**Contents**


#### **Samyeon Kim and Seung Ki Moon**


## **About the Editors**

**Qi Zhou** Ph.D., is an Associate Professor at the School of Aerospace Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China. His research expertise areas include multi-fidelity surrogate model, airticial intelligence, and design optimization under uncertainty. He was awarded the National Defense Innovation Award, and obtained the Outstanding Young Researcher project from HUST. He was the session chair of the International Conference on System Modeling and Optimization committee. He has published over 80 peer-reviewed international journal and conference papers.

**Seung-Kyum Choi** began at Georgia Tech in Fall 2006. Dr. Choi's research interests include structural reliability, probabilistic mechanics, statistical approaches to the design of structural systems, multidisciplinary design optimization, and information engineering for complex engineered systems. He conducted a challenging research effort on the uncertainty quantification for the analytical certification of aerospace and underwater vehicles. The success of this challenging effort includes the development of state-of-the-art numerical techniques in statistical methods, and structural analysis methods with innovative uncertainty quantification techniques. Dr. Choi's current research focus is on the development of robust simulation and decision support tools to assist the management of complex engineered systems involving material structures design and multifunctional products. He is the principal author of a graduate-level textbook on the topics of probabilistic mechanics and reliability-based design optimization (Reliability-based Structural Design, Springer, 2007). He is an invited reviewer of numerous journals, including *Structural and Multidisciplinary Optimization*, *Probabilistic Engineering Mechanics*, *ASME JMD*, and *AIAA* journal. In addition, he has served as a Guest Editor for highly qualified journals, including *Journal of Engineering Design and Journal of Electronic Materials*.

**Recep M. Gorguluarslan** Ph.D., is an Assistant Professor at TOBB University of Economics and Technology in Turkey. He started his PhD in Mechanical Engineering at Georgia Institute of Technology in August 2011. He completed his PhD in December 2016. His PhD study was about developing a multi-level upscaling and validation approach for uncertainty quantification in additively manufactured lattice structures. In 2017, he worked at the Georgia Institute of Technology as a post-doctorate researcher in the project of developing an automated railway design approach of seats that can climb stairs, supported by ThsyennKrupp.

## *Editorial* **Editorial for the Special Issue: Computer-Aided Manufacturing and Design**

**Qi Zhou 1, Seung-Kyum Choi 2,\* and Recep M. Gorguluarslan <sup>3</sup>**


Received: 10 July 2020; Accepted: 9 August 2020; Published: 14 August 2020

#### **1. Introduction**

Recent advancements in computer technology have allowed designers to have direct control over the production process through the help of computer-based tools, creating the possibility of completely integrated design and manufacturing processes. Over the last few decades, artificial intelligence (AI) techniques such as machine learning and deep learning have been topics of interest in computer-based design and manufacturing research fields. This Special Issue aims to collect novel articles covering artificial intelligence-based design, manufacturing, and data-driven design.

#### **2. Content**

This Special Issue comprises 10 selected papers that demonstrate the successful application of computer-based tools in design and manufacturing research fields.

Among these works, three papers focus on engineering optimization by combining computer-aided engineering (CAE) models with intelligent optimization algorithms. Specifically, in Reference [1], the finite element analysis (FEA) model for simulating the filling and packing stage was combined with a gradient-based algorithm and robust genetic algorithm to design the conformal cooling channels. In Reference [2], the hydraulic optimization of automotive electronic pumps was finished by combining the computational fluid dynamics (CFD) technology with a multi-island genetic algorithm. In Reference [3], the design optimization of an underwater vehicle base was successfully performed by integrating the FEA simulation-based design with the Kriging surrogate model and genetic algorithm.

Six of these papers focus on data-driven design and optimization. Specifically, in Reference [4], a stretchable micro-strip patch MSP (micro-strip patch) antenna-based strain sensor was optimized by a proposed design framework, which exploits dimensional reduction, machine learning-based surrogate modeling, structural optimization, and reliability assessment approaches. In Reference [5], a field repair kit for a complex product-service system was optimized in terms of the field inventory kit cost, while satisfying the availability requirement set by contract with the customer. In Reference [6], a methodology of a product image design integrated decision system based on Kansei engineering theory was developed. In Reference [7], to improve the quality of the large-scale assembly, an assemblability analysis and optimization method based on the coordination space model was developed. In Reference [8], a region-based convolutional neural network was constructed to recognize graphical symbols in piping and instrument diagrams. In Reference [9], the design specifications for a multifunctional console of Jangbogo class submarines that can accommodate, as much as possible, the anthropometric dimensions of Korean males were optimized.

The last paper [10] focuses on computer-based design for additive manufacturing. Specifically, the authors developed a design method to consolidate parts for considering maintenance and product recovery at the end-of-life stage.

#### **3. Results**

AI techniques shine in many areas, including the computer-based design and manufacturing research fields. The 10 papers described here show some successful applications of machine learning and intelligent optimization algorithms in different cases. It is believed that the collection of 10 papers in this Special Issue will be beneficial to readers who have interests in applying AI techniques in the computer-based design and manufacturing domain.

**Author Contributions:** All the Guest Editors contribute equally to the editorial paper of this Special Issue. All authors have read and agreed to the published version of the manuscript.

**Funding:** This editorial paper has been supported by the National Natural Science Foundation of China (NSFC) under Grant No. 51805179 and the National Defense Innovation Program under Grant No. 18-163-00-TS-004-033-01.

**Acknowledgments:** The Guest Editors sincerely thank all the authors for their excellent contributions to this Special Issue. Furthermore, we would like to thank all the anonymous reviewers for their selfless help in providing valuable comments and suggestions. Finally, the Guest Editors sincerely appreciate Lucia Li, the contact editor of this Special Issue, for her time and efforts.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*
