A Physics-Informed Convolutional Neural Network with Custom Loss Functions for Porosity Prediction in Laser Metal Deposition

Physics-informed machine learning is emerging through vast methodologies and in various applications. This paper discovers physics-based custom loss functions as an implementable solution to additive manufacturing (AM). Specifically, laser metal deposition (LMD) is an AM process where a laser beam melts deposited powder, and the dissolved particles fuse to produce metal components. Porosity, or small cavities that form in this printed structure, is generally considered one of the most destructive defects in metal AM. Traditionally, computer tomography scans measure porosity. While this is useful for understanding the nature of pore formation and its characteristics, purely physics-driven models lack real-time prediction ability. Meanwhile, a purely deep learning approach to porosity prediction leaves valuable physics knowledge behind. In this paper, a hybrid model that uses both empirical and simulated LMD data is created to show how various physics-informed loss functions impact the accuracy, precision, and recall of a baseline deep learning model for porosity prediction. In particular, some versions of the physics-informed model can improve the precision of the baseline deep learning-only model (albeit at the expense of overall accuracy).

Argumentointimallit fysiikan tiedon kuvaamisessa

Argumentointi on tunnistettu tärkeäksi koulutuksen tavoitteeksi, minkä vuoksi sitä on painotettu luonnontieteiden opetuksessa. Painotus on avannut lupaavia mahdollisuuksia, mutta myös ongelmia, joiden ymmärtämiseksi tarkastelemme neljää argumentoinnin mallia. Tavoitteena on ymmärtää, millaisella mallilla voitaisiin kattavasti kuvata fysiikan tiedon argumentointia opettajakoulutuksen kontekstissa. Toulmin kuvaa argumentin rakennetta ottamatta kantaa sisältötiedon oikeellisuuteen. Böttcher ja Meisert kuvaavat argumentointia selitysmallien kehittymisen näkökulmasta. Sampson tarkastelee argumentointia koulukokeellisuuden näkökulmasta. Sandoval ja Millwood käsittelevät argumentin käsitteellistä ja episteemistä laatua. Argumentointimallit ovat ansiokkaita, mutta riittämättömiä fysiikan tiedon luonteen kuvaamiseksi. Niistä puuttuu muun muassa fysiikan tiedonmuodostuksen kannalta keskeinen teorialähtöisen päätelmän näkökulma. Fysiikan tiedon argumentointiin tarvitaankin fysiikan tiedon luonteesta kumpuava malli. Argumentation Models in Illustrating Physics Knowledge Abstract Understanding scientific argumentation is an important educational goal. To emphasize argumentation in science class, we need suitable models to illustrate what argumentation is. We review four argumentation models to find perspectives for specifically physics argumentation. Toulmin’s model identifies argumentative moves. Böttcher and Meisert see argumentation as comparing data and explanatory models. Sampson looks at the use of evidence. Sandoval and Millwood study the conceptual and epistemic quality of an argument. Deductive reasoning, which is a relevant part of physics knowledge, is not for example lacking in some argumentation models. Physics argumentation needs a model, which raises from the nature of physics knowledge. Keywords: argumentation, argumentation models, physics

Enhancing sundanese students’ creative thinking skills using ICARE model on physics concepts: A rasch analysis approach

Creative thinking skills are vital in physics knowledge as in physics knowledge equal through investigation arrangements on attentions accomplishment subsequently sensing, articulating difficulties, gathering information, dispensation and seeing information, and concluding. This study drives to enhance Sundanese students’ creative thinking skills on physics by the ICARE model through Rasch Analysis. The investigate technique used a pre-experimental technique with one group pretest-posttest design for ten grade pupils (10 boys and 12 girls) and exploitation Rasch analysis to portion the person consistency with bubble chart and variable wright map. The instrument was based on indicators by Torrance. Informations were composed through pretest and posttest to amount creative thinking skills and person consistency on students. The outcomes displayed that ICARE model influenced creative thinking skills through the middling score of 0.61 characterized as intermediate. It can be decided that ICARE model is one of the erudition models that can stimulus and enhance students’ creative thinking skills on physics ideas.

The effects of instructional design based web course on pre-service teachers’ competencies

<span>Web Pedagogical Content Knowledge (WPACK) is an important competence for pre-service teachers in the educational technology course. However, novice pre-service teachers require the preparation stage to integrate the Web into instruction. The purpose of the study was to develop and to investigate the new instructional model for pre-service teachers in integrating the Web. The Preparation, Isolation, Transformation, Action, Reflection, and Revision (PINTARR) and two other models were implemented in three physics education technology groups with seventy-four participants. The instrument test was constructed to assess the pre-service teachers’ competencies, namely Student analysis, Curriculum organization, Instructional strategy selection, Evaluation, Technological knowledge, and Physics knowledge. The result of MANOVA showed pre-service teachers in PINTARR group outperformed overall the pre-service teachers’ competencies rather than the Multimedia and Web Design Learning group. The results indicated that the Preparation and the Isolation stage were the most important for novice pre-service teachers in improving the competencies.</span>

Intelligence and academic performance: Is it all in your head?

Academic performance relies, in part, on intelligence; however, intelligence quotient (IQ) is limited in predicting academic success. Furthermore, while the search for the biological seat of intelligence predates neuroscience itself, its findings remain conflicting. Here, we assess the interplay between IQ, academic performance, and brain connectivity with behavioral and functional MRI data collected from undergraduate students as they completed an active learning or lecture-based semester-long university physics course. IQ (i.e., full-scale WAIS scores) increased significantly pre-to post-instruction, were associated with physics knowledge and reasoning measures, but were unrelated to overall course grade. IQ was related to brain connectivity during physics-related cognition, but connectivity did not mediate IQ’s association with task performance. These relations depended on students’ sex and instructional environment, providing evidence that physics classroom environment and pedagogy may have a gendered influence on students’ performance. Discussion focuses on opportunities to improve physics reasoning skills for all students.