Traditionally, the discovery of new materials has often depended on scholars’ computational and experimental experience. The traditional trial-and-error methods require many resources and computing time. Due to new materials’ properties becoming more complex, it is difficult to predict and identify new materials only by general knowledge and experience. Material prediction tools based on machine learning (ML) have been successfully applied to various materials fields; they are beneficial for modeling and accelerating the prediction process for materials that cannot be accurately predicted. However, the obstacles of disciplinary span led to many scholars in materials not having complete knowledge of data-driven materials science methods. This paper provides an overview of the general process of ML applied to materials prediction and uses solid-state electrolytes (SSE) as an example. Recent approaches and specific applications to ML in the materials field and the requirements for building ML models for predicting lithium SSE are reviewed. Finally, some current obstacles to applying ML in materials prediction and prospects are described with the expectation that more materials scholars will be aware of the application of ML in materials prediction.
Designers frequently utilize engineering equipment to create physical prototypes during the iterative concept generation and prototyping phases of design. Currently, evaluating designers' efficiency during prototype creation is a manual process that either involves observational or survey based approaches. Real-time feedback when using engineering equipment has the potential to enhance designers' efficiency or mitigate potential injuries that may result from incorrect use of equipment. Toward an automated approach to addressing these challenges, the authors of this work test the hypotheses that (i) there exists a difference in designers' comfort levels before and after they use a piece of engineering prototyping equipment and (ii) a machine learning model predicts the level of comfort a designer has while using engineering prototyping equipment with accuracies greater than random chance. It has been shown that the level of comfort that an individual has while completing a task impacts their performance. The authors investigate whether automatic tracking of designers' facial expressions during prototype creation predicts their level of comfort. A study, involving 37 participants using various engineering equipment, is used to validate the approach. The support vector machine (SVM) regression model yielded a range of R squared values from 0.82 to 0.86 for an equipment-specific model. A general model built to predict comfort level across all engineering equipment yielded an R squared value of 0.68. This work has the potential to transform the manner in which design teams utilize engineering equipment toward more efficient concept generation and prototype creation processes.
Assessment and feedback play an instrumental role in an individual’s learning process. Continued assistance is required to help students learn better and faster. This need is especially prominent in engineering laboratories where students must perform a wide range of tasks using different machines. One approach to understanding how students feel towards using certain machines is to assess their affective states while they use these machines. Affective state can be defined as the state of feeling an emotion. The authors of this work hypothesize that there is a correlation between students’ perceived affective states and task complexity. By adopting the Wood’s complexity model, the authors propose to assess how the correlations of perceived affective states of students change while they perform tasks of different complexity. In this study, each student performs a “hard” and an “easy” task on the same machine. Each student is given the same tasks using the same materials. Knowledge gained from testing this hypothesis will provide a fundamental understanding of the tasks that negatively impact students’ affective states and risk them potentially dropping out of STEM tracks, and the tasks that positively impact students’ affective states and encourage them to engage in more STEM-related activities. A case study involving 22 students using a power saw machine is conducted. Perceived affective states and completion time were collected. It was found that task complexity has an effect on subjects’ affective states. In addition, we observed some weak correlation between some of the perceived affective states and laboratory task performance. The distribution of correlation between affective states may change as the tasks change. With the knowledge of the relationship between task complexity and affective states, there is the potential to predict students’ affective states before starting a given engineering task.
Lithium niobate (LN) is a type of multifunctional dielectric and ferroelectric crystal that is widely used in acoustic, optical, and optoelectronic devices. The performance of pure and doped LN strongly depends on various factors, including its composition, microstructure, defects, domain, and homogeneity. The structure and composition homogeneity can affect both the chemical and physical properties of LN crystals, including their density, Curie temperature, refractive index, and piezoelectric and mechanical properties. In terms of practical demands, both the composition and microstructure characterizations these crystals must range from the nanometer scale up to the millimeter and wafer scales. Therefore, LN crystals require different characterization technologies when verifying their quality for various device applications. Optical, electrical, and acoustic technologies have been developed, including x-ray diffraction, Raman spectroscopy, electron microscopy, and interferometry. To obtain detailed structural information, advanced sub-nanometer technologies are required. For general industrial demands, fast and non-destructive technologies are preferable. This review outlines the advanced methods used to characterize both the composition and homogeneity of LN melts and crystals from the micro-to wafer scale.
Internet of Things (IoT) and data mining techniques have laid the foundation for the next generation of smart and secure manufacturing systems where big data are leveraged to extract useful information about the manufacturing processes and further help optimize decisions. The threat of data breach exists especially for nonpersonal, yet sensitive data, which are pertinent to every aspect of manufacturing. Data breach and privacy leakages can significantly impede the manufacturer’s business and lead to damaging a company’s reputation. With a comprehensive case study in the manufacturing setting, we show that adversaries can utilize accessible shop floor predictive models and other available background information to make inferences about sensitive attributes that were used as inputs to the original model and use that information for their own purposes. From this view, this article presents a privacy-preserving data mining framework to build a smart and secure manufacturing system. First, we introduce differential privacy (DP), an emerging approach to preserve the individual’s privacy in the data mining process. Second, we present a privacy-preserving system where DP mechanisms and queries are enforced to obtain differentially private results. Third, we propose to optimize the selection of DP mechanisms and privacy parameters by balancing the model utility and the robustness to attack. Further, we evaluate and validate the proposed privacy-preserving data mining framework with a real-world case study on the modeling of cutting power consumption in computer numerical control turning processes. Experimental results show that the performance gain, i.e., the trade-off between model utility and the robustness to attack, is improved from the nonprivate model by 5.6, 9.4, and 13.1 % for privacy-preserving Laplace, Gaussian, and sensitive mechanisms, respectively. This article is among the first to investigate and present a privacy-preserving data mining framework for smart manufacturing. The proposed methodology shows great potential to be generally applicable in industry for data-enabled smart and sustainable manufacturing.
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