AI-Based Industrial Management for Enhancing Operational Manufacturing Processes of Medical Bed Parts via AI-Driven Quality

Hadi Gholampoor

Abstract


In the realm of medical equipment manufacturing, ensuring the quality of each component is crucial due to the direct impact on patient safety and product reliability. This study introduces a novel application of machine learning within industrial management to enhance the operational manufacturing processes of medical bed parts. Utilizing a Random Forest classifier, we developed a predictive model based on five critical features collected during the manufacturing process: the physical dimensions of Length, Width, Height, Weight of the parts, and the operator involved in manual grinding. The classifier aimed to predict whether each part would be defective or accepted before assembly, potentially revolutionizing the traditional quality control approach by reducing dependency on post-manufacturing inspections and minimizing human error. The model was trained on a dataset of 500 parts, with a class distribution reflecting a significant imbalance between defected and accepted pieces. Despite this, the classifier achieved a high accuracy of 97.0% on the test set, demonstrating robustness and reliability in predicting part quality. Feature importance analysis revealed that while physical attributes like Weight and Height significantly influenced predictions, operator variability also played a crucial role, indicating areas for operational improvement through training and standardization. This research highlights how integrating AI into industrial manufacturing processes can significantly enhance efficiency, reduce waste, and ensure higher standards of quality control, setting a precedent for future applications in similar high-stakes manufacturing environments

Keywords


AI-based Industrial Management, Machine Learning, Quality Prediction, Medical Equipment Manufacturing, Operational Efficiency

Full Text:

PDF

References


T. Saßmannshausen, P. Burggräf, J. Wagner, M. Hassenzahl, T. Heupel, and F. Steinberg, "Trust in artificial intelligence within production management–an exploration of antecedents," Ergonomics, vol. 64, no. 10, pp. 1333-1350, 2021.

L. Espinosa-Leal, A. Chapman, and M. Westerlund, "Autonomous industrial management via reinforcement learning," Journal of intelligent & Fuzzy systems, vol. 39, no. 6, pp. 8427-8439, 2020.

S. Ramazanov, V. Babenko, and O. Honcharenko, "Information technologies for the industrial management of objects in an innovative economy under conditions of instability and development of Industry 4.0," in Advanced Trends in ICT for Innovative Business Management: CRC Press, 2021, pp. 147-170.

P. M. Mah, I. Skalna, and J. Muzam, "Natural language processing and artificial intelligence for enterprise management in the era of industry 4.0," Applied Sciences, vol. 12, no. 18, p. 9207, 2022.

K. Kumar, D. Zindani, and J. P. Davim, Artificial intelligence in mechanical and industrial engineering. CRC Press, 2021.

S. Gholampour, "Computerized biomechanical simulation of cerebrospinal fluid hydrodynamics: Challenges and opportunities," Computer Methods and Programs in Biomedicine, vol. 200, pp. 105938-105938, 2021.

S. Gholampour, "Can magnetic resonance elastography serve as a diagnostic tool for gradual-onset brain disorders?," Neurosurgical Review, vol. 47, no. 1, p. 3, 2023.

S. Gholampour and H. H. H. Deh, "The effect of spatial distances between holes and time delays between bone drillings based on examination of heat accumulation and risk of bone thermal necrosis," Biomedical engineering online, vol. 18, pp. 1-14, 2019.

S. Gholampour, J. Droessler, and D. Frim, "The role of operating variables in improving the performance of skull base grinding," Neurosurgical Review, vol. 45, no. 3, pp. 2431-2440, 2022.

S. Gholampour and K. Hajirayat, "Minimizing thermal damage to vascular nerves while drilling of calcified plaque," BMC research notes, vol. 12, pp. 1-7, 2019.

S. Gholampour, H. H. Hassanalideh, M. Gholampour, and D. Frim, "Thermal and physical damage in skull base drilling using gas cooling modes: FEM simulation and experimental evaluation," Computer Methods and Programs in Biomedicine, vol. 212, p. 106463, 2021.

S. Gholampour, N. Soleimani, F. Zare Karizi, A. Reza Zalii, N. Masoudian, and A. Saeed Seddighi, "Biomechanical assessment of cervical spine with artificial disc during axial rotation, flexion and extension," International Clinical Neuroscience Journal, vol. 3, no. 2, pp. 113-119, 2016.

