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Behdani AM, Zhao Y, Yao G, Wasalathanthri D, Hodgman E, Borys M, Li G, Khetan A, Wijesinghe D, Leone A. Rapid total sialic acid monitoring during cell culture process using a machine learning model based soft sensor. Biotechnol Prog 2024:e3493. [PMID: 38953182 DOI: 10.1002/btpr.3493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/07/2024] [Accepted: 06/24/2024] [Indexed: 07/03/2024]
Abstract
Total sialic acid content (TSA) in biotherapeutic proteins is often a critical quality attribute as it impacts the drug efficacy. Traditional wet chemical assays to quantify TSA in biotherapeutic proteins during cell culture typically takes several hours or longer due to the complexity of the assay which involves isolation of sialic acid from the protein of interest, followed by sample preparation and chromatographic based separation for analysis. Here, we developed a machine learning model-based technology to rapidly predict TSA during cell culture by using typically measured process parameters. The technology features a user interface, where the users only have to upload cell culture process parameters as input variables and TSA values are instantly displayed on a dashboard platform based on the model predictions. In this study, multiple machine learning algorithms were assessed on our dataset, with the Random Forest model being identified as the most promising model. Feature importance analysis from the Random Forest model revealed that attributes like viable cell density (VCD), glutamate, ammonium, phosphate, and basal medium type are critical for predictions. Notably, while the model demonstrated strong predictability by Day 14 of observation, challenges remain in forecasting TSA values at the edges of the calibration range. This research not only emphasizes the transformative power of machine learning and soft sensors in bioprocessing but also introduces a rapid and efficient tool for sialic acid prediction, signaling significant advancements in bioprocessing. Future endeavors may focus on data augmentation to further enhance model precision and exploration of process control capabilities.
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Affiliation(s)
- Amir M Behdani
- School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Yuxiang Zhao
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, Massachusetts, USA
| | - Grace Yao
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, Massachusetts, USA
| | - Dhanuka Wasalathanthri
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, Massachusetts, USA
| | - Eric Hodgman
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, Massachusetts, USA
| | - Michael Borys
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, Massachusetts, USA
| | - Gloria Li
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, Massachusetts, USA
| | - Anurag Khetan
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, Massachusetts, USA
| | - Dayanjan Wijesinghe
- School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Anthony Leone
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, Massachusetts, USA
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Khan IA, Birkhofer H, Kunz D, Lukas D, Ploshikhin V. A Random Forest Classifier for Anomaly Detection in Laser-Powder Bed Fusion Using Optical Monitoring. MATERIALS (BASEL, SWITZERLAND) 2023; 16:6470. [PMID: 37834607 PMCID: PMC10573617 DOI: 10.3390/ma16196470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/21/2023] [Accepted: 09/23/2023] [Indexed: 10/15/2023]
Abstract
Metal additive manufacturing (AM) is a disruptive production technology, widely adopted in innovative industries that revolutionizes design and manufacturing. The interest in quality control of AM systems has grown substantially over the last decade, driven by AM's appeal for intricate, high-value, and low-volume production components. Geometry-dependent process conditions in AM yield unique challenges, especially regarding quality assurance. This study contributes to the development of machine learning models to enhance in-process monitoring and control technology, which is a critical step in cost reduction in metal AM. As the part is built layer upon layer, the features of each layer have an influence on the quality of the final part. Layer-wise in-process sensing can be used to retrieve condition-related features and help detect defects caused by improper process conditions. In this work, layer-wise monitoring using optical tomography (OT) imaging was employed as a data source, and a machine-learning (ML) technique was utilized to detect anomalies that can lead to defects. The major defects analyzed in this experiment were gas pores and lack of fusion defects. The Random Forest Classifier ML algorithm is employed to segment anomalies from optical images, which are then validated by correlating them with defects from computerized tomography (CT) data. Further, 3D mapping of defects from CT data onto the OT dataset is carried out using the affine transformation technique. The developed anomaly detection model's performance is evaluated using several metrics such as confusion matrix, dice coefficient, accuracy, precision, recall, and intersection-over-union (IOU). The k-fold cross-validation technique was utilized to ensure robustness and generalization of the model's performance. The best detection accuracy of the developed anomaly detection model is 99.98%. Around 79.40% of defects from CT data correlated with the anomalies detected from the OT data.
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Affiliation(s)
- Imran Ali Khan
- Airbus Endowed Chair for Integrative Simulation and Engineering of Materials and Processes (ISEMP), University of Bremen, Am Fallturm 1, 28359 Bremen, Germany; (H.B.); (V.P.)
| | - Hannes Birkhofer
- Airbus Endowed Chair for Integrative Simulation and Engineering of Materials and Processes (ISEMP), University of Bremen, Am Fallturm 1, 28359 Bremen, Germany; (H.B.); (V.P.)
| | - Dominik Kunz
- Electro Optical Systems GmbH, Robert-Stirling Ring 1, 82152 Krailling, Germany;
| | - Drzewietzki Lukas
- Leibherr-Aerospace Lindenberg GmbH, Pfänderstraße 50-52, 881161 Lindenberg, Germany;
| | - Vasily Ploshikhin
- Airbus Endowed Chair for Integrative Simulation and Engineering of Materials and Processes (ISEMP), University of Bremen, Am Fallturm 1, 28359 Bremen, Germany; (H.B.); (V.P.)
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A Review of Non-Destructive Testing (NDT) Techniques for Defect Detection: Application to Fusion Welding and Future Wire Arc Additive Manufacturing Processes. MATERIALS 2022; 15:ma15103697. [PMID: 35629723 PMCID: PMC9147555 DOI: 10.3390/ma15103697] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/10/2022] [Accepted: 05/18/2022] [Indexed: 12/04/2022]
Abstract
In Wire and Arc Additive Manufacturing (WAAM) and fusion welding, various defects such as porosity, cracks, deformation and lack of fusion can occur during the fabrication process. These have a strong impact on the mechanical properties and can also lead to failure of the manufactured parts during service. These defects can be recognized using non-destructive testing (NDT) methods so that the examined workpiece is not harmed. This paper provides a comprehensive overview of various NDT techniques for WAAM and fusion welding, including laser-ultrasonic, acoustic emission with an airborne optical microphone, optical emission spectroscopy, laser-induced breakdown spectroscopy, laser opto-ultrasonic dual detection, thermography and also in-process defect detection via weld current monitoring with an oscilloscope. In addition, the novel research conducted, its operating principle and the equipment required to perform these techniques are presented. The minimum defect size that can be identified via NDT methods has been obtained from previous academic research or from tests carried out by companies. The use of these techniques in WAAM and fusion welding applications makes it possible to detect defects and to take a step towards the production of high-quality final components.
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