Comparative evaluation in quality control environment between a robotic arm used for measurement versus a manual instrument
Evaluación comparativa en ambiente de control de calidad entre un brazo robótico utilizado para medición frente a un instrumento manual
Article Sidebar
Main Article Content
Over time, measuring instruments have had an important role in quality control processes in the manufacturing industry, for this reason; this article portrays the research work carried out where they are determined through the application of Statistical Quality Control and Time Study; the advantages and disadvantages of a robotic arm used to make measurements versus a portable instrument designed for the same purpose, such as the Vernier Caliper; it is also determined whether the processes are under control and in turn, the possible assignable variables that positively and negatively affect the two operations; for this, an experimental environment is initially established where the additive manufacturing process of a three-dimensional geometric piece is taken into account, which once made is subjected to quality control where the two tools are used to measure the different selected sides; following this and having collected the different data from each of the instruments, reliability parameters are established and the respective analyzes and statistical studies are carried out to determine the advantages and disadvantages that one process has over the other, considerations to take into account and suggestions.
Downloads
Article Details
M. Piron, J. Wu, A. Fedele, y A. Manzardo, “Industry 4.0 and life cycle assessment: Evaluation of the technology applications as an asset for the life cycle inventory”, Sci Total Environ, vol. 916, p. 170263, mar. 2024, doi: 10.1016/j.scitotenv.2024.170263.
M. Malik, V. K. Gahlawat, R. Mor, K. Rahul, B. P. Singh, y S. Agnihotri, “Chapter 14 - Industry 4.0 technologies in postharvest operations: current trends and implications”, en Postharvest Management of Fresh Produce, B. P. Singh, S. Agnihotri, G. Singh, y V. K. Gupta, Eds., Academic Press, 2023, pp. 347-368. doi: 10.1016/B978-0-323-91132-0.00012-5.
D. A. Senna et al., “Industry 4.0 as a strategy to contribute to the water supply universalization in developing countries”, Journal of Environmental Chemical Engineering, vol. 11, n.o 6, p. 111198, dic. 2023, doi: 10.1016/j.jece.2023.111198.
A. Martini et al., “Robot-assisted Radical Cystectomy with Orthotopic Neobladder Reconstruction: Techniques and Functional Outcomes in Males”, European Urology, vol. 84, pp. 484-490, abr. 2023, doi: 10.1016/j.eururo.2023.04.009.
S. Demirtas, T. Cankurt, y E. Samur, “An adjustable robotic tool for nut running operations”, Procedia CIRP, vol. 107, pp. 191-195, ene. 2022, doi: 10.1016/j.procir.2022.04.032.
D. S. Paraforos, M. Reutemann, G. Sharipov, R. Werner, y H. W. Griepentrog, “Total station data assessment using an industrial robotic arm for dynamic 3D in-field positioning with sub-centimetre accuracy”, Computers and Electronics in Agriculture, vol. 136, pp. 166-175, abr. 2017, doi: 10.1016/j.compag.2017.03.009.
D. Hu, V. J. L. Gan, T. Wang, y L. Ma, “Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments”, Building and Environment, vol. 221, p. 109349, ago. 2022, doi: 10.1016/j.buildenv.2022.109349.
C. Bai, P. Dallasega, G. Orzes, y J. Sarkis, “Industry 4.0 technologies assessment: A sustainability perspective”, International Journal of Production Economics, vol. 229, p. 107776, nov. 2020, doi: 10.1016/j.ijpe.2020.107776.
R. Hamzeh, L. Thomas, J. Polzer, X. W. Xu, y H. Heinzel, “A Sensor Based Monitoring System for Real-Time Quality Control: Semi-Automatic Arc Welding Case Study”, Procedia Manufacturing, vol. 51, pp. 201-206, ene. 2020, doi: 10.1016/j.promfg.2020.10.029.
W. P. Syam, R. Leach, K. Rybalcenko, A. Gaio, y J. Crabtree, “In-process measurement of the surface quality for a novel finishing process for polymer additive manufacturing”, Procedia CIRP, vol. 75, pp. 108-113, ene. 2018, doi: 10.1016/j.procir.2018.04.088.
M. Grazia Guerra y F. Lavecchia, “Measurement of additively manufactured freeform artefacts: The influence of surface texture on measurements carried out with optical techniques”, Measurement, vol. 209, p. 112540, mar. 2023, doi: 10.1016/j.measurement.2023.112540.
C. Latsou, M. Farsi, y J. A. Erkoyuncu, “Digital twin-enabled automated anomaly detection and bottleneck identification in complex manufacturing systems using a multi-agent approach”, Journal of Manufacturing Systems, vol. 67, pp. 242-264, abr. 2023, doi: 10.1016/j.jmsy.2023.02.008.
Z. Ali et al., “Design and development of a low-cost 5-DOF robotic arm for lightweight material handling and sorting applications: A case study for small manufacturing industries of Pakistan”, Results in Engineering, vol. 19, p. 101315, sep. 2023, doi: 10.1016/j.rineng.2023.101315.
F. Yang y J. E. Hein, “Training a robotic arm to estimate the weight of a suspended object”, Device, vol. 1, n.o 1, p. 100011, jul. 2023, doi: 10.1016/j.device.2023.100011.
Y.-T. Tsai et al., “Utilization of a reinforcement learning algorithm for the accurate alignment of a robotic arm in a complete soft fabric shoe tongues automation process”, Journal of Manufacturing Systems, vol. 56, pp. 501-513, jul. 2020, doi: 10.1016/j.jmsy.2020.07.001.
J. Chamberlin, Y. Zhong, y Y. Wang, “Robots for Pharmaceutical Production: A Benchtop Robotic Automation Approach for Manufacturing Prefilled Syringes”, IFAC-PapersOnLine, vol. 55, n.o 37, pp. 469-474, ene. 2022, doi: 10.1016/j.ifacol.2022.11.227.
D. Li, R. Wei, Y. Du, X. Guan, y M. Zhou, “Measurement methods of geometrical parameters and amount of corrosion of steel bar”, Construction and Building Materials, vol. 154, pp. 921-927, nov. 2017, doi: 10.1016/j.conbuildmat.2017.08.018.
T. Saiboh et al., “Visual detection of formalin in food samples by using a microfluidic thread-based analytical device”, Microchemical Journal, vol. 190, p. 108685, jul. 2023, doi: 10.1016/j.microc.2023.108685.
G. J. da Silva, A. C. Borges, M. C. Moreira, y A. P. Rosa, “Statistical process control in assessing water quality in the Doce river basin after the collapse of the Fundão dam (Mariana, Brazil)”, Journal of Environmental Management, vol. 317, p. 115402, sep. 2022, doi: 10.1016/j.jenvman.2022.115402.
D. Sarkar, “Advanced materials management for Indian construction industry by application of statistical process control tools”, Materials Today: Proceedings, vol. 62, pp. 6934-6939, ene. 2022, doi: 10.1016/j.matpr.2021.12.082.
L.-T. Zhao, T. Yang, R. Yan, y H.-B. Zhao, “Anomaly detection of the blast furnace smelting process using an improved multivariate statistical process control model”, Process Safety and Environmental Protection, vol. 166, pp. 617-627, oct. 2022, doi: 10.1016/j.psep.2022.08.035.
S. Huang y W. Zhang, “A fast calculation method of rolling times in the GNSS real-time compaction quality supervisory system”, Advances in Engineering Software, vol. 128, pp. 20-33, feb. 2019, doi: 10.1016/j.advengsoft.2018.11.008.
Y. Liu, J. Cheng, C. Zou, L. Lu, y H. Jing, “Ignition delay times of ethane under O2/CO2 atmosphere at different pressures by shock tube and simulation methods”, Combustion and Flame, vol. 204, pp. 380-390, jun. 2019, doi: 10.1016/j.combustflame.2019.03.031.
