Studying Your Students as They Learn: A Case Study of CAD Education

2009 ◽  
Author(s):  
R. F. Hamade
Keyword(s):  
Learning Styles ◽  
Computer Aided Design ◽  
Mechanical Engineering ◽  
Mechanical Design ◽  
Learning Preferences ◽  
Actual Behavior ◽  
Computer Aided ◽  
Cad Models ◽  
Behavioral Attributes ◽  
Aided Design

Having observed mechanical engineering seniors at the American University of Beirut (AUB) go about learning computer-aided design (CAD) in a formal setting, the instructors always wondered why some students acquire CAD skills with relative ease while some others seem to struggle. For this reason, a methodical study was launched in order to address this issue. Hence, and in order to “study the students as they learn” was accomplished by following 74 mechanical engineering seniors (it took three academic years including AY 2008–09 in order to have access to this relatively large number of trainees) as they went through a semester-long formal training on a commercial computer-aided design (CAD) package (Pro/Engineer, version Wildfire). The study methodically explored the trainees’: (1) technical background, (2) behavioral attributes (willingness-to-learn), and their (3) learning preferences. Investigating the technical background included quantifying the trainees’ relevant technical competencies specifically: basic math foundation, advanced math foundation, CAD-related mathematical foundation, computer science and engineering foundation, methodologies related to CAD, graphics foundation, and mechanical design foundation. Determining the trainees’ behavioral attributes included exploring their initial attitude towards learning of CAD, perception and imagination, and gauging their actual behavior (practice and CAD skills learned) throughout the training. Trainees’ learning styles were determined according to the index of learning styles, ILS [1]. Furthermore, and in order to assess the trainees’ progress in CAD knowledge acquisition, competency tests were conducted at four intervals throughout the semester-long study (2, 4, 7, and 12 weeks). The assessment involved hands-on building of CAD test parts of comparable complexity. At the conclusion of the study, statistical methods were used to correlate the trainees’ attributes with their monitored performance. Only a fraction (17 out of a class of 74 trainees or about one in four) of the trainees were found to fit the “star CAD trainee” mold which was defined in this study as someone who is fast on the tube as well as perceptive enough to be see through the procedure of building progressively more sophisticated CAD models. A profile of this “star CAD trainee” character emerges as an individual who is technically competent and perceptive, with personal drive and positive attitude, and who possesses active, sensor, sequential and visualizing learning styles.

Profiling the Desirable CAD Trainee: Technical Background, Personality Attributes, and Learning Preferences

2009 ◽  
Vol 131 (12) ◽  
Author(s):  
Ramsey F. Hamade
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Computer Aided Design ◽  
Mechanical Design ◽  
Three Dimensional ◽  
Learning Preferences ◽  
Actual Behavior ◽  
3D Cad ◽  
Active Sensor ◽  
Cad Models

This research aims to explore some of the underlying reasoning for why some individuals acquire mechanical computer-aided design (CAD) skills with relative ease while some others seem to falter. A methodical study was performed by monitoring 74 mechanical engineering seniors (over a 3 year period) in a semester-long formal training on a commercial three-dimensional (3D) CAD package (PRO/ENGINEER, version WILDFIRE). The study methodically explored the trainees’ (1) technical background, (2) personality attributes, and (3) learning preferences. Investigating the technical background included quantifying the trainees’ following technical foundations: basic math, advanced math, CAD-related math, computer science and engineering, methodologies related to CAD, graphics, and mechanical design. Determining the trainees’ personality attributes included exploring their willingness-to-learn CAD, perception, gauging their actual behavior (practice), and CAD syntax learned throughout the training. Trainees’ learning preferences were determined according to the index of learning styles (ILS). Furthermore, and in order to assess the trainees’ progress in CAD knowledge acquisition, competency tests were conducted at four intervals throughout the semester-long study. The assessment involved hands-on modeling of CAD test parts of comparable complexity. At the conclusion of the study, statistical methods were used to correlate the trainees’ attributes with their monitored performance. Only a fraction (17 out of a class of 74 trainees or 1 in 4) of the trainees were found to fit the “star CAD trainee” mold, which is defined here as someone who is fast on the tube and perceptive enough to see through the procedure of building progressively more sophisticated CAD models. A profile of the star CAD trainee character emerges as an individual who is technically competent, perceptive, and motivated. The study also reveals these most desirable trainees to possess an active, sensor, visual, and sequential learning style.

