Journal: Volume 21, No. 3, 2016
Pages: 5 – 10
559 Views

Optimization of the procedure for selecting sensors in laser technological equipment

Valentyna Lukashenko, Tetyana Utkina , Andrii Germanovich Lukashenko, Serhii Mitsenko, K. Dubitskiy

Abstract

Introduction. Automated and automatic control systems are an integral part of modern hightech production. Physical quantity sensors are an essential element for every control loop, which allows providing the feedback signal of electronics by control on actuator device. Accelerometers are responsive to acceleration or force acting on the sensor element. The purpose of scientific work. The objective of this research is to optimize the procedure for determining the most suitable accelerometer model from a variety of existing ones through the design and development of multiparameter quality criteria. Formulation of the problem. Most of integrated accelerometers, manufactured by MEMS technology, combine the functions of forward and reverse electromechanical conversion with built-in intelligence. The area of integrated accelerometers application is very wide: from aerospace equipment to automotive one and robotics, which makes a wide range of requirements put forward to them. Today MEMS technologies have reached a level that allows not only the production of inexpensive components in mass quantities, but also the massive expansion of their applications, including in laser technological equipment. The investigation of current accelerometers models on many parameters without mathematical description of relationships between them doesn’t allow to optimize the procedure using a PC and takes time. Therefore, the task of rapid determination of accelerometer model from a variety of existing ones on many parameters is important. The main material. The authors have conducted the analysis of modern accelerometers models of different MEMS manufacturing companies. As a result, the main technical parameters that affect the performance properties of accelerometers are determined. In this paper the quality criteria for key indicators are calculated. A generalized mathematical model is created. Based on the properties of dimensions theory, dimensionless power complexes and heuristic method, multiparameter quality criteria are developed and their physical interpretation is determined. Conclusions. The procedure for optimization of selecting sensors in laser technological equipment from a variety of existing ones due to the development of quality criteria and the visualization of the proposed sign model of the dependencies of multiparameter quality criteria of many modern accelerometers is offered. As the result of functional analysis of sign model the most suitable model from many modern accelerometers by many parameters at the same time is determined

Keywords

accelerometer, sign model, quality criteria, dimensions theory

References

  1. 2015: A year of contrasts for MEMS companies. (n.d.). i‑Micronews. Retrieved from http://www.imicronews.com/mems-sensors/7174-2015-ayear-of-contrasts-for-mems-companies.html
  2. Lukashenko,V.M., Chichuzhko,M.V., Lukashenko,D.A., & Lukashenko,V.A. (2013). Determination method of efficiency units for conditional similarity criterion. Bulletin of Cherkasy State Technological University, (2), 44–47.
  3. The MEMS industry: desperately seeking a second wind. (n.d.). i‑Micronews. Retrieved from http://www.imicronews.com/mems-sensors/7185-themems-industry-desperately-seeking-a-secondwind.html
  4. Lebedev,A. (2008). Digital MEMS accelerometers in automotive applications. Modern Electronics, (5), 12–15.
  5. Lukashenko,V.M., Utkina,T.Y., Lukashenko,A.H., Lukashenko,D.A., & Dubitsky,K.O. (2016). Methodology of functional analysis of a set of accelerometer models. In Prospects of World Science: Materials of the XII International Scientific and Practical Conference (July 30 – August 7, 2016) (Vol. 17, pp. 103–108). Sheffield: Science and Education Ltd.
  6. Lukashenko,V.M., Chichuzhko,M.V., & Lukashenko,D.A. (2013). Method of extending the functionality of modern microcontrollers. Bulletin of Khmelnytskyi National University. Technical Sciences, (6), 186–189.
  7. Petropavlovsky,Y. (2015). Modern MEMS products of Analog Devices. Part 1. Elements and Components, (6), 40–45.
  8. Petropavlovsky,Y. (2015). Modern MEMS products of Analog Devices. Part 2. Elements and Components, (7), 24–29.
  9. Rudakov,K.S., Lukashenko,V.M., & Utkina,T.Y. (2015). Two-quadrant image-symbolic model for determining the effective router. Bulletin of Khmelnytskyi National University. Technical Sciences, (2), 150–156.
  10. Sysoyeva,S. (2010). Key segments of the MEMS components market. Accelerometers. Components and Technologies, (3), 20–26.
  11. Sysoyeva,S. (2010). Key segments of the MEMS components market. Inertial systems – from low-end to high-end segments. Components and Technologies, (5), 22–30.
  12. Yudin,A. (2009). New accelerometers from STMicroelectronics. Components and Technologies, (2), 28–31.

Suggested citation

Lukashenko, V., Utkina , T., Lukashenko, A.G., Mitsenko, S., & Dubitskiy, K. (2016). Optimization of the procedure for selecting sensors in laser technological equipment. Bulletin of Cherkasy State Technological University, 21(3), 5-10.