scholarly journals Machine-learning-assisted fabrication: Bayesian optimization of laser-induced graphene patterning using in-situ Raman analysis

2020 ◽  
Author(s):  
Hud Wahab ◽  
Vivek Jain ◽  
Alexander Scott Tyrrell ◽  
Michael Alan Seas ◽  
Lars Kotthoff ◽  
...  
Keyword(s):  
Machine Learning ◽  
Irradiation Time ◽  
Electronic Devices ◽  
Parameter Tuning ◽  
Search Space ◽  
Underlying Mechanism ◽  
Single Step ◽  
Bayesian Optimization ◽  
High Quality ◽  
Optimization Search

The control of the physical, chemical, and electronic properties of laser-induced graphene (LIG) is crucial in the fabrication of flexible electronic devices. However, the optimization of LIG production is time-consuming and costly. Here, we demonstrate state-of-the-art automated parameter tuning techniques using Bayesian optimization to advance rapid single-step laser patterning and structuring capabilities with a view to fabricate graphene-based electronic devices. In particular, a large search space of parameters for LIG explored efficiently. As a result, high-quality LIG patterns exhibiting high Raman G/D ratios at least a factor of four larger than those found in the literature were achieved within 50 optimization iterations in which the laser power, irradiation time, pressure and type of gas were optimized. Human-interpretable conclusions may be derived from our machine learning model to aid our understanding of the underlying mechanism for substrate-dependent LIG growth, e.g. high-quality graphene patterns are obtained at low and high gas pressures for quartz and polyimide, respectively. Our Bayesian optimization search method allows for an efficient experimental design that is independent of the experience and skills of individual researchers, while reducing experimental time and cost and accelerating materials research.

scholarly journals Hexacoordinated Gallium(III) Triazenide Precursor for Epitaxial Gallium Nitride by Atomic Layer Deposition

2020 ◽  
Author(s):  
Polla Rouf ◽  
Rouzbeh Samii ◽  
Karl Rönnby ◽  
Babak Bakhit ◽  
Sydney Buttera ◽  
...  
Keyword(s):  
Gallium Nitride ◽  
Atomic Layer Deposition ◽  
Electronic Devices ◽  
Atomic Layer ◽  
High Electron Mobility Transistor ◽  
Deposition Process ◽  
Single Step ◽  
High Electron ◽  
High Quality ◽  
Layer Deposition

Gallium nitride (GaN) is the main component of modern-day high electron mobility transistor electronic devices due to its favorable electronic properties. As electronic devices become smaller with more complex architecture, the ability to deposit high-quality GaN films at low temperature is required. Herein, we report a new highly volatile Ga(III) triazenide precursor and demonstrate its ability to deposit high-quality epitaxial GaN by atomic layer deposition (ALD). This new Ga(III) triazenide precursor, the first hexacoordinated M–N bonded Ga(III) precursor used in a vapor deposition process, was easily synthesized and purified by sublimation. Thermogravimetric analysis showed single step volatilization with an onset temperature of 150 °C and negligible residual mass. Three temperature intervals with self-limiting growth were observed when depositing GaN films. In the second growth interval, the films were found to be near stoichiometric with very low levels of impurities and epitaxial orientation on 4H-SiC without an AlN seed layer. The films grown at 350 °C were found to be smooth with a sharp interface between the substrate and film. The bandgap of these films was 3.41 eV with the Fermi level at 1.90 eV, showing that the GaN films were unintentionally <i>n</i>-type doped. This new triazenide precursor enables ALD of GaN for semiconductor applications and provides a new Ga(III) precursor for future deposition processes.

