The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

A Framework for Life Cycle Cost Estimation of a Product Family at the Early Stage of Product Development

A cost estimation method is required to estimate the life cycle cost of a product family at the early stage of product development in order to evaluate the product family design. There are difficulties with existing cost estimation techniques in estimating the life cycle cost for a product family at the early stage of product development. This paper proposes a framework that combines a knowledge based system and an activity based costing techniques in estimating the life cycle cost of a product family at the early stage of product development. The inputs of the framework are the product family structure and its sub function. The output of the framework is the life cycle cost of a product family that consists of all costs at each product family level and the costs of each product life cycle stage. The proposed framework provides a life cycle cost estimation tool for a product family at the early stage of product development using high level information as its input. The framework makes it possible to estimate the life cycle cost of various product family that use any types of product structure. It provides detailed information related to the activity and resource costs of both parts and products that can assist the designer in analyzing the cost of the product family design. In addition, it can reduce the required amount of information and time to construct the cost estimation system

Computer vision-based monitoring of abrasive loading during wood machining

Surface quality is an important characteristic commonly assessed in wooden products. Sanding relies on coated abrasives as tooling for both dimensioning and surface finishing but their performance is dependent on chip loading and grit wear. Traditionally, the useful life of abrasive belts in sanding operation has been manually assessed. This type of inspection is highly subjective and dependent upon individual expertise, consequently leading to under utilization or over utilization of the abrasive. This, in turn, affects the production costs and quality of the product. In this work, an intelligent classification method that determines the optimal replacement policy for a belt exposed to known manufacturing parameters is developed. Controlled experiments were conducted to develop abrasive belts of known exposure, followed with digital microscopy to capture images and process them with pattern recognition and classification algorithms. Grit size and machining time were the parameters of interest while response of the experiments included image information from the abrasive sheets after every experimental run. These images were used in training an artificial neural network that in turn, help in determining data to categorize the useful life of the abrasive. The results show a 95% success rate in accurately classifying abrasive images of similarly conditioned abrasives. Also, the results show that the classification of interpolated and extrapolated times of abrasive usage are classified with a 95% success rate. A classification of abrasive images is proposed to be used as one of the inputs to a decision system that would help in evaluating the life of the abrasive and replacement policies. Further research on the relationship between the different parameters affecting the useful life of the abrasive is proposed

Measurements, Models, Systems and Design

531 s.

Laser Surface Treatment and Laser Powder Bed Fusion Additive Manufacturing Study Using Custom Designed 3D Printer and the Application of Machine Learning in Materials Science

Selective Laser Melting (SLM) is a laser powder bed fusion (L-PBF) based additive manufacturing (AM) method, which uses a laser beam to melt the selected areas of the metal powder bed. A customized SLM 3D printer that can handle a small quantity of metal powders was built in the lab to achieve versatile research purposes. The hardware design, electrical diagrams, and software functions are introduced in Chapter 2. Several laser surface engineering and SLM experiments were conducted using this customized machine which showed the functionality of the machine and some prospective fields that this machine can be utilized. Chapter 3 evaluated the effects of laser beam irradiation-based surface modifications of Ti-10Mo alloy samples under either Ar or N2 environment to the corrosion resistance and cell integration properties. The customized 3D printer was used to conduct the laser surface treatment. The electrochemical behaviors of the Ti-10Mo samples were evaluated in simulated body fluid maintained at 37 ± 0.5 ̊C, and a cell-material interaction test was conducted using the MLO-Y4 cells. Laser surface modification in the Ar environment was found to enhance corrosion behavior but did not affect the surface roughness, element distribution, or cell behavior, compared to the non-laser scanned samples. Processing the Ti-10Mo alloy in N2 formed a much rougher TiN surface that improved both the corrosion resistance and cell-material integration compared with the other two conditions. The mechanical behavior of spark plasma sintering (SPS) treated SLM Inconel 939 samples was evaluated in Chapter 4. Flake-like precipitates (η and σ phases) are observed on the 800-SPS sample surface which increased the hardness and tensile strength compared with the as-fabricated samples. However, the strain-to-failure value decreased due to the local stress concentration. γ’/ γ’’ phases were formed on the 1200-SPS sample. Although not fully formed due to the short holding time, the 1200-SPS sample still showed the highest hardness value and best tensile strength and deductibility. Apply machine learning to the materials science field was discussed in the fifth chapter. Firstly, a simple (Deep Neural Network) DNN model is created to predict the Anti-phase Boundary Energy (APBE) based on the limited training data. It achieves the best performance compared with Random Forest Regressor model and K Neighbors Regressor model. Secondly, the defects classification, the defects detection, and the defects image segmentation are successfully performed using a simple CNN model, YOLOv4 and Detectron2, respectively. Furthermore, defects detection is successfully applied on video by using a sequence of CT scan images. It demonstrates that Machine Learning (ML) can enable more efficient and economical materials science research

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog