Engineering topics

Hamoush, Sameer

Publications and source records attributed to Hamoush, Sameer.

4 records

DOE OSTI2024

Systems and methods for automated depowdering and transfer of additively manufactured objects

Systems and methods for additive manufacturing of objects are disclosed. The system includes an object builder subsystem configured to additively manufacture an object in a powder bed, an object extraction subsystem configured to extract the object from the powder bed, and a depowdering subsystem configured to remove powder from the object while the object is gripped by the gripper assembly. The object extraction subsystem includes a gripper assembly comprising two or more gripper members that each include a perforated mesh structure.

Elliott, Amelia M.↗

DOE OSTI2020

Integrating Predictions for Improving Defect Classification Accuracy in NDT-based Assessment of Concrete - 20229

There is an increasing need to create predictive models for defect classification in concrete using the output of non-destructive testing (NDT) techniques. Recent advancement of machine-learning algorithms has offered several techniques for developing classification models for different types of data sets. However, the performance of these algorithms is very uncertain, mainly when applied to small and noisy datasets. For example, when human access is limited (e.g., nuclear facility), robot-based NDT is preferred. But compared to manual tests with humans present on site, the data sets are small, and more noise can exist. Therefore, it is imperative to develop new approaches to ensure a consistently high classification accuracy for inadequate data sets. This study explores the classification performance on NDT dataset using classifiers from different machine-learning algorithms, namely k-Nearest Neighbor (kNN), Decision Tree, Naive Bayes, Logistic Regression, and Support Vector Machine (SVM). The authors further integrated the predictions from these classifiers using proposed methods. The integration strategy combines the output of the classifiers based on two different measures, accuracy, and performance (ACC and PERF), using equations such as sum, average, and square-root-of-sums-of-squares (SRSS). Our results reveal varying classification accuracies across individual classifiers with different misclassifications across the test data set. The integration strategy provided significant improvement in the classification accuracy compared to the individual classifiers. Furthermore, the results indicate minimal variation across the integration methods as compared to the variation across the individual classifiers. To conclude, prediction integration offers a unique approach for combining the output of multiple classifiers to create redundancies with the potential of achieving high classification performance and improved reliability in predictive models for defect detection in concrete. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Autonomous Navigation and Control of UGVs' in Nuclear Power Plants - 20381

The purpose of the husky A200 ground robot is to autonomously navigate through the places where it is very hazardous for human beings to reach and operate, like nuclear power plants, chemical industries. The aim is to navigate the ground robot autonomously with an Arm mounted on the robot along with the different sensors as camera, and Lidar. The autonomous motion of the robot is controlled by the controller which uses path planner for trajectory generation of the robot. The mission planner uses the current position of the husky A200, given the way points of the initial and the destination it would extract a best possible route based on the current events provided using GMapping. The global reference frame is used for planning the way points. Creating the appropriate path and the actions required to follow the path are given by the motion planner. The motion planner depends on the active sensor data such as obstacles, lanes, based on the sensor data feasible path is generated. Feasibility of the path is determined by the dynamics of the husky and a series of points generated with certain velocity and acceleration profile. The controller adjusts the lateral, longitudinal and yaw motion of the husky to command the behaviors. The kinematic model is developed for kinematic motion of the husky and the dynamic model is developed for transient and steady state characteristics. The images and other type of data captured by the camera are processed through the computational framework used to build machine learning models. TensorFlow will be used for deep learning and to identify and classify different objects around the husky. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

GPR Robotic Assisted Non-Destructive Evaluation of Concrete Structures - 20196

Reinforced concrete structures are always under exposure of internal or external deteriorations due to many factors such as environmental, nuclear radiation or even construction deficiencies. The use of nondestructive test is one of the reliable methods to inspect structural members. In this case, GPR has a good capability to detect inside deteriorations of concrete structures. However, in many cases it is hard to inspect structural members by hand due to safety issues or inaccessibility. Thus, an alternative way to reduce such risks is to employ robot to carry nondestructive equipment. In this research, twelve reinforced concrete RC beams (8 x 16 x 96, width, height and length) are cast in the lab. Three RC beams have no defect and nine others various defects such as corrosion, de-bonded bars, void, delamination and honeycombing. The one-point load is applied at the center of the RC beam. The loading level is carried out at three levels: 50%, 75% and 100% of maximum load and total load-displacement diagram are performed. The load is held at each level and the robotic GPR is used to scan the side of RC beams. Then, RADAN software is employed to find out any defects at different loading conditions and visualize the crack propagation. The RADAN results show that the pattern of crack propagation for corrosion and debonding are same. Also, the load-displacement results present that inside defect cause reduction of load-capacity of RC beam. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