A Bayesian optimized discriminant analysis model for condition monitoring of face milling cutter using vibration datasets

2021 ◽  
pp. 1-12
Author(s):  
Naman S. Bajaj ◽  
Abhishek D. Patange ◽  
R. Jegadeeshwaran ◽  
Kaushal A. Kulkarni ◽  
Rohan S. Ghatpande ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Condition Monitoring ◽  
Machining Process ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Analysis Model ◽  
Tool Condition ◽  
Learning Techniques ◽  
Optimization Search

Abstract With the advent of Industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), data analytics, cloud computing, etc. The significant research area in predictive maintenance is Tool Condition Monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tool's condition in operation. These techniques are cost-saving and help industries with adopting future-proof solutions for their operations. One such technique called Discriminant analysis (DA) must be examined particularly for TCM. Owing to its less expensive computation and shorter run times, using them in TCM will ensure effective use of the cutting tool and reduce maintenance times. This paper presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data is collected using an in-house designed and developed Data Acquisition (DAQ) module set up on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter which gives the best model was found out to be ‘Linear’, achieving an accuracy of 93.3%. This work confirms the feasibility of machine learning techniques like DA in the field of TCM and using Bayesian optimization algorithms to fine-tune the model, making it industry-ready.

scholarly journals Automated Hyper-parameter Tuning for Machine Learning Models in Machine Health Prognostics

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Wang-Chi Cheung ◽  
Weiwen Zhang ◽  
Yong Liu ◽  
Feng Yang ◽  
Rick-Siow-Mong Goh
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Domain Knowledge ◽  
Parameter Tuning ◽  
Bayesian Optimization ◽  
Learning Models ◽  
Effective Choice ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Machine Learning Models

Recent studies have revealed the success of data-driven machine health monitoring, which motivates the use of machine learning models in machine health prognostic tasks. While the machine learning approach to health monitoring is gaining importance, the construction of machine learning models is often impeded by the difficulty in choosing the underlying hyper-parameter configuration (HP-config), which governs the construction of the machine learning model. While an effective choice of HP-config can be achieved with human effort, such an effort is often time consuming and requires domain knowledge. In this paper, we consider the use of Bayesian optimization algorithms, which automate an effective choice of HP-config by solving the associated hyperparameter optimization problem. Numerical experiments on the data from PHM 2016 Data Challenge demonstrate the salience of the proposed automatic framework, and exhibit improvement over default HP-configs in standard machine learning packages or chosen by a human agent.

scholarly journals Automated Machine Learning with Monte-Carlo Tree Search

2019 ◽  
Author(s):  
Herilalaina Rakotoarison ◽  
Marc Schoenauer ◽  
Michèle Sebag
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Search Algorithm ◽  
Search Space ◽  
Bayesian Optimization ◽  
Peak Performance ◽  
Tree Search ◽  
Algorithm Selection ◽  
Monte Carlo Tree Search ◽  
Warm Start

The AutoML approach aims to deliver peak performance from a machine learning  portfolio on the dataset at hand. A Monte-Carlo Tree Search Algorithm Selection and Configuration (Mosaic) approach is presented to tackle this mixed (combinatorial and continuous) expensive optimization problem on the structured search space of ML pipelines. Extensive lesion studies are conducted to independently assess and compare: i) the optimization processes based on Bayesian Optimization or Monte Carlo Tree Search (MCTS); ii) its warm-start initialization based on meta-features or random runs; iii) the ensembling of the solutions gathered along the search. Mosaic is assessed on the OpenML 100 benchmark and the Scikit-learn portfolio, with statistically significant gains over AutoSkLearn, winner of all former AutoML challenges.

scholarly journals Bayesian Optimization for Categorical and Category-Specific Continuous Inputs

2020 ◽  
Vol 34 (04) ◽  
pp. 5256-5263 ◽  
Author(s):  
Dang Nguyen ◽  
Sunil Gupta ◽  
Santu Rana ◽  
Alistair Shilton ◽  
Svetha Venkatesh
Keyword(s):  
Machine Learning ◽  
Parameter Tuning ◽  
Bayesian Optimization ◽  
Parallel Optimization ◽  
Continuous Variables ◽  
Unified Framework ◽  
Multiple Resources ◽  
Thompson Sampling ◽  
Machine Learning Model ◽  
Automated Machine Learning

Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) methods. To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables. Our goal is to identify the best arm and the maximizer of the corresponding continuous function simultaneously. Our algorithm uses a Thompson sampling scheme that helps connecting both multi-arm bandit and BO in a unified framework. We extend our method to batch BO to allow parallel optimization when multiple resources are available. We theoretically analyze our method for convergence and prove sub-linear regret bounds. We perform a variety of experiments: optimization of several benchmark functions, hyper-parameter tuning of a neural network, and automatic selection of the best machine learning model along with its optimal hyper-parameters (a.k.a automated machine learning). Comparisons with other methods demonstrate the effectiveness of our proposed method.

scholarly journals Hexacoordinated Gallium(III) Triazenide Precursor for Epitaxial Gallium Nitride by Atomic Layer Deposition

2020 ◽  
Author(s):  
Polla Rouf ◽  
Rouzbeh Samii ◽  
Karl Rönnby ◽  
Babak Bakhit ◽  
Sydney Buttera ◽  
...  
Keyword(s):  
Gallium Nitride ◽  
Atomic Layer Deposition ◽  
Electronic Devices ◽  
Atomic Layer ◽  
High Electron Mobility Transistor ◽  
Deposition Process ◽  
Single Step ◽  
High Electron ◽  
High Quality ◽  
Layer Deposition

Gallium nitride (GaN) is the main component of modern-day high electron mobility transistor electronic devices due to its favorable electronic properties. As electronic devices become smaller with more complex architecture, the ability to deposit high-quality GaN films at low temperature is required. Herein, we report a new highly volatile Ga(III) triazenide precursor and demonstrate its ability to deposit high-quality epitaxial GaN by atomic layer deposition (ALD). This new Ga(III) triazenide precursor, the first hexacoordinated M–N bonded Ga(III) precursor used in a vapor deposition process, was easily synthesized and purified by sublimation. Thermogravimetric analysis showed single step volatilization with an onset temperature of 150 °C and negligible residual mass. Three temperature intervals with self-limiting growth were observed when depositing GaN films. In the second growth interval, the films were found to be near stoichiometric with very low levels of impurities and epitaxial orientation on 4H-SiC without an AlN seed layer. The films grown at 350 °C were found to be smooth with a sharp interface between the substrate and film. The bandgap of these films was 3.41 eV with the Fermi level at 1.90 eV, showing that the GaN films were unintentionally <i>n</i>-type doped. This new triazenide precursor enables ALD of GaN for semiconductor applications and provides a new Ga(III) precursor for future deposition processes.

scholarly journals An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 763
Author(s):  
Ran Yang ◽  
Zhenbo Wang ◽  
Jiajia Chen
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Food Product ◽  
Integrated Approach ◽  
Mechanistic Modeling ◽  
Bayesian Optimization ◽  
Initial Training ◽  
Thickness Optimization ◽  
Heating Uniformity ◽  
Food Design

Mechanistic-modeling has been a useful tool to help food scientists in understanding complicated microwave-food interactions, but it cannot be directly used by the food developers for food design due to its resource-intensive characteristic. This study developed and validated an integrated approach that coupled mechanistic-modeling and machine-learning to achieve efficient food product design (thickness optimization) with better heating uniformity. The mechanistic-modeling that incorporated electromagnetics and heat transfer was previously developed and validated extensively and was used directly in this study. A Bayesian optimization machine-learning algorithm was developed and integrated with the mechanistic-modeling. The integrated approach was validated by comparing the optimization performance with a parametric sweep approach, which is solely based on mechanistic-modeling. The results showed that the integrated approach had the capability and robustness to optimize the thickness of different-shape products using different initial training datasets with higher efficiency (45.9% to 62.1% improvement) than the parametric sweep approach. Three rectangular-shape trays with one optimized thickness (1.56 cm) and two non-optimized thicknesses (1.20 and 2.00 cm) were 3-D printed and used in microwave heating experiments, which confirmed the feasibility of the integrated approach in thickness optimization. The integrated approach can be further developed and extended as a platform to efficiently design complicated microwavable foods with multiple-parameter optimization.

scholarly journals Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

2021 ◽  
pp. 1639-1648
Author(s):  
Yu-Ting Chen ◽  
Marc Duquesnoy ◽  
Darren H. S. Tan ◽  
Jean-Marie Doux ◽  
Hedi Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
High Quality ◽  
Solid State Electrolyte

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

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