Abstract
Micron measurement of the manufactured part is an integral part of production process. This decides qualification of the manufactured part’s acceptance or rejection. First principle methods are well established to measure the manufactured parts with utmost certainty. In the advent of Industry 4.0 in the digital revolution era of manufacturing, an electronic measurement of various dimensional parameters is gaining prominence. The shortcomings of measurement by first principle methods such as written documents, inability to automate the analysis and hence cloud connectivity, etc., can be achieved with electronic gauging. In the proposed work, an electronic gauge with micron resolution and cloud connectivity is devised for measurement of outer diameter of a mass production component. The measured readings are validated using statistical methods for the Gauge Repeatability and Reproducibility (GRR). The electronic gauge registered greater stability in the key parameters of gauge capability such as Equipment Variation (EV), Appraiser Variation (AV), Part Variation (PV) and %GRR over its conventional measurement counterpart. The electronic gauge recorded %GRR of 7.81% against conventional gauge’s %GRR of 14.47%. This made the electronic gauge acceptable without any conditions for the measurement of a critical parameter in mass production environment. The paper extended the scope to record the measurement readings in cloud-enabled platform to make the measurement system ready in the context of Industry 4.0. The proposed model has been implemented and validated in a mass production set-up, engaged in manufacturing of precision auto components.
Similar content being viewed by others
References
Chrysler Group LLC, Ford Motor Company, General Motors Corporation (AIAG). Measurement System Analysis. Reference manual, 4th Edition. ISBN#:978-1-60-534211-5. (2010).
Hannola L, Richter A, Richter S, Stocker A. Empowering production workers with digitally facilitated knowledge processes—a conceptual framework. Int J Prod Res. 2018;56(14):4729–43. https://doi.org/10.1080/00207543.2018.1445877.
Mishra D, Gunasekaran A, Childe SJ, Papadopoulos T, Dubey R, Wamba S. Vision, applications and future challenges of internet of things. Indust Manage Data Syst. 2016;116(7):1331–55. https://doi.org/10.1108/IMDS-11-2015-0478.
Moeuf A, Pellerin R, Lamouri S, Tamayo-Giraldo S, Barbaray R. The industrial management of SMEs in the era of Industry 4.0. Int J Prod Res. 2018;56(3):1118–36. https://doi.org/10.1080/00207543.2017.1372647.
Monostori L. Cyber-physical production systems: roots, expectations and R&D challenges. Procedia Cirp. 2014;17:9–13. https://doi.org/10.1016/j.procir.2014.03.115.
Machado CG, Winroth MP, Ribeiro da Silva EHD. Sustainable manufacturing in Industry 4.0: an emerging research agenda. Int J Prod Res. 2020;58(5):1462–84. https://doi.org/10.1080/00207543.2019.1652777.
Qin J, Liu Y, Grosvenor R. A categorical framework of manufacturing for Industry 4.0 and beyond. Procedia Cirp. 2016;52:173–8.
Gunasekaran A, Subramanian N, Ngai WTE. Quality management in the 21st century enterprises: research pathway towards Industry 4.0. Int J Prod Econ. 2019;207:125–9. https://doi.org/10.1016/j.ijpe.2018.09.005.
Moona G, Jewariya M, Sharma R. Relevance of dimensional metrology in manufacturing industries. MAPAN. 2019;34:97–104. https://doi.org/10.1007/s12647-018-0291-3.
Liu Y, Kuo Y, Yan D. System integration for on-machine measurement using a capacitive LVDT-like contact sensor. Adv Manuf. 2017;5:50–8. https://doi.org/10.1007/s40436-016-0169-y.
Li D, Wang B, Tong Z, Blunt L, Jiang X. On-machine surface measurement and applications for ultra-precision machining: a state-of-the-art review. Int J Adv Manuf Technol. 2019;104(1–4):831–47. https://doi.org/10.1007/s00170-019-03977-8.
Gadelmawla ES. Computer vision algorithms for measurement and inspection of external screw threads. Measurement. 2017;100:36–49. https://doi.org/10.1016/j.measurement.2016.12.034.
Xiang J, Zhen T, Duo L. On-machine measurement system and its application in ultra-precision manufacturing; 2019. https://doi.org/10.1007/978-981-10-5192-0_16-1
Gu J, Agapiou JS, Kurgin S. CNC machine tool work offset error compensation method. J Manuf Syst. 2015;37:576–85. https://doi.org/10.1016/j.jmsy.2015.04.001.
Gu J, Agapiou SJ, Kurgin S. Global offset compensation for CNC machine tools based on workpiece errors. Procedia Manuf. 2016;5:442–54. https://doi.org/10.1016/j.measurement.2016.12.034.
Prathima BA, Sudha PN, Suresh PM. Electronic sensors for micron resolution dimension measurement. In: International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT) IEEE; 2017. pp. 359–367. https://doi.org/10.1109/ICEECCOT.2017.8284528
Cepova L, Kovacikova A, Cep R, Klaput P, Mizera O. Measurement system analyses–Gauge repeatability and reproducibility methods. Measur Sci Rev. 2018;18(1):20–7. https://doi.org/10.1515/msr-2018-0004.
Srinivasan A, Kurey B. Creating a culture of quality. Harv Bus Rev. 2014;92(4):23–5.
Jain RK. Engineering metrology. 20th ed. Khanna Publishers; 2007.
ISO 5725. Accuracy (Trueness and Precision) of Measurement Methods and Results. International Organization for Standardization; 1994.
Beckwith TG, Marangoni RD, Lienhard JHV. Mechanical measurements. 6th ed. Pearson Education Inc; 2010.
Kuphaldt TR. Lessons in Industrial Instrumentation. Creative Commons Attribution/PAControl.com. Hong Kong: Samurai Media Limited; 2008. ISBN 9789888407088.
Lamb, F. Industrial Automation: Hands On. New York: McGraw Hill Education; 2013. ISBN 9780071816458.
Prathima BA, Sudha PN, Suresh PM. Shop floor to cloud connect for live monitoring the production data of CNC machines. Int J Comput Integr Manuf. 2020;33(2):142–58. https://doi.org/10.1080/0951192X.2020.1718762.
Prathima BA, Sudha PN, Suresh PM. Proposed methodology for auto error correction and detection for closed loop manufacturing using embedded system. Int J Curr Res. 2015;7(09):20519–23.
Acknowledgements
The authors would like to acknowledge the support and contributions of Distinct Productivity Solutions, Bangalore, India, to carry out the research and validate the same. The authors would like to acknowledge the support and contributions of Management and Principal of K S Institute of Technology, Bangalore, India.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Data Science and Communication” guest edited by Kamesh Namudri, Naveen Chilamkurti, Sushma S. J. and S. Padmashree.
Rights and permissions
About this article
Cite this article
Prathima, B.A., Sudha, P.N., Suresh, P.M. et al. Electronic Gauge for Micron Measurement and its Relevance to Industry 4.0. SN COMPUT. SCI. 2, 195 (2021). https://doi.org/10.1007/s42979-021-00570-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-021-00570-3