Computational thinking embedded in engineering design: capturing computational thinking of children in an informal engineering design activity

2020 ◽  
Author(s):  
Hoda Ehsan ◽  
Abeera P. Rehmat ◽  
Monica E. Cardella
Keyword(s):  
Engineering Design ◽  
Computational Thinking ◽  
Design Activity

The Origins of Engineering Design

1993 ◽  
Author(s):  
Andrew D. Dimarogonas
Keyword(s):  
Engineering Design ◽  
19Th Century ◽  
Scientific Knowledge ◽  
Industrial Revolution ◽  
Design Activity ◽  
Ancient Greek ◽  
Modern Engineering ◽  
Hellenistic World ◽  
Systematic Development ◽  
The 19Th Century

Abstract Engineering is distinguished from craft or invention by systematic development and use of intelligence and scientific knowledge. Elements of engineering design can be found in the great Potamic civilizations but systematic engineering design activity started in the ancient Greek and Hellenistic world and matured under the Romans. The renaissance and the industrial revolution revived Engineering and modern engineering design was eventually defined during the 19th Century.

A Preliminary Study of Novice and Expert Users’ Decision-Making Procedures During Visual Trade Space Exploration

2009 ◽  
Author(s):  
David Wolf ◽  
Timothy W. Simpson ◽  
Xiaolong Luke Zhang
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Data Visualization ◽  
Space Exploration ◽  
Computational Thinking ◽  
Test Group ◽  
Design Decision ◽  
Ongoing Research ◽  
Trade Space Exploration ◽  
Insight Into

Thanks to recent advances in computing power and speed, designers can now generate a wealth of data on demand to support engineering design decision-making. Unfortunately, while the ability to generate and store new data continues to grow, methods and tools to support multi-dimensional data exploration have evolved at a much slower pace. Moreover, current methods and tools are often ill-equipped at accommodating evolving knowledge sources and expert-driven exploration that is being enabled by computational thinking. In this paper, we discuss ongoing research that seeks to transform decades-old decision-making paradigms rooted in operations research by considering how to effectively convert data into knowledge that enhances decision-making and leads to better designs. Specifically, we address decision-making within the area of trade space exploration by conducting human-computer interaction studies using multi-dimensional data visualization software that we have been developing. We first discuss a Pilot Study that was conducted to gain insight into expected differences between novice and expert decision-makers using a small test group. We then present the results of two Preliminary Experiments designed to gain insight into procedural differences in how novices and experts use multi-dimensional data visualization and exploration tools and to measure their ability to use these tools effectively when solving an engineering design problem. This work supports our goal of developing training protocols that support efficient and effective trade space exploration.

A Multi Autonomic Objects Flexible Workflow and its Knowledge Acquisition Supporting Engineering Design Process

2010 ◽  
Vol 97-101 ◽  
pp. 3341-3344
Author(s):  
Dong Bo Wang ◽  
Xiu Tian Yan ◽  
Ning Sheng Guo ◽  
Tao Li
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Literature Review ◽  
Knowledge Acquisition ◽  
Engineering Design ◽  
Design Process ◽  
Design Activity ◽  
Learning Method ◽  
Matlab Simulation ◽  
Engineering Design Process

In order to support the dynamic and creative Engineering Design Process (EDP) comprehensively, after a detailed literature review, a multi autonomic objects (AO) flexible workflow is applied into the supporting and management of EDP, its support for decision making, EDP evolution and design activity granularity is explained, finally and most importantly, a genetic algorithm-based AO knowledge learning method is proposed, the algorithm is demonstrated by a MATLAB simulation that it can satisfy the knowledge acquisition in EDP satisfactorily.

Applications and prospects for AI in mechanical engineering design

1990 ◽  
Vol 5 (3) ◽  
pp. 167-179 ◽  
Author(s):  
Ian M. Carter
Keyword(s):  
Engineering Design ◽  
Conceptual Design ◽  
Mechanical Engineering ◽  
Subject Area ◽  
Computer Support ◽  
Design Activity ◽  
Engineering Systems ◽  
Design Systems ◽  
Broad Subject ◽  
Future Topics

AbstractMechanical engineering design is a broad subject area covering many topics and bas influences upon many other engineering disciplines and activities. Computer support for mechanical engineering design activity has been in draughting Systems and analysis packages, but there has been little in conceptual design assistance. This paper presents a number of areas of work in which AI techniques and developments are being used, sometimes in conjunction with traditional methods, to improve the support of design. The approaches to design and design Systems are covered, along with some techniques that are used. Specifie design Systems illustrate progress, and integration issues and simultaneous engineering Systems indicate the way research is moving. Finally, discussion of the trends and future topics indicates where and how effort may be applied in the future.

The Maintenance and Handling of Constraints in Machine Design

2009 ◽  
Author(s):  
Jason Matthews ◽  
Lian Ding ◽  
Jonathan Feldman ◽  
Glen Mullineux
Keyword(s):  
Engineering Design ◽  
Machine Design ◽  
Design Activity ◽  
Industrial Case Study ◽  
Design Constraints ◽  
Engineering Design Process ◽  
Design Variation ◽  
Single Objective ◽  
Machinery Design

The engineering design process can be considered to be constraint oriented. It involves the identification, negotiation and resolution of an evolving set of constraints. The nature of engineering design is that such problems are rarely as simple as satisfying a single objective with all the design variation continuous and unbounded. Previous research has shown that as the design evolves, the designer can miss or overlook some of these constraints. To overcome this, the paper presents a supportive approach which allows the designer to annotate the initial CAD drawn models with the design constraints. These constraints are then maintained with the model as it evolves, this presents the opportunity to refine the constraints when the design activity requires. The approach has been created to support manufacturing machinery design and is demonstrated with an industrial case study.

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