Morphological diversity in directionally-solidified microstructures with varying anisotropy of solid-liquid interfacial free energy

Morphological diversity in directionally-solidified microstructures of fcc-based binary alloys was investigated using quantitative phase-field simulation. The growth morphology for each set of anisotropy parameters was identified from the degree of undercooling of the dendrite tip and a morphology map was constructed. We investigated the effects of solidification conditions and alloy systems on growth morphology and found that the pulling speed has a significant effect, while temperature gradient and partition coefficient have only small effects on the morphology selection. Furthermore, we examined the emergence of doublon and triplet dendrites under conditions of weak interfacial anisotropy

Explainable AI for Material Property Prediction Based on Energy Cloud: A Shapley-Driven Approach

The scientific community has raised increasing apprehensions over the transparency and interpretability of machine learning models employed in various domains, particularly in the field of materials science. The intrinsic intricacy of these models frequently results in their characterization as “black boxes”, which poses a difficulty in emphasizing the significance of producing lucid and readily understandable model outputs. In addition, the assessment of model performance requires careful deliberation of several essential factors. The objective of this study is to utilize a deep learning framework called TabNet to predict lead zirconate titanate (PZT) ceramics’ dielectric constant property by employing their components and processes. By recognizing the crucial importance of predicting PZT properties, this research seeks to enhance the comprehension of the results generated by the model and gain insights into the association between the model and predictor variables using various input parameters. To achieve this, we undertake a thorough analysis with Shapley additive explanations (SHAP). In order to enhance the reliability of the prediction model, a variety of cross-validation procedures are utilized. The study demonstrates that the TabNet model significantly outperforms traditional machine learning models in predicting ceramic characteristics of PZT components, achieving a mean squared error (MSE) of 0.047 and a mean absolute error (MAE) of 0.042. Key contributing factors, such as d33, tangent loss, and chemical formula, are identified using SHAP plots, highlighting their importance in predictive analysis. Interestingly, process time is less effective in predicting the dielectric constant. This research holds considerable potential for advancing materials discovery and predictive systems in PZT ceramics, offering deep insights into the roles of various parameters

A versatile strategy for hybridizing small experimental and large simulation data: A case for ceramic tape-casting process

In manufacturing industry, finding optimal design parameters for targeted properties has traditionally been guided by trial and error. However, limited data availability to few hundreds sets of experimental data in typical materials processes, the machine-learning capabilities and other data-driven modeling (DDM) techniques are too far from it to be practical. In this study, we show how a versatile design strategy, tightly coupled with physics-based modeling (PBM) data, can be applied to small set of experimental data to improve the optimization of process parameters. Our strategy uses PBM to achieve augmented data that includes essential physics: in other words, the PBM data allows the inverse design model to ‘learn’ physics, indirectly. We demonstrated the accuracy of both forward-prediction and inverse-optimization have been dramatically improved with the help of PBM data, which are relatively cheap and abundant. Furthermore, we found that the inverse model with augmented data can accurately optimize process parameters, even for ones those were not considered in the simulation. Such versatile strategy can be helpful for processes/experiments for the cases where the number of collectable data is limited, which is most of the case in industries

Topological images of ESBL-EC treated with sub-MICs of EGCG, cefotaxime or their combinations.

<p>Cells were: treated with 100 mg/L of EGCG for 4 h (A) and 8 h (B); treated with 250 mg/L of EGCG for 4 h (C) and 8 h (D); treated with 4 mg/L of cefotaxime for 4 h (E) and 8 h (F); treated with 100 mg/L of EGCG and 4 mg/L of cefotaxime in combination for 4 h (G) and 8 h (H); and treated with 250 mg/L of EGCG and 4 mg/L of cefotaxime in combination for 4 h (I) and 8 h (J). Scale bar: 1 µm.</p

Piston and Piston Pin Manufacturing Process Improvement (Semester Unknown) IPRO 339

The team of IPRO 339’s main objective is to assist Burgess-Norton Manufacturing Company with eliminating a persisting problem that has been affecting the company for many years. Burgess-Norton Manufacturing Company is the world’s largest manufacturer of piston pins, as well as the leading producer of powder metal parts. Supplying many major car companies across the world, Burgess-Norton thrives on maintaining a high standard of quality in both their products and services. Recently there has been a change in the automobile industry that has affected the way piston pins are manufactured. These changes have brought on difficulties for the company as they must now change their product specs and adapt to the evolving market. Their main problem is the existence of nicks on the piston pins. The main goal of this IPRO team is to find a way to eliminate these nicks that occur throughout the company’s manufacturing process. This document will outline the main objectives and tasks that the team has been assigned with. Team structures, time lines and other plans are included. Also, a further company history and description of Burgess-Norton will be provided in order to make the objectives more clear.Sponsorship: Burgess-Norton Manufacturing CompanyDeliverable

MIC and FIC indices of cefotaxime in combination with EGCG against ESBL-EC.

<p>A, Cefotaxime alone; B, plus EGCG (50 mg/L); C, plus EGCG (100 mg/L); D, plus EGCG (250 mg/L). The MIC of EGCG alone was 1500 mg/L.</p>a<p>Fractional inhibitory concentration (FIC) was calculated as MIC of antibiotics alone or EGCG in com-bination divided by MIC of antibiotics or EGCG alone, and the FIC Index was obtained by adding theFICs.</p>b<p>FIC indices were interpreted as below: ≤0.5, synergy; >0.5 to 1, addition; and >1, indifference.</p

Piston and Piston Pin Manufacturing Process Improvement (Semester Unknown) IPRO 339: Piston%26PistonPinManufacturingProcessImprovementIPRO339FinalReportSp11_redacted

The team of IPRO 339’s main objective is to assist Burgess-Norton Manufacturing Company with eliminating a persisting problem that has been affecting the company for many years. Burgess-Norton Manufacturing Company is the world’s largest manufacturer of piston pins, as well as the leading producer of powder metal parts. Supplying many major car companies across the world, Burgess-Norton thrives on maintaining a high standard of quality in both their products and services. Recently there has been a change in the automobile industry that has affected the way piston pins are manufactured. These changes have brought on difficulties for the company as they must now change their product specs and adapt to the evolving market. Their main problem is the existence of nicks on the piston pins. The main goal of this IPRO team is to find a way to eliminate these nicks that occur throughout the company’s manufacturing process. This document will outline the main objectives and tasks that the team has been assigned with. Team structures, time lines and other plans are included. Also, a further company history and description of Burgess-Norton will be provided in order to make the objectives more clear.Sponsorship: Burgess-Norton Manufacturing CompanyDeliverable