. In this paper we also describe an example of the applications (microfilters) for the proposed technology.

Improved method of handwritten digit recognition tested on MNIST database

Abstract We have developed a novel neural classifier LImited Receptive Area (LIRA) for the image recognition. The classifier LIRA contains three neuron layers: sensor, associative and output layers. The sensor layer is connected with the associative layer with no modifiable random connections and the associative layer is connected with the output layer with trainable connections. The training process converges sufficiently fast. This classifier does not use floating point and multiplication operations. The classifier was tested on two image databases. The first database is the MNIST database. It contains 60,000 handwritten digit images for the classifier training and 10,000 handwritten digit images for the classifier testing. The second database contains 441 images of the assembly microdevice. The problem under investigation is to recognize the position of the pin relatively to the hole. A random procedure was used for partition of the database to training and testing subsets. There are many results for the MNIST database in the literature. In the best cases, the error rates are 0.7, 0.63 and 0.42%. The classifier LIRA gives error rate of 0.61% as a mean value of three trials. In task of the pin–hole position estimation the classifier LIRA also shows sufficiently good results.

Micromechanical engineering: a basis for the low-cost manufacturing of mechanical microdevices using microequipment

Microelectronics-based micromechanics is rather limited for the construction of 3D micromechanisms with moving parts. We propose to use microequipment to transfer the technologies of mechanical engineering to the microdomain. We show that equipment precision increases linearly with decreasing size. To make microequipment, we suggest a series of equipment generations with gradually decreasing dimensions. Miniaturization of equipment will reduce power consumption and floor area occupied. Coupled with automation, it will drastically reduce the cost of microequipment. This in its turn will reduce the cost of micromechanical devices manufactured by microequipment. Microequipment-based manufacturing will also increase throughput by the concurrent operation of large numbers of low-cost microequipment pieces. The low cost and high productivity of microequipment-based manufacturing will widen the range of feasible micromechanical applications, both single-unit and mass. We propose designs for microvalve fluid filters, capillary heat exchangers, electromagnetic and hydraulic step motors that could be easily implemented by micromechanical engineering technologies. Hybrid technologies combining massively parallel microequipment based manufacturing and batch manufacturing may also be promising.

Flat image recognition in the process of microdevice assembly

An image recognition system for use in the assembly of microdevices is developed. The system gives an increase in the assembly process precision. A pin-to-hole insertion task was used to test developed system. The system will be used for assembly of microring-based filters.

Development of low-cost microequipment

In this article an alternative technology of micromechanical device production is considered. This technology is based on micromachine tools and microassembly devices. The micromachine tools and microassembly devices could be produced as sequential generations of microequipment. This paper describes the efforts and some results of first generation microequipment prototyping. A micromachining center having overall sizes 130 /spl times/ 160 /spl times/ 85 mm was produced and characterized. This center permits one to manufacture micromechanical details having the sizes from 50 /spl mu/m to 5 mm. These details have complex 3D-shape (for example, screw, gear, graduated shaft, conic details, etc.), and are made from different materials, for example, brass, steel, different plastics, etc. We started to investigate and make prototypes of the assembly microdevices controlled by a computer vision system. An example of the proposed technology applications (microfilters) is also described in this paper.

Sparse Binary Distributed Encoding of Numeric Vectors

we suggested a model of competitiveness of brand product, which takes into account 10 factors. The model is based on 52 fuzzy rules of type. Potential of application of the model for control of competitiveness of brand product is shown. We state a problem of learning of fuzzy model of competitiveness on the basis of experimental data.

Sparse Binary Distributed Encoding of Scalars

we suggested a model of competitiveness of brand product, which takes into account 10 factors. The model is based on 52 fuzzy rules of type. Potential of application of the model for control of competitiveness of brand product is shown. We state a problem of learning of fuzzy model of competitiveness on the basis of experimental data.

Genetic Algorithm for Neurocomputer Image Recognition

Genetic algorithms for optimization of feature set and internal structure of neural networks are considered. Results of experimental investigation of genetic algorithms are given. Experiments show that performance of neural networks after such optimization substantially increases.

A Process of Differentiation in the Assembly Neural Network

An assembly neural network model is described. The network is artificially partitioned into several sub-networks according to the number of classes that the network has to recognize. In the process of primary learning Hebb’s neural assemblies are formed in the sub-networks by means of modification of connections’ weights. Then, a differentiation process is executed which significantly improves the recognition accuracy of the network. A computer simulation of the assembly network is performed with the aid of which the differentiation process is studied in a set of experiments on a character recognition task using two types of separate handwritten characters: Ukrainian letters and Arabic numerals of MNIST database.

Application of neural network classifiers for the OCR of printed texts

A neural network classifier is developed for application to the optical character recognition of printed texts. The paper describes the structure of the classifier, the subtasks that are solved by the classifier, and the experimental results.