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

The Design and Analysis of Experiments

Additive manufacturing is increasingly of interest for commercial and military applications due to its potential to create novel geometries with increased performance. For additive manufacturing to find commercial application, it will have to be cost competitive against traditional processes such as forging. Forecasting the production costs of future products prior to large-scale investment is challenging due to the limits of traditional cost accounting’s ability to handle the systemic process implications of new technologies and cognitive biases in humans’ additive and systemic estimates. Leveraging a method uniquely suited to these challenges, we quantify the production and use economics of an additively-manufactured versus a traditionally forged GE engine bracket for commercial aviation with equivalent performance. Our results show that, despite the simplicity of the engine bracket, when taking into account part redesign for AM and the associated lifetime fuel savings of the additively-designed bracket, the additively manufactured part and design is cheaper than the forged one for a wide range of scenarios, including at higher volumes of 2,000 to 12,000 brackets per year. Opportunities to further reduce costs include cheaper material prices without compromising quality, being able to produce vertical builds with equivalent performance to horizontal builds, and increasing process control so as to enable reduced testing. Given the conservative nature of our assumptions as well as our choice of part, these results suggest there may be broader economic viability for additively manufactured parts, especially when systemic factors and use costs are incorporated.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2882847_code2575183.pdf?abstractid=2815047&mirid=1

Metal Additive Manufacturing: Cost Competitive Beyond Low Volumes

Classification of optimization problems in fog computing

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. We argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

https://openresearch-repository.anu.edu.au/bitstream/1885/251724/1/Add%20Manufact%2046%20%282021%29%20102089.pdf

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing

An industrial cyber-physical system (ICPS) integrates the physical processes, systems and networks with the computation resources to provide reliable and responsive computation services. A cyber-manufacturing system (CMS), which is generated from ICPS, poses significant challenges on reliability, accuracy and responsiveness of computation services for manufacturing decision making. In this paper, we focus on reliability and responsiveness. Due to the heterogeneities of the computation and communication capacities and conditions, demanding computation services may not be completed in a timely manner. To facilitate reliable and responsive computation services, we propose a deadline constrained predictive offloading method based on a mobile-fog-cloud (MFC) network. This method optimizes the offloading decisions by solving a quadratically constrained integer linear programming constrained by latency requirements and predicted availability of devices. A newly constructed hybrid cyber-additive manufacturing network is used to test the performance of the proposed predictive offloading method and the MFC network. The results show that the proposed method outperforms mobile computing, fog computing, cloud computing, and fog-cloud computing benchmarks to minimize resource consumption and comply with latency requirements.

Predictive offloading in mobile-fog-cloud enabled cyber-manufacturing systems

Finite element analysis (FEA) has been widely adopted to identify potential defects in additive manufacturing (AM) processes. For personalized product realization, it is necessary to validate a number of heterogeneous product and process designs before or during manufacturing by using FEA. Multi-fidelity FEA simulations can be readily implemented with different capabilities in terms of simulation accuracy. However, due to its complexity, high-fidelity FEA simulation is time-consuming and decreases the efficiency of product realization in AM, while low-fidelity FEA simulation has fast computation speed yet limited capability. Hence, our objective is to improve the capability of FEA by providing an efficient data-driven model. In this research, a Gaussian process-constrained general path model is proposed to approximate the high-fidelity FEA simulation results based on low-fidelity results voxel-by-voxel. The proposed model quantifies the heterogeneous discrepancies between low- and high-fidelity FEA simulation results by incorporating the product design information (e.g., Cartesian coordinates of deposition sequence) and process design information from inputs of FEA simulation (e.g., input heat). Therefore, it enables the validation of new product and process designs based on the simulation results with the desired capability in a timely manner. The advantages of the proposed method are illustrated by FEA simulations of the fused deposition modeling (FDM) process with two levels of fidelity (i.e., low- and high-fidelity).

Meta-modeling of high-fidelity FEA simulation for efficient product and process design in additive manufacturing

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.

Fog Computing for Distributed Family Learning in Cyber-Manufacturing Modeling

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

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

\n 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.

Lening Wang

Papers

Predictive offloading in mobile-fog-cloud enabled cyber-manufacturing systems

Meta-modeling of high-fidelity FEA simulation for efficient product and process design in additive manufacturing

Fog Computing for Distributed Family Learning in Cyber-Manufacturing Modeling

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

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