In-Situ Process Monitoring for Metal Additive Manufacturing (AM) Through Acoustic Technique

Additive Manufacturing (AM) is currently a widely used technology in different industries such as aerospace, medical, and consumer products. Previously it was mainly used for prototyping of the products, but now it is equally valuable for commercial product manufacturing. More profound understanding is still needed to track and identify defects during the AM process to ensure higher quality products with less material waste. Nondestructive testing becomes an essential form of testing for AM parts, where AE is one of the most used methods for in situ process monitoring. The Acoustic Emission (AE) approach has gained a reputation in nondestructive testing (NDT) as one of the most influential and proven techniques in numerous engineering fields. Material testing through Acoustic Emission (AE) has become one of the most popular techniques in AM because of its capability to detect defects and anomalies and monitor the progress of flaws. Various AE technique approaches have been under investigation for in-situ monitoring of AM products. The preliminary results from AE exploration show promising results which need further investigation on data analysis and signal processing. AE monitoring technique allows finding the defects during the fabrication process, so that failure of the AM can be prevented, or the process condition can be finely tuned to avoid significant damages or waste of materials. In this work, recorded AE data over the Direct Energy Deposition (DED) additive manufacturing process was analyzed by the Machine Learning (ML) algorithm to classify different build conditions. The feature extraction method is used to obtain the required data for further processing. Wavelet transformation of signals has been used to acquire the time-frequency spectrum of the AE signals for different process conditions, and image processing by Convolutional Neural Network (CNN) is used to identify the transformed spectrum of different build conditions. The identifiers in AE signals are correlated to the part quality by statistical methods. The results show a promising approach for quality evaluation and process monitoring in AM. In this work, the assessment of deposition properties at different process conditions is also done by optical microscope, Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and nanoindentation technique

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Management and Modeling of Winter-time Basil Cultivars Grown with a Cap MAT System

Basil (Ocimum basilicum) is a high value crop, currently grown in the field and greenhouses in Nebraska. Winter-time, greenhouse studies were conducted during 2015 and 2016, focusing on cultivars of basil grown on a Cap MAT II® system with various levels of fertilizer application. The goal was to select high value cultivars that could be grown in Nebraska greenhouses. The studies used water content, electrical conductivity, photosynthetically active radiation (PAR), and relative humidity, air and soil media temperature sensors. Greenhouse systems can be very complex, even though controlled by mechanical heating and cooling. Uncertain or ambiguous environmental and plant growth factors can occur, where growers need to plan, adapt, and react appropriately. Plant harvest weights and electronic sensor data was recorded over time and used for training and internally validating fuzzy logic inference and classification models. Studies showed that GENFIS2 ‘subtractive clustering’ of data, prior to ANFIS training, resulted in good correlations for predicted growth (R2 \u3e 0.85), with small numbers of effective rules and membership functions. Cross-validation and internal validation studies also showed good correlations (R2 \u3e 0.85). Decisions on basil cultivar selection and forecasting as to how quickly a basil crop will reach marketable size will help growers to know when to harvest, for optimal yield and predictable quantity of essential oils. If one can predict reliably how much essential oil will be produced, then the methods and resultant products can be proposed for USP or FDA approval. Currently, most plant medicinal and herbal oils and other supplements vary too widely in composition for approval. The use of fuzzy set theory could be a useful mathematical tool for plant and horticultural production studies