Modern Micro-Controllers to Improve the Quality of Production Process

• Autorzy: Dorota Klimecka-Tatar , Tomasz Chajduga
• Języki publikacji: EN

Abstrakty

EN

In general, the production processes are more and more complex. This is the result of more and more sophisticated materials used, the pressure to save them as well as other production resources (ecological perspective), also the tendency to buy mobile products lighter and less power consuming than before. In previous years using highly specialized technology was very costly for the company. Nowadays, this situation is possible to be changed because of emerging types of modern micro-controllers and variety of compatible sensors. Some of those micro-controllers are more power saving, some are more powerful in terms of computing power. The common denominator is that both purchasing them as well as programming is possible for ordinary person, a hobbyist building DIY projects. This sheds new light to the professional usage of modern micro-controllers-based solutions that can become possible to offer comparable level of precision at the fraction of cost. The authors recognized the strong potential in modern micro-controllers and made the research among professionals in the area of production companies. The outcome of research showed that the professionals share the opinion of the authors. Therefore, the outbreak of usage of such inexpensive solutions in professional applications is expected. However, the research showed, that there is possible to find single situations, where usage of modern micro-controllers may be limited, for example in the company targeted to produce hand-made products or handicraft (manufacture). (original abstract)

Słowa kluczowe

EN

Production process; Production enterprise

PL

Proces produkcji; Przedsiębiorstwo produkcyjne

• Czasopismo: Multidisciplinary Aspects of Production Engineering
• Rocznik: 2021
• Tom: 4
• Strony: 212--220

Twórcy

autor

Dorota Klimecka-Tatar

• Czestochowa University of Technology, Poland

autor

Tomasz Chajduga

• Czestochowa University of Technology, Poland

Bibliografia

• Alavi, M., & Leidner, D.,1999. Knowledge Management Systems: Issues, Challenges, and Benefits. Communications of the Association for Information Systems, 1., https://doi.org/10.17705/1CAIS.00107
• ARM Webpage, (accessed: 02.12.2020), https://www.arm.com/
• Bilan, Y., Hussain, H. I., Haseeb, M., & Kot, S., 2020. Sustainability and Economic Performance: Role of Organizational Learning and Innovation, Engineering Economics, 31(1), pp. 93-103. https://doi.org/10.5755/j01.ee.31.1.24045
• Borkowski, S., Ulewicz, R., 2009. Instruments of Production Processes Improvement, PTM, Warszawa.
• Chajduga, T., 2021. Embedded systems ensuring safety for people with disabilities, System Safety: Human - Technical Facility - Environment, 3(1), (in press).
• Chmielarz, G., 2019. Present state and future application of smart technologies in manufacturing processes, Production Engineering Archives, 24(24), pp. 14-19. https://doi.org/10.30657/pea.2019.24.04
• Edge computing with low power consumption, (accessed: 11.12.2020), https://developer.sony.com/develop/spresense/
• Embedded systems, Omni Sci, (accessed: 11.12.2020), https://www.omnisci.com/technical-glossary/embedded-systems
• Frăticiu, L., Mihăescu, D., & Andănuţ, M., 2015. Culture-Civilization-Organizational Culture and Managerial Performance, Procedia Economics and Finance, 27, pp. 69-72. https://doi.org/10.1016/S2212-5671(15)00973-9
• Frustaci, F., Perri, S., Cocorullo, G., & Corsonello, P., 2020. An embedded machine vision system for an in-line quality check of assembly processes, Procedia Manufacturing, 42(1-3), pp. 211-218. https://doi.org/10.1016/j.promfg.2020.02.072
• Gede Riana, I., Suparna, G., Gusti Made Suwandana, I., Kot, S., & Rajiani, I., 2020. Human resource management in promoting innovation and organizational performance, Problems and Perspectives in Management, 18(1), pp. 107-118. https://doi.org/10.21511/ppm.18(1).2020.10
• Grabara, J., Cehlar, M., & Dabylova, M., 2019. Human factor as an important element of success in the implementation of new management solutions, Polish Journal of Management Studies, 20(2), pp. 225-235. https://doi.org/10.17512/pjms.2019.20.2.19
• Ingaldi, M., 2018., Overview of the main methods of service quality analysis, Production Engineering Archives, 18(18), pp. 54-59. https://doi.org/10.30657/pea.2018.18.10
• Ingaldi, M., 2020., A new approach to quality management: Conceptual matrix of service attributes Polish Journal of Management Studies, 22(2), pp. 187-200. https://doi.org/10.17512/pjms.2020.22.2.13
• Kapler, M., 2021, Barriers to the implementation of innovations in information systems in SMEs. Production Engineering Archives, 27(20, pp. 156-162. https://doi.org/10.30657/pea.2021.27.20
• KAMAMI oficjalnym partnerem STMICROELECTRONICS, (accessed: 10.12.2020), https://stm32.eu/
• Kim, J., Lee, S., Chun, H., & Lee, C., 2021. Compact curved-edge displacement sensor-embedded spindle system for machining process monitoring. Journal of Manufacturing Processes, 64(12), pp. 1255-1260. https://doi.org/10.1016/j.jmapro.2021.02.056
• Klimecka-Tatar, D., & Ingaldi, M., 2020. How to indicate the areas for improvement in service process - the Knowledge Management and Value Stream Mapping as the crucial elements of the business approach, G&T, Revista Gestão & Tecnologia, 20(2), pp. 52-74. https://doi.org/10.20397/2177-6652/2020.v20i2.1878
• Klimecka-Tatar, D., & Niciejewska, M., 2021. Small-sized enterprises management in the aspect of organizational culture, G&T, Revista Gestão & Tecnologia, 21(1), pp. 4-24. https://doi.org/10.20397/2177-6652/2021.v21i1.2023
• Knijn, T., & van Wel, F., 2014. Better at work: Activation of partially disabled workers in the Netherlands, Alter, 8(4), 282-294. https://doi.org/10.1016/j.alter.2014.09.005
• Korpysa, J., 2021. Process Ambidexterity in Startups Innovation, Management Systems in Production Engineering, 29(1), pp. 27-32. https://doi.org/10.2478/mspe-2021-0004
• Lazar, S., Klimecka-Tatar, D., & Obrecht, M., 2021. Sustainability Orientation and Focus in Logistics and Supply Chains, Sustainability, 13(6), 3280. https://doi.org/10.3390/su13063280
• Liu, J., & Feng, J., 2021. Design of embedded digital image processing system based on ZYNQ, Microprocessors and Microsystems, 83(1), 104005. https://doi.org/10.1016/j.micpro.2021.104005
• Lozhkin, A., Maiorov, K., & Bozek, P., 2021. Convolutional Neural Networks Training for Autonomous Robotics, Management Systems in Production Engineering, 29(1), pp. 75-79. https://doi.org/10.2478/mspe-2021-0010
• Matuszny, M., 2020. Building decision trees based on production knowledge as support in decision-making process, Production Engineering Archives, 26(2), pp. 36-40. https://doi.org/10.30657/pea.2020.26.08
• Mittal, S., 2018, A Survey on Optimized Implementation of Deep Learning Models on the NVIDIA Jetson Platform, (accessed: 10.12.2020),
• https://www.researchgate.net/publication/329802520_A_Survey_on_Optimized_Implementaion_of_Deep_Learning_Models_on_the_NVIDIA_Jetson_Platform
• NVIDIA Jetson TX2 Tegra Developer Kit, (accessed: 11.12.2020), https://www.precisioncomputers.com.au/nvidia-jetson-tx2-tegra-developer-kit/
• Pietraszek, J., Radek, N., & Goroshko, A. V., 2020. Challenges for the DOE methodology related to the introduction of Industry 4.0, Production Engineering Archives, 26(4), pp. 190-194. https://doi.org/10.30657/pea.2020.26.33
• Raspberry Pi 400 - Your complete personal computer built into a compact keyboard, (accessed: 10.12.2020), https://www.raspberrypi.org/
• Raspberry Pi GPIO pins. (accessed: 11.12.2020) https://forum.arduino.cc/index.php?topic=539419.0
• Schwenk, C. H., 1986. Information, Cognitive Biases, and Commitment to a Course of Action, Academy of Management Review, 11(2), pp. 298-310. https://doi.org/10.5465/amr.1986.4283106
• Teo, T. S.H., & Too, B. L., 2000. Information Systems Orientation and Business Use of the Internet: An Empirical Study, International Journal of Electronic Commerce, 4(4), pp. 105-130. https://doi.org/10.1080/10864415.2000.11518381
• Tkachenko, V., Klymchuk, M., & Tkachenko, I., 2021. Recursive and Convergence Methodology of the Investment Management of the Enterprise Digitalization Processes, Management Systems in Production Engineering, 29(1), pp. 14-19. https://doi.org/10.2478/mspe-2021-0002
• Valvano, J.W. PhD, 2017. Embedded systems: introduction to arm cortex-m microcontrollers (accessed: 11.12.2020) http://users.ece.utexas.edu/~valvano/arm/outline1.htm
• Zhang, K., 2021, Animation virtual reality scene modeling based on complex embedded system and FPGA, Microprocessors and Microsystems, 80(1), 103632. https://doi.org/10.1016/j.micpro.2020.103632
• Zhou, J., 2020. Real-time task scheduling and network device security for complex embedded systems based on deep learning networks, Microprocessors and Microsystems, 79(1), 103282. https://doi.org/10.1016/j.micpro.2020.103282
• Żywiołek, J., Rosak-Szyrocka, J., Jereb, B. 2021, Barriers to Knowledge Sharing in the Field of Information Security, Management Systems in Production Engineering 29(2): pp. 114-119. https://doi.org/10.2478/mspe-2021-0015

Identyfikatory

DOI

10.2478/mape-2021-00019