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Significance of sensors for industry 4.0: Roles, capabilities, and applications

Open access
Javaid, Mohd, Haleem, Abid, Singh, Ravi Pratap, Rab, Shanay, Suman, Rajiv·2021·Sensors International

Abstract

Sensors play a crucial role in factory automation in making the system intellectual. Different types of sensors are available as per the suitability and applications; some of them are produced in mass and available in the market at affordable costs. The standard sensor types available are position sensors, pressure sensors, flow sensors, temperature sensors, and force sensors. They are used in many sectors, such as motorsport, medical, industry, aerospace, agriculture, and daily life. The objective of Industry 4.0 is to increase efficiency through automation. Sensors are vital components of Industry 4.0, allowing several transitions such as changes in positions, length, height, external and dislocations in industrial production facilities to be detected, measured, analysed, and processed. Smart factories will also enhance sustainability by tracking real-time output, and automated control systems will minimise potential factory maintenance costs. It can also be seen that digitalisation can improve production mobility, which gives advanced manufacturing firms a competitive advantage. This paper discusses sensors and their various types, along with significant capabilities for manufacturing. The step-by-step working Blocks and Quality Services of Sensors during implementation in Industry 4.0 are elaborated diagrammatically. Finally, we identified thirteen significant applications of sensors for Industry 4.0. Industry 4.0 provides an excellent opportunity for the development of the sensor market across the globe. In Industry 4.0, sensors will enjoy higher acceptance rates and benefit from a fully enabled connecting and data exchange and logistics integration. In the coming years, sensor installations may grow in process management, automated production lines, and digital supply chains.

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Research works printed but not linked: 37

  1. Schütze, 2018, Sensors 4.0–smart sensors and measurement technology enable Industry 4.0, Journal of Sensors and Sensor Systems, 7, 359, 10.5194/jsss-7-359-2018 · doi:10.5194/jsss-7-359-2018
  2. Yoon, 2021, Highly stretchable metal-polymer hybrid conductors for wearable and self-cleaning sensors, NPG Asia Mater., 13, 1, 10.1038/s41427-020-00277-6 · doi:10.1038/s41427-020-00277-6
  3. Wang, 2021, Application of three self-developed ECT sensors for monitoring the moisture content in sand and mortar, Construct. Build. Mater., 267, 121008, 10.1016/j.conbuildmat.2020.121008 · doi:10.1016/j.conbuildmat.2020.121008
  4. Farahani, 2019, Evaluation of in-mold sensors and machine data towards enhancing product quality and process monitoring via Industry 4.0, Int. J. Adv. Manuf. Technol., 105, 1371, 10.1007/s00170-019-04323-8 · doi:10.1007/s00170-019-04323-8
  5. Grantz, 2021, Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys, Sci. Rep., 11, 1, 10.1038/s41598-021-81673-y · doi:10.1038/s41598-021-81673-y
  6. Delipinar, 2021, Fabrication and materials integration of flexible humidity sensors for emerging applications, ACS Omega, 6, 8744, 10.1021/acsomega.0c06106 · doi:10.1021/acsomega.0c06106
  7. Lebosse, 2011, Modeling and evaluation of low-cost force sensors, IEEE Trans. Robot., 27, 815, 10.1109/TRO.2011.2119850 · doi:10.1109/tro.2011.2119850
  8. Vecchi, 2000, Experimental evaluation of two commercial force sensors for applications in biomechanics and motor control, 5th Ann. Conf. of Int. FES
  9. Ejeian, 2019, Design and applications of MEMS flow sensors: a review, Sens. Actuators, A, 295, 483, 10.1016/j.sna.2019.06.020 · doi:10.1016/j.sna.2019.06.020
  10. Repelianto A, 2019, The improvement of flaw detection by the configuration of uniform eddy current probes, Sensors, 19, 397, 10.3390/s19020397 · doi:10.3390/s19020397
  11. Sun, 2005, Enhanced flaw detection using an eddy current probe with a linear array of hall sensors, vol. 760, 516
  12. Gómez-Robledo, 2013, Using the mobile phone as Munsell soil-colour sensor: an experiment under controlled illumination conditions, Comput. Electron. Agric., 99, 200, 10.1016/j.compag.2013.10.002 · doi:10.1016/j.compag.2013.10.002
  13. Othman, 2014, Fuzzy ripening mango index using RGB colour sensor model, Researchers World, 5, 1
  14. Sakai, 2021, In-vacuum active colour sensor and wireless communication across a vacuum-air interface, Sci. Rep., 11, 1, 10.1038/s41598-020-80501-z · doi:10.1038/s41598-020-80501-z
  15. Shi, 2021, A simultaneous calibration technique of the extrinsic and turntable for structured-light-sensor-integrated CNC system, Opt Laser. Eng., 138, 106451, 10.1016/j.optlaseng.2020.106451 · doi:10.1016/j.optlaseng.2020.106451
  16. Minaee, 2021, Deep-emotion: facial expression recognition using attentional convolutional network, Sensors, 21, 3046, 10.3390/s21093046 · DOI
  17. Elashery, 2021, Novel and selective potentiometric sensors for Cinchocaine HCl determination in its pure and Co-formulated dosage form: a comparative study of in situ carbon sensors based on different ion pairing agents, Measurement, 173, 108549, 10.1016/j.measurement.2020.108549 · doi:10.1016/j.measurement.2020.108549
  18. Mueller, 2018, Wireless sensors for industry 4.0-wireless communication and wireless powering, Smart SysTech
  19. Sigg, 2019, Capturing human-machine interaction events from radio sensors in industry 4.0 environments, International Conference on Business Process Management, 430
  20. Mishra, 2020, Real-time monitoring and control of friction stir welding process using multiple sensors, CIRP Journal of Manufacturing Science and Technology, 30, 1, 10.1016/j.cirpj.2020.03.004 · doi:10.1016/j.cirpj.2020.03.004
  21. Braunmüller, 2020, Advanced sensors: industry 4.0 for the laser microjet technology, vol. 11268, 112681M
  22. Pinto, 2021, Antibiofouling strategy for optical sensors by chlorine generation using low-cost, transparent and highly efficient electrodes based on platinum nanoparticles coated oxide, Chem. Eng. J., 404, 126479, 10.1016/j.cej.2020.126479 · doi:10.1016/j.cej.2020.126479
  23. Benitez R, Ramirez C, Vazquez JA. Sensors Calibration for Metrology 4.0. 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4. 0&IoT) 2019 Jun 4 (pp. 296-299). IEEE. · doi:10.1109/metroi4.2019.8792886
  24. Abarca, 2009, Intelligent sensor for tracking and monitoring of blood temperature and hemoderivatives used for transfusions, Sens. Actuators, A, 152, 241, 10.1016/j.sna.2009.03.018 · doi:10.1016/j.sna.2009.03.018
  25. Simon, 2001, Micromachined metal oxide gas sensors: opportunities to improve sensor performance, Sensor. Actuator. B Chem., 73, 1, 10.1016/S0925-4005(00)00639-0 · doi:10.1016/s0925-4005(00)00639-0
  26. Quinn, 2010, Use of environmental sensors and sensor networks to develop water and salinity budgets for seasonal wetland real-time water quality management, Environ. Model. Software, 25, 1045, 10.1016/j.envsoft.2009.10.011 · doi:10.1016/j.envsoft.2009.10.011
  27. Gaynor, 2008, Implications of sensors and sensor networks for data quality management, Int. J. Inf. Qual., 2, 75
  28. Rada, 2016, Perspectives of low-cost sensors adoption for air quality monitoring, InAir Quality, 29
  29. Entler, 2021, Ceramic-chromium hall sensors for environments with high temperatures and neutron radiation, Sensors, 21, 721, 10.3390/s21030721 · doi:10.3390/s21030721
  30. Indri, 2019, Smart sensors applications for a new paradigm of a production line, Sensors, 19, 650, 10.3390/s19030650 · doi:10.3390/s19030650
  31. Dhinesh, 2020, A review on 3D printed sensors, vol. 764
  32. Qian, 2021, Facile preparation of self-assembled black phosphorus-based composite LB films as new chemical gas sensors, Colloid. Surface. Physicochem. Eng. Aspect., 608, 125616, 10.1016/j.colsurfa.2020.125616 · doi:10.1016/j.colsurfa.2020.125616
  33. Nica, 2020, Smart connected sensors, industrial big data, and real-time process monitoring in cyber-physical system-based manufacturing, J. Self Govern. Manag. Econ., 8, 29, 10.22381/JSME8420203 · doi:10.22381/jsme8420203
  34. Cui, 2021, Stretchable strain sensors with dentate groove structure for enhanced sensing recoverability, Compos. B Eng., 211, 108641, 10.1016/j.compositesb.2021.108641 · doi:10.1016/j.compositesb.2021.108641
  35. Janak, 2016, Quantities and sensors for machine tool spindle condition monitoring, MM science journal, 2016, 1648, 10.17973/MMSJ.2016_12_2016204 · doi:10.17973/mmsj.2016_12_2016204
  36. Mahato, 2020, Clinically comparable impedimetric immunosensor for serum alkaline phosphatase detection based on electrochemically engineered Au-nano-Dendroids and graphene oxide nanocomposite, Biosens. Bioelectron., 148, 111815, 10.1016/j.bios.2019.111815 · doi:10.1016/j.bios.2019.111815
  37. Dutta, 2020, Nanobiosensor-based diagnostic system: transducers and surface materials, Nano biomaterials Engineering, 1

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Metadata

Authors
Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R.
Venue
Sensors International
Publisher
Elsevier BV
Published
2021
Volume
2
Pages
100110
ISSN
2666-3511 (ISSN-L)
OpenAlex
W3173907290
Cited by
355
References
125
FWCI
29.17
Type
journal article
Language
en
Open access
gold
Licence

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Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2021). Significance of sensors for industry 4.0: Roles, capabilities, and applications. Sensors International. https://doi.org/10.1016/j.sintl.2021.100110

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