Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.publishedVersio

Three-dimensional stress-based topology optimization using SIMP method

Structural topology optimization problems have been formulated and solved to minimize either compliance or weight of a design domain under volume or stress constraints. The introduction of three-dimensional analysis is a more realistic approach to many applications in industry and research, but most of the developments in stress-based topology optimization are two-dimensional. This article presents an extension of two-dimensional stress-based topology optimization into three-dimensional using SIMP method. The article includes a mathematical model for three-dimensional stress-based topology optimization problems and sensitivity analysis. The article also includes finite element analysis used to compute stress induced in the design domains. The developed model is validated using benchmark problems and the results are compared with three-dimensional compliance-based formulation. From the results, it was clear that the developed model can generate optimal topologies that can sustain applied loads under the boundary conditions defined

ENHANCING COMPUTATIONAL EFFICIENCY OF STRESS CONSTRAINED TOPOLOGY OPTIMIZATION

Structural topology optimization is a mathematical approach which seeks optimal material distribution for a given design domain under defined loading and boundary conditions. It has been formulated and solved either to minimize compliance or weight under volume and stress constraints, respectively

Three-dimensional stress-based topology optimization using SIMP method

Structural topology optimization problems have been formulated and solved to minimize either compliance or weight of a design domain under volume or stress constraints. The introduction of three-dimensional analysis is a more realistic approach to many applications in industry and research, but most of the developments in stress-based topology optimization are two-dimensional. This article presents an extension of two-dimensional stress-based topology optimization into three-dimensional using SIMP method. The article includes a mathematical model for three-dimensional stress-based topology optimization problems and sensitivity analysis. The article also includes finite element analysis used to compute stress induced in the design domains. The developed model is validated using benchmark problems and the results are compared with three-dimensional compliance-based formulation. From the results, it was clear that the developed model can generate optimal topologies that can sustain applied loads under the boundary conditions defined

Food taboo among pregnant Ethiopian women: magnitude, drivers, and association with anemia

Abstract Background There are pervasive pregnancy-related food taboos and myths (PRFT) in Ethiopia. The evidence, however, is limited on whether PRFT contributes to the burden of maternal anemia. Thus, this study was aimed to determine the magnitude of PRFT, the reasons for adherence to PRFT, and the association of adherence to PRFT with anemia, among pregnant Ethiopian women. Methods The study was case-control in design and recruited a sample of 592 pregnant women attending antenatal care in four health facilities in Addis Ababa, Ethiopia. Participants were classified into anemic cases (n = 187) and non-anemic controls (n = 405) based on their hemoglobin level. PRFT was assessed by the participants’ subjective reporting of avoidance of certain food items during the current pregnancy due to taboo reasons. The specific types of food items avoided and the underlying reasons for the avoidance were also assessed. The relation of PRFT with anemia was evaluated by multiple logistic regression analysis, controlling for covariate factors. Result Almost a fifth of the study participants (18.2%) avoided one or more food items due to PRFT. Adherence to PRFT was 26.2 and 14.6% among the anemic and the non-anemic individuals, respectively. The food items most avoided due to adherence to PRFT were green chili pepper, organ meat, and dark green leafy vegetables like spinach, lettuce, kale, and broccoli. The underlying reasons for the adherence to PRFT were largely traditionally held beliefs and misconceptions. After controlling for covariates, PRFT was significantly and independently associated with a higher odds of anemia [adjusted odds ratio (AOR) = 2.12, 95% confidence interval (CI) = 1.32–3.42, P = 0.002]. Conclusion PRFT might be contributing to the burden of maternal anemia in Ethiopia. It is time for public health authorities in Ethiopia to recognize PRFT as a public health risk, strengthen maternal nutrition counseling, and create public awareness of the consequences of PRFT. Trial registration ClinicalTrials.gov (NCT03251664), 16 August 2017

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain