Correction: Optimal designs of the side sensitive synthetic chart for the coefficient of variation based on the median run length and expected median run length.

[This corrects the article DOI: 10.1371/journal.pone.

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing.

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM-printed parts.

Understanding droplet jetting on varying substrate for biological applications.

In the inkjet printing process, the droplet experience two phases, namely the jetting and the impacting phases. In this review article, we aim to understand the physics of a jetted ink, which begins during the droplet formation process. Following which, we highlight the different impacts during which the droplet lands on varying substrates such as solid, liquid, and less commonly known viscoelastic material.

Anomaly Detection in Fused Filament Fabrication Using Machine Learning.

Fused filament fabrication (FFF) has been widely used in various industries, and the adoption of technology is growing significantly. However, the FFF process has several disadvantages like inconsistent part quality and print repeatability. The occurrence of manufacturing-induced defects often leads to these shortcomings.

The Design and Development of Instrumented Toys for the Assessment of Infant Cognitive Flexibility.

The first years of an infant's life represent a sensitive period for neurodevelopment where one can see the emergence of nascent forms of executive function (EF), which are required to support complex cognition. Few tests exist for measuring EF during infancy, and the available tests require painstaking manual coding of infant behaviour. In modern clinical and research practice, human coders collect data on EF performance by manually labelling video recordings of infant behaviour during toy or social interaction.