S. Gholampour, "FSI simulation of CSF hydrodynamic changes in a large population of non-communicating hydrocephalus patients during treatment process with regard to their clinical symptoms," PLoS One, vol. 13, no. 4, p. e0196216, 2018.

S. Gholampour, "Modeling and simulation of cerebrospinal fluid disorders," vol. 11, ed: Frontiers Media SA, 2023, p. 1331170.

S. Gholampour, "Feasibility of assessing non-invasive intracranial compliance using FSI simulation-based and MR elastography-based brain stiffness," Scientific Reports, vol. 14, no. 1, p. 6493, 2024.

S. Gholampour and M. Bahmani, "Hydrodynamic comparison of shunt and endoscopic third ventriculostomy in adult hydrocephalus using in vitro models and fluid-structure interaction simulation," (in eng), Comput Methods Programs Biomed, vol. 204, p. 106049, Jun 2021, doi: 10.1016/j.cmpb.2021.106049.

S. Gholampour, H. Balasundaram, P. Thiyagarajan, and J. Droessler, "A mathematical framework for the dynamic interaction of pulsatile blood, brain, and cerebrospinal fluid," Computer Methods and Programs in Biomedicine, vol. 231, p. 107209, 2023.

S. Gholampour and N. Fatouraee, "Boundary conditions investigation to improve computer simulation of cerebrospinal fluid dynamics in hydrocephalus patients," Communications biology, vol. 4, no. 1, pp. 1-15, 2021.

S. Gholampour, N. Fatouraee, A. Seddighi, and A. Seddighi, "Numerical simulation of cerebrospinal fluid hydrodynamics in the healing process of hydrocephalus patients," Journal of Applied Mechanics and Technical Physics, vol. 58, no. 3, pp. 386-391, 2017.

S. Gholampour, D. Frim, and B. Yamini, "Long-term recovery behavior of brain tissue in hydrocephalus patients after shunting," Communications Biology, vol. 5, no. 1, pp. 1-13, 2022.

S. Gholampour and H. Gholampour, "Correlation of a new hydrodynamic index with other effective indexes in Chiari I malformation patients with different associations," Scientific Reports, vol. 10, no. 1, pp. 1-13, 2020.

S. Gholampour and S. Mehrjoo, "Effect of bifurcation in the hemodynamic changes and rupture risk of small intracranial aneurysm," Neurosurgical Review, vol. 44, no. 3, pp. 1703-1712, 2021.

S. Gholampour and M. Taher, "Relationship of morphologic changes in the brain and spinal cord and disease symptoms with cerebrospinal fluid hydrodynamic changes in patients with Chiari malformation type I," World neurosurgery, vol. 116, pp. e830-e839, 2018.

S. Gholampour, B. Yamini, J. Droessler, and D. Frim, "A New Definition for Intracranial Compliance to Evaluate Adult Hydrocephalus After Shunting," Front. Bioeng. Biotechnol. 10: 900644. doi: 10.3389/fbioe, 2022.

S. Bahoo, M. Cucculelli, and D. Qamar, "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, vol. 188, p. 122264, 2023.

Y. Wang and S. H. Chung, "Artificial intelligence in safety-critical systems: a systematic review," Industrial Management & Data Systems, vol. 122, no. 2, pp. 442-470, 2022.

P. Dhamija and S. Bag, "Role of artificial intelligence in operations environment: a review and bibliometric analysis," The TQM Journal, vol. 32, no. 4, pp. 869-896, 2020.

S. Gholampour, "Impact of Nature of Medical Data on Machine and Deep Learning for Imbalanced Datasets: Clinical Validity of SMOTE Is Questionable," Machine Learning and Knowledge Extraction, vol. 6, no. 2, pp. 827-841, 2024.

M. G. Waterstraat, A. Dehghan, and S. Gholampour, "Optimization of number and range of shunt valve performance levels in infant hydrocephalus: a machine learning analysis," Frontiers in Bioengineering and Biotechnology, vol. 12, p. 1352490, 2024.

T. M. Khoshgoftaar, C. Seiffert, J. Van Hulse, A. Napolitano, and A. Folleco, "Learning with limited minority class data," in Sixth International Conference on Machine Learning and Applications (ICMLA 2007), 2007: IEEE, pp. 348-353.




DOI: https://doi.org/10.52088/jaiem.v1i2.18

Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Journal of Industrial Engineering and Management (JAIEM) eISSN 2985-5683