Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches

Additive manufacturing (AM) is gaining attention across various industries like healthcare, aerospace, and automotive. However, identifying defects early in the AM process can reduce production costs and improve productivity - a key challenge. This study explored the effectiveness of machine learning (ML) approaches, specifically transfer learning (TL) models, for defect detection in 3D-printed cylinders. Images of cylinders were analyzed using models including VGG16, VGG19, ResNet50, ResNet101, InceptionResNetV2, and MobileNetV2. Performance was compared across two datasets using accuracy, precision, recall, and F1-score metrics. In the first study, VGG16, InceptionResNetV2, and MobileNetV2 achieved perfect scores. In contrast, ResNet50 had the lowest performance, with an average F1-score of 0.32. Similarly, in the second study, MobileNetV2 correctly classified all instances, while ResNet50 struggled with more false positives and fewer true positives, resulting in an F1-score of 0.75. Overall, the findings suggest certain TL models like MobileNetV2 can deliver high accuracy for AM defect classification, although performance varies across algorithms. The results provide insights into model optimization and integration needs for reliable automated defect analysis during 3D printing. By identifying the top-performing TL techniques, this study aims to enhance AM product quality through robust image-based monitoring and inspection

Invariant Scattering Transform for Medical Imaging

Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and orientation in the input signal. IST aims to be invariant to transformations that are common in medical images, such as translation, rotation, scaling, and deformation, used to improve the performance in medical imaging applications such as segmentation, classification, and registration, which can be integrated into machine learning algorithms for disease detection, diagnosis, and treatment planning. Additionally, combining IST with deep learning approaches has the potential to leverage their strengths and enhance medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners

SUCCESS in Engineering Education: Applying an ID Motivational Framework to Promote Engagement and Innovation

The Journal of Applied Instructional Design is available at http://www.jaidpub.org.The purpose of this study was to identify motivational gaps and design to optimize for motivational needs in a current university course in mechanical engineering. The course instructor and instructional designer collaboratively used the SUCCESS framework (Hardré, 2009) to assess the existing motivational components of the course, examine gaps in the course relative to its goals, and then propose motivating strategies to address those gaps. This paper presents the model and course description, process and products of the analysis, and strategic redesign of the course to optimize motivation for engagement and innovation. This project demonstrates the iterative process of exposing both implicit and explicit motivational elements of instruction and identifying opportunities to improve them. For this process it utilizes coursework in an applied profession that requires open-ended problem-solving and solution design. It illustrates the utility of the SUCCESS framework, as well as an implementation process, for identifying and addressing motivational gaps in instruction, based on key competencies and performance goals.Ye

Internet-based Framework to Support Integration of Customer in the Design of Customizable Products

A necessary element to design and produce customer-centric products is the integration of customers in the design process. Challenges faced during customer integration into the design process include generating models of the customized product, performing analysis of these to determine feasibility, and optimizing to increase the performance. These tasks have to be performed relatively quickly, if not in real time, to provide feedback to the customer. The focus of this article is to present a framework that utilizes CAD, finite element analysis (FEA), and optimization to integrate the customer into the design process via the Internet for delivering user customized products. The design analysis, evaluation, and optimization need to be automated and enhanced to enable operation over the Internet. A product family CAD/FEA template has been developed to perform analysis, along with a general formulation to optimize the customized product. The CAD/FEA template generalizes the geometry building and analysis of each configuration developed using a product platform approach. The proposed setup is demonstrated through the use of a bicycle frame family. In this study, the focus is on the application of optimization and FEA to facilitate the design of customer-centric products.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Experiments on the Neurocognitionof Creativity

Creative production is often correlated to divergent thinking to produce many different ideas; hence, for the engineering education domain, design learning presents opportunities to enhance divergent thinking.https://openprairie.sdstate.edu/asee_nmws_2020_posters/1002/thumbnail.jp

Squamous cell carcinoma developed on hypertropic lichen planus

Carcinoma occurring in the cutaneous lesions of Lichen Planus though rarely mentioned in literature does occw-and should be kept in mind while treating such lesions. We report a 30 year female who developed a squamous cell carcinoma in a long standing hypertropic lichen planus in the lower leg. This case is being presented to indicate the possibility of malignant transformation of cutaneous lichen planus to carcinoma, especially in the hypertrophic fonns and the need to have an early diagnosis so that it can be treated in the initial stages. A high degree of suspicion should be present when­ever we come across a non healing lesion in a patient with lichen planus. A few markers, which may give us a clue for increased chances of malignant transformation in these cases is presented

Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method.Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye

The Investigation of the Relationship Between Emotional Engagement and Creativity

Background - One of the most critical challenges in engineering education is improving students’ divergent thinking skills. Usually, we observe students’ fixating on only one single solution for engineering problems. However, their ability to think outside the box and provide alternative solutions should be developed. Research shows that engagement may foster the development of thoughts and boost creativity. Purpose/Hypothesis – Our aim was to investigate students’ engagement with tasks that inspire different facets of creativity (verbal, numeric, and visual). Considering the role of demographics in student engagement, we explored the relationship between their engagement level and demographic traits such as gender, major, age, grades (GPA), and the languages they know besides their native tongue. Design/Method - We utilized electrodermal activity (EDA) sensors, a well-documented proxy of emotional engagement, to measure students’ engagement level while performing tasks that inspire different facets of creativity (verbal, numeric, and visual). Due to the non-normal distribution of the data, non-parametric statistical tests were conducted considering engagement as a dependent variable and demographic traits as independent variables. Results - Statistically significant differences in students’ engagement when exposed to creativity inspired tasks were observed. However, no association between demographics and engagement levels were detected. Conclusions - The results of the study may support educators in designing the instructional materials considering creativity-inspired activities so that students’ engagement level can be increased. Further, results from this study can inform experimental designs, specifically participant selection, in engagement focused studies

Estimating Cost Savings when Implementing a Product Platform Approach

Many market forces are driving companies to improve their targeting of increasingly small market niches. To accomplish this efficiently, products are organized into product families that typically share common platforms. To reorganize the current product offerings or new products into a product family, using a platform approach, requires estimating the savings for such a modification. One of the problems encountered in estimating development and design cost is the lack of availability of hard information during the initial design phases. The purpose of this paper is to estimate the design and development cost, when moving towards a platform approach, using simple models. The activity based product family cost models are developed from existing single product design activities, which are modified and extended to reflect activities related to development of product platform and subsequent product family members supported by the platform. Uncertainty related to cost associated with activities are included in the model, which is solved using Monte Carlo simulation. The approach is demonstrated using a hard disk drive spindle motor platform development for a family of hard disks.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline