Ontology-guided Attribute Learning to Accelerate Certification for Developing New Printing Processes

AbstractIdentifying printing defects is vital for process certification, especially with evolving printing technologies. However, this task proves challenging, especially for micro-level defects necessitating microscopy, which presents a scalability barrier for manufacturing. To address this challenge, we propose an attribute learning methodology inspired by human learning, which identifies shared attributes among seen and unseen objects. First, it extracts defect class embeddings from an engineering-guided defect ontology. Then, attribute learning identifies the combination of attributes for defect estimation. This approach enables it to recognize previously unseen defects by identifying shared attributes, even those not included in the training dataset. The research formulates a joint optimization problem for learning and fine-tuning class embedding and ontology and solves it by integrating natural language processing, metaheuristics for exploration and exploitation, and stochastic gradient descent. In a case study involving a direct-ink-writing process for creating nanocomposites, this methodology was used to learn new defects not found in the training data using the optimized ontology. Compared to traditional zero-shot learning, this ontology-based approach significantly improves class embedding, outperforming transfer learning in one-shot and two-shot learning scenarios. This research represents an early effort to learn new defect concepts, potentially reducing the need for extensive measurements in defect identification.Keywords: Additive ManufacturingAttribute learningOntologyDefect identificationProcess certificationDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Additional informationNotes on contributorsTsegai O. YhdegoTsegai O. Yhdego is a researcher in Industrial Engineering pursuing a Ph.D. at Florida A&M University. His academic journey includes a BSc. in Electrical and Electronics Engineering (2015) from Eritrea Institute of Technology and an MSc. in Mechatronic Engineering (2019) from The Pan African University Institute for Basic Sciences, Technology and Innovation. His research focuses on developing small-sample machinelearning algorithms, specializing in ontology-based federated learning, emphasizing data security and collaborative machine learning. He has also contributed to the aviation industry, developing ML models to forecast flight delay and delay impact.Hui WangHui Wang is an associate professor of industrial engineering at the Florida A&M University-Florida State University College of Engineering and a member of the HighPerformance Materials Institute (HPMI). His research has been focused on (i) data modeling and analytics to support quality control for manufacturing processes, including small-sample learning under an interconnected environment, and (ii) optimization of manufacturing system design and supply chain. He received his PhD in industrial engineering from the University of South Florida and an MSE in mechanical engineering from the University of Michigan.Zhibin YuZhibin Yu is an associate professor of industrial engineering at the Florida A&M University-Florida State University College of Engineering and a member of the HighPerformance Materials Institute (HPMI). His research has been focused on nanomaterials synthesis and processing for printing electronics. He received his PhD in materials science and engineering from the University of California, Los Angeles.Hongmei ChiHongmei Chi is a professor of computer and information sciences at Florida A&M University. Her research focuses on areas of applied cybersecurity, mobile health privacy, Monte Carlo and quasi-Monte Carlo, and data science. She received her PhD in computer science at Florida State University.