Integration of physics with data science models for predicting porosity and emission in additive manufacturing
Creators: name: Gawade, Vidita
Publisher: name: No Publisher Supplied
Year: 2023

description: Smart manufacturing in Industry 4.0 &amp; 5.0 creates the connection of systems, voluminous real-time data, machine learning, and automation, intending to allow humans and machines to make decisions for personalized autonomous manufacturing. In turn, there is an increase in demand to bring transparency in the models developed, which capture the physics while harnessing the diversity of sensor data. One pillar of smart manufacturing, different from traditional subtractive manufacturing, is additive manufacturing (AM). Though AM enables customization, it is not widely adopted for functional metal parts because challenges prevent a stable and repeatable process. One of the challenges is leveraging analytical models of process physics, including process-property-quality relationships, with state-of-the-art data science models which entail real-time online sensing insight. Another challenge in AM is understanding the convoluted links between the process parameters, online sensing data, and part quality that result in inconsistent and non-standard prints and how to obtain parts that are reliable and free from defects. A final challenge identified is facilitating decision-making for the user to improve future prints. This research overcomes these challenges by integrating physics with data science models with Explainable Artificial Intelligence (XAI) in metal AM examples. One example is addressing a significant knowledge gap when predicting porosity accurately for Directed Laser Deposition (DLD) with theoretical Finite Element Analysis (FEA) and empirical in situ evidence alone. The gap is bridged by combining the data sources, specifically by feature engineering using functional principal component analysis and by capturing the melt pool’s novel 3-D temperature shape profile via FEA. Moreover, comparing predicted and true porosity labels of a Ti–6Al–4V thin-wall structure from DLD verified the interpretable and robust model. A second example is using online time series emission data with process parameter data to model and track emission signals’ uniformity as a potential measure for overheating in Powder Bed Fusion (PBF). A novel DNN-LSTM (Deep Neural Network-Long Short-Term Memory) model is developed to incorporate online sensing data and process physics knowledge. The developed model is compared with traditional regression-based time series and statistical Auto-Regressive Integrated Moving Average (ARIMA). A case study on 16 boxes of 316L Stainless Steel in an AconityMINI printer demonstrates the framework’s effectiveness. The predictive hybrid DNN-LSTM model remains a Black-box until the employment of XAI models: Shapley Additive Explanations, Integrated Gradients, and Local Interpretable Model-Agnostic Explanations. The three XAI models increased trust and transparency of the predictive model for potential control of emission (thereby of defects such as overheating) by developing a human-centric approach to work hand in hand with the machine, models, and data. Subjective and quantitative evaluations are developed, including global and local force plots, heat maps, sensitivity, readability, and area under the cumulative feature importance, to review the extent to which the DL model is trustworthy and can be used for decision-making. This research aims to contribute to the foundation of smart manufacturing by understanding the convoluted relation between product quality and process physics using integrated physics with data science models. The research fills in gaps between traditional and emerging machine learning techniques with state-of-art AM technologies. The methodology can be generalized to applications in manufacturing and other fields outside of manufacturing needing the integration of physics with data science models for informed decision-making, productivity, and dependability.