Comparing Strategies for Topologic and Parametric Rule Application in Automated Computational Design Synthesis1

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Corinna Königseder ◽  
Kristina Shea
Keyword(s):  
Search Algorithm ◽  
Computational Design ◽  
Search Process ◽  
Graph Grammars ◽  
Design Synthesis ◽  
Design Task ◽  
Rule Application ◽  
Bicycle Frame ◽  
Computational Design Synthesis

Graph grammars are used for computational design synthesis (CDS) in which engineering knowledge is formalized using graphs to represent designs and rules that describe their transformation. Most engineering tasks require both topologic and parametric rules to generate designs. The research presented in this paper compares different strategies for rule application to combine topologic and parametric rules during automated design synthesis driven by a search process. The presented strategies are compared considering quantity and quality of the generated designs. The effect of the strategies, the selected search algorithm, and the initial design, from which the synthesis is started, are analyzed for two case studies: gearbox synthesis and bicycle frame synthesis. Results show that the effect of the strategy is dependent on the design task. Recommendations are given on which strategies to use for which design task.

A Spatial Grammar Method for the Computational Design Synthesis of Virtual Soft Locomotion Robots

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Merel van Diepen ◽  
Kristina Shea
Keyword(s):  
Degrees Of Freedom ◽  
Computational Design ◽  
Simulation Method ◽  
Search Space ◽  
Search Process ◽  
Design Synthesis ◽  
Spatial Grammar ◽  
Three Stages ◽  
Computational Design Synthesis ◽  
Simulation Feedback

Soft locomotion robots are intrinsically compliant and have a large number of degrees of freedom. They lack rigid components that provide them with higher flexibility, and they have no joints that need protection from liquids or dirt. However, the hand-design of soft robots is often a lengthy trail-and-error process. This work presents the computational design of virtual, soft locomotion robots using an approach that integrates simulation feedback. The computational approach consists of three stages: (1) generation, (2) evaluation through simulation, and (3) optimization. Here, designs are generated using a spatial grammar to explicitly guide the type of solutions generated and exclude infeasible designs. The soft material simulation method developed and integrated is stable and sufficiently fast for use in a highly iterative simulated annealing search process. The resulting virtual designs exhibit a large variety of expected and unexpected gaits, thus demonstrating the method capabilities. Finally, the optimization results and the spatial grammar are analyzed to understand and map the challenges of the problem and the search space.

A Framework for Computational Design Synthesis Based on Graph-Grammars and Function-Behavior-Structure

2009 ◽  
Author(s):  
Bergen Helms ◽  
Kristina Shea ◽  
Frank Hoisl
Keyword(s):  
Graph Transformation ◽  
Computational Design ◽  
Synthesis Methods ◽  
Graph Grammars ◽  
Levels Of Abstraction ◽  
Product Model ◽  
Product Representation ◽  
Design Synthesis ◽  
Simulation Performance ◽  
Computational Design Synthesis

Computational design synthesis supports the knowledge-intensive process of developing new products. However, most approaches to date are often limited to a narrow domain and viewpoint of a synthesis task. The framework introduced in this paper aims to respond to the need for a method that integrates a richer product representation for computational synthesis within a framework that includes simulation, performance evaluation, and search. A computational and parameterized product model is presented that combines the Function-Behavior-Structure levels of abstraction. Graph-grammars are then used to create a formal definition of vocabulary and valid graph transformation rules. This approach offers the possibility to harness the large knowledge source of design catalogues in order to formulate vocabulary in a viewpoint-independent and thus, flexible way. In addition to manually entered and problem-specific rules, a class of generic rules is introduced that is instantiated computationally based on the defined vocabulary and has the advantage of being problem independent and re-usable. Finally, validation of the method is given through the synthesis of electric power-trains. This research goes beyond prior work in the field as it allows for synthesis and reasoning on different layers of abstraction, including function, behavior and structure, and introduces methods to alleviate the encapsulation of engineering knowledge for synthesis methods.

Automated Graph Grammar Generation for Engineering Design With Frequent Pattern Mining

2017 ◽  
Author(s):  
Shraddha Sangelkar ◽  
Daniel A. McAdams
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Computational Design ◽  
Graph Grammar ◽  
Frequent Pattern ◽  
Graph Grammars ◽  
Design Synthesis ◽  
Graph Data ◽  
Grammar Rules ◽  
Computational Design Synthesis

Graph grammars, a technique for formulating new graphs based on a set of rules, is a very powerful tool for computational design synthesis. It is particularly suitable for discrete categorical data where principal component analysis is generally not applicable. Furthermore, this technique utilizes three different programs in conjunction with a design repository, which is opposed to traditional methods that require experts to empirically derive graph grammars. This technique can be separated into three steps. These steps are the creation of the input, graph data mining, and interpretation of the output with the intention of these steps being to automate or assist an expert with the process of extracting engineering graph grammars. Graph grammars that can then serve as guidelines during concept generation. The results of this paper show that this technique is very applicable to computational design synthesis by testing only a small number of products and still producing tangible results that coincide with empirically derived graphs. Fifty electromechanical products from the design repository are used in this study. When comparing, the machine generated grammar rules with expert derived grammar rules, it can be seen that only 14% cannot be developed, 58% cannot be mined with the current setup and 28% were mined with the current set up. However, it is important to keep in mind a few considerations. Specifically, the technique does not replace the expert. Instead, the technique acts as more of an aid than a replacement. Also, while this technique has great potential in regards to computational design synthesis, it is limited to the products in the design repository and the current implementation of the aforementioned programs. Despite these minor considerations, this work proposes application of graph data mining to derive engineering grammars.

