Active Learning to Support In-situ Process Monitoring in Additive Manufacturing

Dasari, Siva Krishna; Cheddad, Abbas; Lundberg, Lars; Palmquist, Jonatan

doi:10.1109/icmla52953.2021.00190

Cited by 9 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A solution to this problem that has made major inroads in recent years is active learning (AL), which is a process to build an ML model (and dataset) iteratively from a small starting set of training points using the principles of Bayesian optimization (BO). − This creates a path to obtain informed recommendations for successive experiments, minimizing the number of experiments required (to meet a target material or performance metric) and thus expediting the development of new materials. AL has been used to accelerate material design problems in polymers, alloys, and ceramics. − Within the AM space, ML has been used to enhance in situ process monitoring to improve print quality through optimizing printing parameters, resin composition, and component attributes. ,− AL has been used to accomplish such tasks with automated decision making. ,, A small subset of recent literature deals with the modeling of acrylates for several properties, albeit in a limited capacity. Notable contributions include the modeling of glass transition temperatures for copolymers, tested on an expansive acrylate dataset, and the application of physics-constrained BO to enhance tensile strength and toughness of thermoplastic polymers. , The expanding literature highlights the potential for further investigation into optimizing multiple acrylate properties in data-scarce situations using novel data recommendation approaches and ML algorithms.…”

Section: Introductionmentioning

confidence: 99%

Machine-Guided Discovery of Acrylate Photopolymer Compositions

Jain,

Armstrong,

Joseph

et al. 2024

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young’s modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young’s modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young’s modulus. We then use the materials designed by the ML model to 3D print a multimaterial “hand” with soft “skin” and rigid “bones”. This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine-Guided Discovery of Acrylate Photopolymer Compositions

Jain,

Armstrong,

Joseph

et al. 2024

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

show abstract

“…Active learning has found success in many applications including image segmentation [3], sequence labeling [4], medical image classification [5], cybersecurity [6], [7], and manufacturing [8]. Yet, active learning methods are heavily model-dependent, thus datapoints labeled for one model may not be effective for the training of other models [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Active Learning with Combinatorial Coverage

Prathyush¹,

Cody²,

Beling³

et al. 2023

Preprint

View full text Add to dashboard Cite

Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data labeling but are heavily model-reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. Both issues are of crucial concern in machine learning deployment. We propose active learning methods utilizing combinatorial coverage to overcome these issues. The proposed methods are data-centric, as opposed to modelcentric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to better performing models and has a competitive sampling bias compared to benchmark methods.

show abstract

Autonomous Output‐Oriented Aerosol Jet Printing Enabled by Hybrid Machine Learning

Du,

Jiang,

Zhang

2024

Adv Materials Technologies

View full text Add to dashboard Cite

Additive manufacturing (AM) is rapidly revolutionizing modern manufacturing with recent progress in advanced printing methods and improved properties of printed materials. However, traditional AM methods are limited by their input‐oriented nature, which demands tedious trial‐and‐error tuning of printing parameters to achieve desired output properties. Here, an output‐oriented artificial intelligence‐integrated AM (AIAM) method is reported that enables an user to specify desired output properties while the printer autonomously discovers the optimal input printing parameters by integrating hybrid machine learning models and in situ measurements. Based on a predictive mapping between the input printing parameters and the output properties of interests established with <20 experiments designed by active learning, inverse design tasks are performed to intelligently generate the printing parameter settings that lead to desired outcomes using reinforcement learning. This method is demonstrated by autonomous aerosol jet printing (AJP) of conductive polymer films and achieving user‐defined electrical resistances with an ultralow error of 3.7%. The AIAM method, with its output‐oriented nature, holds the potential to significantly improve the autonomy, predictability, efficiency, and accessibility of the AM processes, which will unlock new possibilities in the autonomous and intelligent printing of a broad range of functional materials and devices.

show abstract

Active Learning to Support In-situ Process Monitoring in Additive Manufacturing

Cited by 9 publications

References 20 publications

Machine-Guided Discovery of Acrylate Photopolymer Compositions

Machine-Guided Discovery of Acrylate Photopolymer Compositions

Active Learning with Combinatorial Coverage

Autonomous Output‐Oriented Aerosol Jet Printing Enabled by Hybrid Machine Learning

Contact Info

Product

Resources

About