Development of the New Drafting and Computer Aided Design Standards for Mechanical Engineering Program

2012 ◽  
Author(s):  
Sung-Hwan Joo
Keyword(s):  
Computer Aided Design ◽  
Mechanical Engineering ◽  
Early Stage ◽  
Design Standards ◽  
Engineering Program ◽  
Computer Aided ◽  
Cad Models ◽  
Level Of Understanding ◽  
Aided Design ◽  
Graduating Students

The paper provides the method to develop the drafting and CAD standards for mechanical engineering program based on the systemic approach. The drafting has been used since the early stage of engineering. Lots of drafting technique has been developed and standardized. Recently, Computer Aided Design (CAD) software has been used widely in academia and industries too. Because of these reasons, every mechanical engineering program offers Drafting and CAD courses to its students. Some programs even have their own Drafting and CAD standards. However, it is not easy to develop the Drafting and CAD standard for whole program. It needs a careful plan to develop the standards. It needs to meet the certain requirements. Those requirements are 1) It needs to meet ASME Y14/ANSI Y14 standard as much as possible, 2) Students should be able to understand the standards and apply the rules to their own drawing and CAD models, 3) Any instructors should be able to give the proper feedback to students about their drawing using the standards, 4) Graduating students should be able to adopt the standard of their company easily. To meet these requirements, some preliminary work must be done. 1) Understanding of ASME Y14 is needed, 2) Expertise of one or more CAD software packages is required, 3) Students’ level of understanding the ASME Y14 standards needs to be measured, 4) Feedback from industries is required. Each steps of development of Drafting and CAD standards are explained using real example of students work.

Identifying the Fundamental Geometric Attributes of Design: An Application of Distinctive Feature Theory

1991 ◽  
Author(s):  
John D. Chovan ◽  
Manjula B. Waldron
Keyword(s):  
Computer Aided Design ◽  
Mechanical Engineering ◽  
Mechanical Design ◽  
Verbal Protocols ◽  
Distinctive Features ◽  
Human Machine Interfaces ◽  
Computer Aided ◽  
Engineering Drawings ◽  
Aided Design ◽  
Feature Theory

Abstract The identification of a basal set of geometric attributes used by designers to describe mechanical engineering design can provide useful information on the manner in which designers communicate information about mechanical design. In this paper, geometric attributes were identified from descriptions provided by five mechanical designers while they read and analyzed thirteen mechanical engineering drawings of varying complexity. From the verbal protocols, the geometric attributes were identified and then subject to a cluster analysis. A set of fifteen geometric attributes were identified as distinctive features since they were minimally sufficient to cluster the set of 61 design features contained in the drawings. These distinctive features provide insights into how designers reason about mechanical engineering drawings, which may be useful when designing human-machine interfaces for computer-aided design.

scholarly journals Automatized Evaluation of Students’ CAD Models

2021 ◽  
Vol 11 (4) ◽  
pp. 145
Author(s):  
Nenad Bojcetic ◽  
Filip Valjak ◽  
Dragan Zezelj ◽  
Tomislav Martinec
Keyword(s):  
Computer Aided Design ◽  
Three Dimensional ◽  
Computer Application ◽  
Test Cases ◽  
Cad Model ◽  
Computer Solution ◽  
3D Cad ◽  
Computer Aided ◽  
Cad Models ◽  
Aided Design

The article describes an attempt to address the automatized evaluation of student three-dimensional (3D) computer-aided design (CAD) models. The driving idea was conceptualized under the restraints of the COVID pandemic, driven by the problem of evaluating a large number of student 3D CAD models. The described computer solution can be implemented using any CAD computer application that supports customization. Test cases showed that the proposed solution was valid and could be used to evaluate many students’ 3D CAD models. The computer solution can also be used to help students to better understand how to create a 3D CAD model, thereby complying with the requirements of particular teachers.

scholarly journals Are Two Heads Better Than One For Computer-Aided Design?

2021 ◽  
pp. 1-38
Author(s):  
Vrushank Phadnis ◽  
Hamza Arshad ◽  
David Wallace ◽  
Alison Olechowski
Keyword(s):  
Computer Aided Design ◽  
Mechanical Design ◽  
Major Effect ◽  
Pair Programming ◽  
Digital Modeling ◽  
Computer Aided ◽  
Model Instance ◽  
Parallel Mode ◽  
Aided Design ◽  
Pair Work

Abstract With the availability of cloud-based software, ubiquitous internet and advanced digital modeling capabilities, a new potential has emerged to design physical products with methods previously embraced by the software engineering community. One such example is pair programming, where two coders work together synchronously to develop one piece of code. Pair programming has been shown to lead to higher quality code and designer satisfaction. Cutting-edge collaborative Computer-aided Design (CAD) technology affords the possibility to apply synchronous collaborative access in mechanical design. We test the generalizability of findings from the pair programming literature to the same dyadic configuration of work in CAD, which we call pair CAD. We performed human subject experiments with 60 participants to test three working styles: individuals working by themselves, pairs sharing control of one model instance and input, and pairs able to edit the same model simultaneously from two inputs. We compare the working styles on speed and quality, and propose mechanisms for our observations via interpretation of patterns of communication, satisfaction, and user cursor activity. We find that on a per-person basis, individuals were faster than pairs due to coordination and overhead inefficiencies. We find that pair work, when done with a single shared input, but not in a parallel mode, leads to higher quality models. We conclude that it is not Industry 4.0 technologies alone that influence designer output; choices regarding work process have a major effect on design outcomes, and we can tailor our process to suit project requirements.

A New Family of Bricard-Derived Deployable Mechanisms

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Hailin Huang ◽  
Bing Li ◽  
Jianyang Zhu ◽  
Xiaozhi Qi
Keyword(s):  
Large Volume ◽  
Computer Aided Design ◽  
Degree Of Freedom ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
New Family ◽  
Revolute Joints ◽  
Computer Aided ◽  
Cad Models ◽  
Aided Design

This paper proposes a new family of single degree of freedom (DOF) deployable mechanisms derived from the threefold-symmetric deployable Bricard mechanism. The mobility and geometry of original threefold-symmetric deployable Bricard mechanism is first described, from the mobility characterstic of this mechanism, we show that three alternate revolute joints can be replaced by a class of single DOF deployable mechanisms without changing the single mobility characteristic of the resultant mechanisms, therefore leading to a new family of Bricard-derived deployable mechanisms. The computer-aided design (CAD) models are used to demonstrate these derived novel mechanisms. All these mechanisms can be used as the basic modules for constructing large volume deployable mechanisms.

Introductory remarks

1971 ◽  
Vol 321 (1545) ◽  
pp. 145-146
Keyword(s):  
Engineering Design ◽  
Computer Aided Design ◽  
Mechanical Engineering ◽  
Future Research ◽  
Design Work ◽  
Computer Aided ◽  
Hidden Line ◽  
Multi Access ◽  
Aided Design ◽  
Design And Manufacture

From time to time the Royal Society organizes meetings for the discussion of some new development in engineering and applied science. It seemed possible to the organizers of this meeting that it would be profitable to bring together workers in industry and in the universities to discuss some aspect of computer-aided design. As you will see we have chosen the application of computer aids to mechanical engineering design and manufacture. This restriction to mechanical engineering was deliberate, partly because the application of computer aids to mechanical engineering design is somewhat behind similar activities in electrical and civil engineering. Another reason is that the development of such applications has reached a particularly interesting stage, and it is now perhaps appropriate to review progress and to discuss the directions in which future research should proceed. Although some examples of computer-aided design in mechanical engineering can be found from the earliest days of computing, the development really started in the late fifties with early experiments in the use of graphic displays and with the introduction of multi-access computing. Some may date the beginning of the developments which we are going to discuss today, from the work at M. I. T. on automated programmed drawing started in 1958. This has led to a concentration of effort on graphics and computer-aided drafting. Much research has been done on the mathematical description of curves, surfaces and volumes in a form suitable for engineering design. Work has been done on the automatic dimensioning of drawings, hidden line removal, the prob­lems of lofting, etc.

Orthogonal Distance Fields Representation for Machine-Learning Based Manufacturability Analysis

2020 ◽  
Author(s):  
Aditya Balu ◽  
Sambit Ghadai ◽  
Soumik Sarkar ◽  
Adarsh Krishnamurthy
Keyword(s):  
Machine Learning ◽  
Computer Aided Design ◽  
Boundary Representation ◽  
Engineering Product ◽  
Distance Fields ◽  
Computer Aided ◽  
Cad Models ◽  
Product Design Process ◽  
3D Cnn ◽  
Aided Design

Abstract Computer-aided Design for Manufacturing (DFM) systems play an essential role in reducing the time taken for product development by providing manufacturability feedback to the designer before the manufacturing phase. Traditionally, DFM rules are hand-crafted and used to accelerate the engineering product design process by integrating manufacturability analysis during design. Recently, the feasibility of using a machine learning-based DFM tool in intelligently applying the DFM rules have been studied. These tools use a voxelized representation of the design and then use a 3D-Convolutional Neural Network (3D-CNN), to provide manufacturability feedback. Although these frameworks work effectively, there are some limitations to the voxelized representation of the design. In this paper, we introduce a new representation of the computer-aided design (CAD) model using orthogonal distance fields (ODF). We provide a GPU-accelerated algorithm to convert standard boundary representation (B-rep) CAD models into ODF representation. Using the ODF representation, we build a machine learning framework, similar to earlier approaches, to create a machine learning-based DFM system to provide manufacturability feedback. As proof of concept, we apply this framework to assess the manufacturability of drilled holes. The framework has an accuracy of more than 84% correctly classifying the manufacturable and non-manufacturable models using the new representation.

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