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Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
摘要： Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.
关键词： Bayesian network,GaAs solar cells,photovoltaics,neural network surrogate model,process optimization
更新于2025-09-23 15:19:57
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Prediction of Selective Laser Melting Part Quality Using Hybrid Bayesian Network
摘要： Additive manufacturing (AM) is gaining popularity because of its ability to manufacture complex parts in less time. Despite recent research involving designs of experiments (DOEs) to characterize the relationships between some AM process parameters and various part quality characteristics, to date, there seems to be no universally accepted comprehensive model that relates process parameters to part quality. In this paper, to support the goal of manufacturing parts right the first time, a Bayesian network in continuous domain is developed which relates four process parameters (laser power, scan speed, hatch spacing, and layer thickness) and five part quality characteristics (density, hardness, top layer surface roughness, ultimate tensile strength in the build direction and ultimate tensile strength perpendicular to the build direction). A machine learning algorithm is used to train the network on a database mined from a large number of publications with experimental data from parts built using 316L with selective laser melting. Using this Bayesian network, the user is able to enter a value for one or more known nodes or variables, and the network provides predictions on all the remaining nodes in the form of probability distributions. A method is developed whereby the user inputs are checked for reasonableness using an ??-dimensional convex hull, and if necessary a recommendation is returned based on user-defined weights. The network is validated by retaining a subset of the training data for testing and comparing the network’s predictions to the known values. Accuracy is optimized by continually re-training the network using parts built with a specific machine of interest. The industrial relevance of this research is outlined with respect to four current challenges in AM, including the length of time to determine optimal process parameters for a new machine, ability to organize relevant knowledge, quantification of machine variability, and transfer of knowledge to new operators.
关键词： Hybrid Bayesian Network,Selective Laser Melting Part Quality,Powder Bed Fusion,??-Dimensional Convex Hull,Predictive Model
更新于2025-09-16 10:30:52
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[ASME ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - Quebec City, Quebec, Canada (Sunday 26 August 2018)] Volume 2A: 44th Design Automation Conference - Design Exploration of Reliably Manufacturable Materials and Structures With Applications to a Microstereolithography System
摘要： One of the challenges in designing for additive manufacturing (AM) is accounting for the differences between as-designed and as-built geometries and material properties. From a designer’s perspective, these differences can lead to degradation of part performance, which is especially difficult to accommodate in small-lot or one-of-a-kind production. In this context, each part is unique, and therefore, extensive iteration is costly. Designers need a means of exploring the design space while simultaneously considering the reliability of additively manufacturing particular candidate designs. In this work, a design exploration approach, based on Bayesian network classifiers (BNC), is extended to incorporate manufacturability explicitly into the design exploration process. The example application is the design of negative stiffness (NS) metamaterials, in which small volume fractions of negative stiffness (NS) inclusions are embedded within a host material. The resulting metamaterial or composite exhibits macroscopic mechanical stiffness and loss properties that exceed those of the base matrix material. The inclusions are fabricated with microstereolithography with features on the scale of tens of microns, but variability is observed in material properties and dimensions from specimen to specimen. In this work, the manufacturing variability of critical features of a NS inclusion fabricated via microstereolithography are characterized experimentally and modelled mathematically. Specifically, the variation in the geometry of the NS inclusions and the Young’s modulus of the photopolymer are measured and modeled by both nonparametric and parametric joint probability distributions. Finally, the quantified manufacturing variability is incorporated into the BNC approach as a manufacturability classifier to identify candidate designs that achieve performance targets reliably, even when manufacturing variability is taken into account.
关键词： negative stiffness metamaterials,Bayesian network classifiers,additive manufacturing,microstereolithography,manufacturing variability
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Finding Community Structure of Bayesian Networks by Improved K-Means Algorithm
摘要： Many Bayesian networks display community structure; study community structure helps to reduce the complexity of learning Bayesian network in high-dimensional data. To find the community structure of Bayesian networks, this paper proposes an improved K-means algorithm by incorporating the mutual information into the K-means algorithm and modifying the iteration conditions. Experiments show that the proposed algorithm is suitable for block tasks of Bayesian network. Compared with FastNewman algorithm, the proposed algorithm can obtain accurate results in a shorter time.
关键词： Bayesian network,improved K-means algorithm,community structure
更新于2025-09-09 09:28:46