Daniel Henkel

Bio: Daniel Henkel is an academic researcher. The author has contributed to research in topics: Artificial neural network & Pyramid. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Family learning: A process modeling method for cyber-additive manufacturing network

Abstract: A Cyber-Additive Manufacturing Network (CAMNet) integrates connected additive manufacturing processes with advanced data analytics as computation services to support personalized product realizatio...

Pyramid Ensemble Convolutional Neural Network for Virtual Computed Tomography Image Prediction in a Selective Laser Melting Process

Abstract: Additive manufacturing (AM) is a type of advanced manufacturing process that enables fast prototyping to realize personalized products in complex shapes. However, quality defects existed in AM products can directly lead to significant failures (e.g., cracking caused by voids) in practice. Thus, various inspection techniques have been investigated to evaluate the quality of AM products, where X-ray computed tomography (CT) serves as one of the most accurate techniques to detect geometric defects (e.g., voids inside an AM product). Taking a selective laser melting (SLM) process as an example, voids can be detected by investigating CT images after the fabrication of products with limited disturbance from noises. However, limited by the sensor size and scanning speed issue, CT is difficult to be used for online (i.e., layer-wise) voids detection, monitoring, and process control to mitigate the defects. As an alternative, optical cameras can provide layer-wise images to support online voids detection. The intricate texture of the layer-wise image restricts the accuracy of void detection in AM products. Therefore, we propose a new method called pyramid ensemble convolutional neural network (PECNN) to efficiently detect voids and predict the texture of CT images using layer-wise optical images. The proposed PECNN can efficiently extract informative features based on the ensemble of the multiscale feature-maps (i.e., image pyramid) from optical images. Unlike deterministic ensemble strategies, this ensemble strategy is optimized by training a neural network in a data-driven manner to learn the fine-grained information from the extracted feature-maps. The merits of the proposed method are illustrated by both simulations and a real case study in a SLM process.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Fog Computing for Distributed Family Learning in Cyber-Manufacturing Modeling

Abstract: Cyber-manufacturing systems (CMS) interconnect manufacturing facilities via sensing and actuation networks to provide reliable computation and communication services in smart manufacturing. In CMS, various advanced data analytics have been proposed to support effective decision-making. However, most of them were formulated in a centralized manner to be executed on single workstations, or on Cloud computation units as the data size dramatically increases. Therefore, the computation or communication service may not be responsive to support online decision-making in CMS. In this research, a method to decompose a group of existing advanced data analytics models (i.e., family learning for CMS modeling) into their distributed variants is proposed via alternative direction method of multipliers (ADMM). It improves the computation services in a Fog-Cloud computation network. A simulation study is conducted to validate the advantages of the proposed distributed method on Fog-Cloud computation network over Cloud computation system. Besides, six performance evaluation metrics are adopted from the literature to access the performance of computation and communication. The evaluation results also indicate the relationship between Fog-Cloud architectures and computation performances, which can contribute to the efficient design of Fog-Cloud architectures in the future.