Analisis Validitas CNC (Computer Numerical Control) Pick and Place pada Trainer Smart Manufacturing Industry 4.0 (SMI) T201 The Validity Analysis of CNC (Computer Numerical Control) Pick and Place on the Trainer Smart Manufacturing Industry 4.0 (SMI) T201
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Abstract
This study is motivated by the need to evaluate the validity and reliability of the automated control program in the CNC Pick and Place system on the Smart Manufacturing Industry 4.0 (SMI) T201 Trainer to ensure precise and efficient performance in the context of industrial automation. A quantitative experimental method was employed, involving stepwise testing of the system's main components, namely the suck pump, X- and Y-axis servo motors, and pneumatic cylinder. Data were collected from 15 trials, documented in video format, and analyzed quantitatively based on operational time and accuracy. The control logic was developed using flowcharts and state diagrams in the TIA Portal software, with performance standards referencing the ability to place 100 SMD components per hour. Instrument validity was assessed using the Pearson correlation coefficient at a 5% significance level, while criterion validity was determined by comparing machine outputs with manual standards. The results demonstrated that the system operates with stability, accuracy, and efficiency, as shown by an average arrangement time of 14.24 minutes for 25 bottles and a standard deviation of ±0.0065 minutes. All main components functioned optimally, the system’s validity was proven to be strong and significant, and operational reliability was confirmed through repeated testing. These findings affirm that the CNC Pick and Place system on the SMI T201 Trainer is a viable and reliable automation solution for intelligent technology-based industrial environments.

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References
Cano, J., Gonzalez, A., & Perez, L. (2020). Robotic Systems in Manufacturing: A Review. Robotics and Computer-Integrated Manufacturing, 61, 101844. https://doi.org/10.1016/j.rcim.2019.101844
Gonzalez, J., Smith, A., & Wang, Q. (2019). Object Recognition and Localization for Robotic Pick-and-Place. IEEE Transactions on Robotics, 35(4), 872–884. https://doi.org/10.1109/TRO.2019.2912345
Jiang, Y.-K. (2015). Application Research of PLC in the CNC System. Proceedings of the International Symposium on Research in Mechanical Engineering (ISRME-15), 231, 20.
Liu, H., Zhang, L., & Chen, X. (2019). Adaptation in Robotic Pick-and-Place Operations. International Journal of Advanced Robotics Systems, 16(5), 1–12. https://doi.org/10.1177/1729881419875123
Riaz, M., Khan, R., & Ahmed, Z. (2020). Automation in Food Processing: Current Trends. Food Control, 110, 107000. https://doi.org/10.1016/j.foodcont.2019.107000
Schilling, R. J. (2018). Fundamentals of Robotics: Analysis and Control. Prentice Hall.
Schwab, K. (2016). The Fourth Industrial Revolution (pp. 139–142). Crown Business.
Sutarman. (2018). Computer Numerical Control (CNC) Milling and Turning for Machining Process in Xintai Indonesia. Quest Journals: Journal of Research in Mechanical Engineering, 3(5), 1–7. http://www.questjournals.org/jrme/papers/vol3-issue5/A350107.pdf
Zhao, Y., Li, F., & Wang, M. (2019). Robotic Solutions for Warehouse Automation. Journal of Logistics Research, 1(2), 33–45.














