Cultivating Students’ Ability of Applying Knowledge in Engineering Design Through Course Project

2011 ◽  
Author(s):  
Jining Qiu ◽  
Bo Zhang ◽  
Huimin Dong ◽  
Yuan Gao
Keyword(s):  
Engineering Design ◽  
Engineering Students ◽  
Mechanical Design ◽  
Gear Train ◽  
Design Problems ◽  
Academic Knowledge ◽  
Multi Stage ◽  
University Of Technology ◽  
Engineering Design Problems ◽  
Wind Power Generator

The ability to solve engineering design problems using academic knowledge flexibly is essential for mechanical engineering students and is also quality that employers look for. This paper introduces how students could explore and experience the process of mechanical design in the course project of Theory of Machines and Mechanisms (TMM) in Dalian University of Technology (DUT) through sharing the design process of accelerator (gear-box) in wind power generator by one representative team of students in the course project. Firstly, design requirements are set based on industrial need and the choosing of the best scheme of multi-stage gear train is conducted. Following that is the design of kinematic parameters of gears and the evaluation of selected system. Then, a possible solution to control the input speed of the generator is proposed. In the end, a survey to 279 students who participate in the course project shows the importance of course project in cultivating their ability to apply knowledge in design.

scholarly journals Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 341
Author(s):  
Bugra Alkan ◽  
Malarvizhi Kaniappan Chinnathai
Keyword(s):  
Engineering Design ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Computation Time ◽  
Population Based ◽  
Performance Comparison ◽  
Design Problems ◽  
Component Design ◽  
Engineering Design Problems ◽  
Optimisation Algorithms

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

A Blackboard Architecture to Support Case-Based Reasoning in Mechanical Design

1992 ◽  
Author(s):  
Theodore Bardsz ◽  
Ibrahim Zeid
Keyword(s):  
Problem Solving ◽  
Engineering Design ◽  
Mechanical Design ◽  
Maximum Amount ◽  
Design Solution ◽  
Case Based Reasoning ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Design Plan ◽  
Case Based

Abstract One of the most significant issues in applying case-based reasoning (CBR) to mechanical design is to integrate previously unrelated design plans towards the solution of a new design problem. The total design solution (the design plan structure) can be composed of both retrieved and dynamically generated design plans. The retrieved design plans must be mapped to fit the new design context, and the entire design plan structure must be evaluated. An architecture utilizing opportunistic problem solving in a blackboard environment is used to map and evaluate the design plan structure effectively and successfuly. The architecture has several assets when integrated into a CBR environment. First, the maximum amount of information related to the design is generated before any of the mapping problems are addressed. Second, mapping is preformed as just another action toward the evaluation of the design plan. Lastly, the architecture supports the inclusion of memory elements from the knowledge base in the design plan structure. The architecture is implemented using the GBB system. The architecture is part of a newly developed CBR System called DEJAVU. The paper describes DEJAVU and the architecture. An example is also included to illustrate the use of DEJAVU to solve engineering design problems.

scholarly journals Evolutionary Spreadsheet Solver in Optimal Engineering Design

2018 ◽  
Vol 196 ◽  
pp. 01047
Author(s):  
Ryszard Robert Gajewski
Keyword(s):  
Linear Programming ◽  
Engineering Design ◽  
Operational Research ◽  
Gear Train ◽  
Sequence Determination ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Research Problems ◽  
Linear Programming Problems ◽  
Excellent Tool

Spreadsheet solver proved to be an excellent tool to solve operational research problems modelled as linear programming problems. Majority of engineering design problems are nonlinear in nature. The paper presents ability of spreadsheet solver to solve such problems as: four bar statically determinate truss, compound gear train problem and sequence determination problem by means of evolutionary engine.

Should Teams Collaborate During Conceptual Engineering Design? An Experimental Study

2018 ◽  
Author(s):  
Joshua T. Gyory ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Problem Solving ◽  
Engineering Design ◽  
Conceptual Design ◽  
Engineering Students ◽  
Individual Performance ◽  
Design Problems ◽  
Group Problem Solving ◽  
Conceptual Engineering ◽  
Engineering Design Problems ◽  
Working Together

A commonly held presumption is that the production of a team is superior to that of individual performance. However, in certain scenarios, such as during brainstorming activities and in configuration engineering design problems, it has been shown that individuals working alone are more effective than teams working together. This research considers whether the same outcomes hold for a more open-ended scenario, in conceptual engineering design. Thus, a behavioral study is run with freshman engineering students solving a conceptual design problem working in teams or individually. Results corroborate previous findings, showing that individuals outperform teams in the quality of their design solutions. One of the primary differences between individuals and group problem solving is the fact that groups need to verbalize to communicate ideas. Consequently, this study also analyzes how verbalization, which may be one disadvantage of team problem solving, affects the performance of individuals in this context of conceptual engineering design. Individuals who verbalize throughout problem solving, however, perform similarly to those who did not. Overall, the results from this study suggest that, individuals are still better performers and teams may not always be the optimal circumstance. Moreover, verbalization does not seem to act as a cognitive barrier to problem solving, and further investigation needs to be done to diagnose the potential impediments which put teams at a disadvantage to individuals during conceptual design.

