Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing

• URL: http://arxiv.org/abs/2112.01687v1
• Date: Fri, 3 Dec 2021 02:51:15 GMT
• Title: Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
• Authors: Loc Truong, WoongJo Choi, Colby Wight, Lizzy Coda, Tegan Emerson, Keerti Kappagantula, Henry Kvinge
• Abstract summary: We propose a machine learning framework, differential property classification (DPC) DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.
• Score: 2.905624971705889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), a solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.

Related papers

• Data-driven tool wear prediction in milling, based on a process-integrated single-sensor approach [1.6574413179773764]
  This study explores data-driven methods, in particular deep learning, for tool wear prediction. The study evaluates several machine learning models, including convolutional neural networks (CNN), long short-term memory networks (LSTM), support vector machines (SVM) and decision trees. The ConvNeXt model has an exceptional performance, achieving a 99.1% accuracy in identifying tool wear using data from only four milling tools operated until they are worn.
  arXiv  Detail & Related papers  (2024-12-27T23:10:32Z)
• A hybrid machine learning framework for clad characteristics prediction in metal additive manufacturing [0.0]
  Metal additive manufacturing (MAM) has experienced significant developments and gained much attention. Predicting the impact of processing parameters on the characteristics of an MAM printed clad is challenging due to the complex nature of MAM processes. Machine learning (ML) techniques can help connect the physics underlying the process and processing parameters to the clad characteristics.
  arXiv  Detail & Related papers  (2023-07-04T18:32:41Z)
• Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics [54.172707311728885]
  We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED) Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
  arXiv  Detail & Related papers  (2023-06-03T06:19:20Z)
• Addressing computational challenges in physical system simulations with machine learning [0.0]
  We present a machine learning-based data generator framework tailored to aid researchers who utilize simulations to examine various physical systems or processes. Our approach involves a two-step process: first, we train a supervised predictive model using a limited simulated dataset to predict simulation outcomes. Subsequently, a reinforcement learning agent is trained to generate accurate, simulation-like data by leveraging the supervised model.
  arXiv  Detail & Related papers  (2023-05-16T17:31:50Z)
• Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing [1.5234614694413722]
  We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery. We generate realistic images conditioned on temper and either experimental parameters or material properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes.
  arXiv  Detail & Related papers  (2023-01-13T00:48:39Z)
• Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
  Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
  arXiv  Detail & Related papers  (2022-09-05T16:48:34Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data [0.0]
  Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. We describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model.
  arXiv  Detail & Related papers  (2021-03-19T02:00:14Z)
• Multi-Stage Influence Function [97.19210942277354]
  We develop a multi-stage influence function score to track predictions from a finetuned model all the way back to the pretraining data. We study two different scenarios with the pretrained embeddings fixed or updated in the finetuning tasks.
  arXiv  Detail & Related papers  (2020-07-17T16:03:11Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)
• Predicting Performance for Natural Language Processing Tasks [128.34208911925424]
  We build regression models to predict the evaluation score of an NLP experiment given the experimental settings as input. Experimenting on 9 different NLP tasks, we find that our predictors can produce meaningful predictions over unseen languages and different modeling architectures.
  arXiv  Detail & Related papers  (2020-05-02T16:02:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.