Studying design abduction in the context of novelty

Design abduction has been studied over the last several decades in order to increase our understanding in design reasoning. Yet, there is still considerable confusion and ambiguity regarding this topic. Some scholars contend that all regressive inferences in design — and design is mostly done by such backwards or regressive reasoning — are in fact abductions. Others focus on formal syllogistic forms in their attempt to clarify abduction. In contrast, we argue here that a defining characteristic of abduction is the production of, or the potential to produce, novel outcomes. Novelty is shown to be relative and depend mostly on what is known to the “reasoner” at the time of making the inference. Novelty is also shown to not necessarily be part of the direct outcome of an abductive inference; but rather, an attribute of an abductive design strategy that is intended to produce a new idea

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented

Hybrid Bulk Metal Components

In recent years, the requirements for technical components have steadily been increasing. This development is intensified by the desire for products with a lower weight, smaller size, and extended functionality, but also with a higher resistance against specific stresses. Mono-material components, which are produced by established processes, feature limited properties according to their respective material characteristics. Thus, a significant increase in production quality and efficiency can only be reached by combining different materials in a hybrid metal component. In this way, components with tailored properties can be manufactured that meet the locally varying requirements. Through the local use of different materials within a component, for example, the weight or the use of expensive alloying elements can be reduced. The aim of this Special Issue is to cover the recent progress and new developments regarding all aspects of hybrid bulk metal components. This includes fundamental questions regarding the joining, forming, finishing, simulation, and testing of hybrid metal parts

Advanced Rotorcraft Transmission (ART) program

Work performed by the McDonnell Douglas Helicopter Company and Lucas Western, Inc. within the U.S. Army/NASA Advanced Rotorcraft Transmission (ART) Program is summarized. The design of a 5000 horsepower transmission for a next generation advanced attack helicopter is described. Government goals for the program were to define technology and detail design the ART to meet, as a minimum, a weight reduction of 25 percent, an internal noise reduction of 10 dB plus a mean-time-between-removal (MTBR) of 5000 hours compared to a state-of-the-art baseline transmission. The split-torque transmission developed using face gears achieved a 40 percent weight reduction, a 9.6 dB noise reduction and a 5270 hour MTBR in meeting or exceeding the above goals. Aircraft mission performance and cost improvements resulting from installation of the ART would include a 17 to 22 percent improvement in loss-exchange ratio during combat, a 22 percent improvement in mean-time-between-failure, a transmission acquisition cost savings of 23 percent of $165K, per unit, and an average transmission direct operating cost savings of 33 percent, or$24K per flight hour. Face gear tests performed successfully at NASA Lewis are summarized. Also, program results of advanced material tooth scoring tests, single tooth bending tests, Charpy impact energy tests, compact tension fracture toughness tests and tensile strength tests are summarized