Visualizing Relations Between Grammar Rules, Objectives, and Search Space Exploration in Grammar-Based Computational Design Synthesis1

2016 ◽  
Vol 138 (10) ◽  
Author(s):  
Corinna Königseder ◽  
Kristina Shea
Keyword(s):  
Method Development ◽  
Search Algorithm ◽  
Computational Design ◽  
Search Space ◽  
Synthesis Process ◽  
Design Synthesis ◽  
Grammar Rules ◽  
Computational Design Synthesis ◽  
Design Characteristics ◽  
Selection Of

Design grammars have been successfully applied in numerous engineering disciplines, e.g., in electrical engineering, architecture, and mechanical engineering. A successful application of design grammars in computational design synthesis (CDS) requires (a) meaningful representation of designs and the design task at hand, (b) careful formulation of grammar rules to synthesize new designs, (c) problem-specific design evaluation, and (d) selection of an appropriate algorithm to guide the synthesis process. Determining these different components of a CDS method requires not only a detailed understanding of each individual part but also of the interdependencies between them. In this paper, a new method is presented to support both CDS method development and application. The method analyzes the designs generated during the synthesis process and visualizes how the design space is explored with respect to design characteristics and objectives. The search algorithm as well as the grammar rules are analyzed with this approach. Two case studies, the synthesis of gearboxes and of bicycle frames, demonstrate how the method can be used to analyze the different components of CDS methods. The presented research can analyze the interplay between grammar rules and the search process during method development.

scholarly journals Closed Shell Iron(IV) Oxo Complex with an Fe–O Triple Bond: Computational Design, Synthesis, and Reactivity

2020 ◽  
Vol 59 (51) ◽  
pp. 23137-23144
Author(s):  
Erik Andris ◽  
Koen Segers ◽  
Jaya Mehara ◽  
Lubomír Rulíšek ◽  
Jana Roithová
Keyword(s):  
Triple Bond ◽  
Computational Design ◽  
Closed Shell ◽  
Design Synthesis ◽  
Computational Design Synthesis

Computational design, synthesis and utilization of a magnetic molecularly imprinted polymer on graphene oxide nanosheets for highly selective extraction and determination of buprenorphine in biological fluids and tablets

2018 ◽  
Vol 10 (43) ◽  
pp. 5214-5226 ◽  
Author(s):  
Farideh Ganjavi ◽  
Mehdi Ansari ◽  
Maryam Kazemipour ◽  
Leila Zeidabadinejad
Keyword(s):  
Biological Fluids ◽  
Computational Design ◽  
Selective Extraction ◽  
Graphene Oxide Nanosheets ◽  
Imprinted Polymer ◽  
Design Synthesis ◽  
Molecularly Imprinted ◽  
Plasma And Urine Samples ◽  
Computational Design Synthesis

A magnetic MIP for the selective extraction of buprenorphine (BUP) from real plasma and urine samples and tablets based on computational design as a novel procedure has been developed.

Toward an automated approach to the design of sheet metal components

2003 ◽  
Vol 17 (3) ◽  
pp. 187-204 ◽  
Author(s):  
ADITYA SOMAN ◽  
SWAPNIL PADHYE ◽  
MATTHEW I. CAMPBELL
Keyword(s):  
Sheet Metal ◽  
User Interaction ◽  
Computational Design ◽  
Design Synthesis ◽  
Manufacturing Operations ◽  
Grammar Rules ◽  
Computational Design Synthesis ◽  
Representation Scheme ◽  
Sheet Metal Component ◽  
Synthesis Approach

The design of sheet metal components is perhaps one of the more challenging concurrent activities for design and manufacturing engineers. To aid this design process, a method is developed to encapsulate the constraints of sheet metal that make designing such components a tedious and iterative procedure. This project involves the implementation and testing of a geometric representation scheme for building feasible sheet metal components through the use of 17 grammar rules that capture manufacturing operations like cutting and bending. The implemented system has benefits both as a user interaction tool and as the basis for a computational design synthesis approach for designing sheet metal components. An example of a constructed sheet metal component is shown along with the method for invoking the sheet metal grammar to create this component.

Study on Free Search for Combinatorial Optimization Problem

2013 ◽  
Vol 411-414 ◽  
pp. 1904-1910
Author(s):  
Kai Zhong Jiang ◽  
Tian Bo Wang ◽  
Zhong Tuan Zheng ◽  
Yu Zhou
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Process ◽  
Standard Data ◽  
Combinatorial Optimization Problems ◽  
Free Search

An algorithm based on free search is proposed for the combinatorial optimization problems. In this algorithm, a feasible solution is converted into a full permutation of all the elements and a transformation of one solution into another solution can be interpreted the transformation of one permutation into another permutation. Then, the algorithm is combined with intersection elimination. The discrete free search algorithm greatly improves the convergence rate of the search process and enhances the quality of the results. The experiment results on TSP standard data show that the performance of the proposed algorithm is increased by about 2.7% than that of the genetic algorithm.

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