A Flexible Optimization Procedure for Mechanical Component Design Based on Genetic Adaptive Search

1998 ◽  
Vol 120 (2) ◽  
pp. 162-164 ◽  
Author(s):  
K. Deb ◽  
M. Goyal
Keyword(s):  
Engineering Design ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Optimization Procedure ◽  
Continuous Variables ◽  
Design Problems ◽  
Component Design ◽  
Engineering Design Problems ◽  
Complex Engineering ◽  
Optimum Solution

A flexible algorithm for solving nonlinear engineering design optimization problems involving zero-one, discrete, and continuous variables is presented. The algorithm restricts its search only to the permissible values of the variables, thereby reducing the search effort in converging near the optimum solution. The efficiency and ease of application of the proposed method is demonstrated by solving four different mechanical design problems chosen from the optimization literature. These results are encouraging and suggest the use of the technique to other more complex engineering design problems.

scholarly journals ∊-constraint heat transfer search (∊-HTS) algorithm for solving multi-objective engineering design problems

2017 ◽  
Vol 5 (1) ◽  
pp. 104-119 ◽  
Author(s):  
Mohamed A. Tawhid ◽  
Vimal Savsani
Keyword(s):  
Heat Transfer ◽  
Engineering Design ◽  
Pareto Front ◽  
Optimization Problems ◽  
Gear Train ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Engineering Design Problems ◽  
Numerical Experimentation

Abstract In this paper, an effective ∊-constraint heat transfer search (∊-HTS) algorithm for the multi-objective engineering design problems is presented. This algorithm is developed to solve multi-objective optimization problems by evaluating a set of single objective sub-problems. The effectiveness of the proposed algorithm is checked by implementing it on multi-objective benchmark problems that have various characteristics of Pareto front such as discrete, convex, and non-convex. This algorithm is also tested for several distinctive multi-objective engineering design problems, such as four bar truss problem, gear train problem, multi-plate disc brake design, speed reducer problem, welded beam design, and spring design problem. Moreover, the numerical experimentation shows that the proposed algorithm generates the solution to represent true Pareto front. Highlights A novel multi-objective optimization (MOO) algorithm is proposed. Proposed algorithm is presented to obtain the Pareto-optimal solutions. The multi-objective optimization algorithm compared with other work in the literature. Test performance of proposed algorithm on MOO benchmark/design engineering problems.

Standards, Codes of Practice and Expert Systems

1988 ◽  
Vol 21 (1) ◽  
pp. 5-9 ◽  
Author(s):  
E G McCluskey ◽  
S Thompson ◽  
D M G McSherry
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Engineering Design ◽  
Performance Criteria ◽  
Design Problems ◽  
Codes Of Practice ◽  
Engineering Design Problems ◽  
And Performance

Many engineering design problems require reference to standards or codes of practice to ensure that acceptable safety and performance criteria are met. Extracting relevant data from such documents can, however, be a problem for the unfamiliar user. The use of expert systems to guide the retrieval of information from standards and codes of practice is proposed as a means of alleviating this problem. Following a brief introduction to expert system techniques, a tool developed by the authors for building expert system guides to standards and codes of practice is described. The steps involved in encoding the knowledge contained in an arbitrarily chosen standard are illustrated. Finally, a typical consultation illustrates the use of the expert system guide to the standard.

Biologically Inspired Design: A Macrocognitive Account

2010 ◽  
Author(s):  
Swaroop S. Vattam ◽  
Michael Helms ◽  
Ashok K. Goel
Keyword(s):  
Biological Sciences ◽  
Information Processing ◽  
Engineering Design ◽  
Design Patterns ◽  
Information Processing Model ◽  
Biologically Inspired ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Other Information ◽  
Biologically Inspired Design

Biologically inspired engineering design is an approach to design that espouses the adaptation of functions and mechanisms in biological sciences to solve engineering design problems. We have conducted an in situ study of designers engaged in biologically inspired design. Based on this study we develop here a macrocognitive information-processing model of biologically inspired design. We also compare and contrast the model with other information-processing models of analogical design such as TRIZ, case-based design, and design patterns.

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