scholarly journals A Knowledge-Based Method for Innovative Design for Additive Manufacturing Supported by Modular Ontologies

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Thomas J. Hagedorn ◽  
Sundar Krishnamurty ◽  
Ian R. Grosse
Keyword(s):  
Additive Manufacturing ◽  
Business Case ◽  
Knowledge Bases ◽  
Product Development Process ◽  
Design Teams ◽  
Design Problems ◽  
Innovative Design ◽  
Knowledge Based ◽  
Formal Ontologies ◽  
Innovative Capabilities

Additive manufacturing (AM) offers significant opportunities for product innovation in many fields provided that designers are able to recognize the potential values of AM in a given product development process. However, this may be challenging for design teams without substantial experience with the technology. Design inspiration based on past successful applications of AM may facilitate application of AM even in relatively inexperienced teams. While designs for additive manufacturing (DFAM) methods have experimented with reuse of past knowledge, they may not be sufficient to fully realize AM's innovative potential. In many instances, relevant knowledge may be hard to find, lack context, or simply unavailable. This design information is also typically divorced from the underlying logic of a products' business case. In this paper, we present a knowledge based method for AM design ideation as well as the development of a suite of modular, highly formal ontologies to capture information about innovative uses of AM. This underlying information model, the innovative capabilities of additive manufacturing (ICAM) ontology, aims to facilitate innovative use of AM by connecting a repository of a business and technical knowledge relating to past AM products with a collection of knowledge bases detailing the capabilities of various AM processes and machines. Two case studies are used to explore how this linked knowledge can be queried in the context of a new design problem to identify highly relevant examples of existing products that leveraged AM capabilities to solve similar design problems.

Cyberphysical Design Automation Framework for Knowledge-based Engineering

2013 ◽  
Vol 1 (1) ◽  
pp. 158-178
Author(s):  
Urcun John Tanik
Keyword(s):  
System Design ◽  
Design Process ◽  
Design Automation ◽  
Design Theory ◽  
Knowledge Bases ◽  
Knowledge Based ◽  
Knowledge Based Engineering ◽  
Cyberphysical System ◽  
Automation Framework

Cyberphysical system design automation utilizing knowledge based engineering techniques with globally networked knowledge bases can tremendously improve the design process for emerging systems. Our goal is to develop a comprehensive architectural framework to improve the design process for cyberphysical systems (CPS) and implement a case study with Axiomatic Design Solutions Inc. to develop next generation toolsets utilizing knowledge-based engineering (KBE) systems adapted to multiple domains in the field of CPS design automation. The Cyberphysical System Design Automation Framework (CPSDAF) will be based on advances in CPS design theory based on current research and knowledge collected from global sources automatically via Semantic Web Services. A case study utilizing STEM students is discussed.

Design for Additive Manufacturing Inspired by TRIZ

2018 ◽  
Author(s):  
Jared Gross ◽  
Kijung Park ◽  
Gül E. Okudan Kremer
Keyword(s):  
Additive Manufacturing ◽  
Design Decision ◽  
Design For Additive Manufacturing ◽  
Specific Design ◽  
Innovative Design ◽  
Creative Solutions ◽  
Design Solutions ◽  
Manufacturing Techniques ◽  
Effective Design ◽  
Matrix Case

With the rise in popularity of additive manufacturing (AM), relevant design methodologies have become necessary for designers to reap the full benefits from this technology. TRIZ is a problem-solving tool developed to assist with innovative and creative solutions. This paper aims to create a new TRIZ matrix specifically developed for designers using additive manufacturing. The TRIZ matrix offers designers general innovative design solutions to improve specific features of a design while not sacrificing the effectiveness of other features. The proposed matrix can help effective design decision making for additive manufacturing in an early design process as well as a redesign process. Also, a design for additive manufacturing (DfAM) worksheet is provided to enable users to easily find specific design solutions for certain additive manufacturing techniques based on the general solutions derived by the TRIZ matrix. To illustrate the potential of this AM specific TRIZ matrix, case studies are presented.

Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hossein Mokhtarian ◽  
Hesam Jafarian ◽  
Saoussen Dimassi ◽  
Shahriar Bakrani-Balani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Expert Knowledge ◽  
Fused Deposition ◽  
Knowledge Based ◽  
Deposition Modeling ◽  
Artificial Neural

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

scholarly journals Supporting Creative Mechanical Design

1994 ◽  
Author(s):  
Kun Sun ◽  
Boi Faltings
Keyword(s):  
Mechanical Design ◽  
Knowledge Bases ◽  
Creative Design ◽  
Knowledge Based Systems ◽  
Cad Systems ◽  
Knowledge Based ◽  
Logical Language ◽  
Promising Application ◽  
Qualitative Physics ◽  
And Function

Abstract Knowledge-based CAD systems limit designers’ creativity by constraining them to work with the prototypes provided by the systems’ knowledge bases. We investigate knowledge-based CAD systems capable of supporting creative designs in the example domain of elementary mechanisms. We present a technique based on qualitative explanations which allows a designer to extend the knowledge base by demonstrating a structure which implements a function in a creative way. Structure is defined as the geometry of the parts, and function using a general logical language based on qualitative physics. We argue that the technique can accommodate any creative design in the example domain, and we demonstrate the technique using an example of a creative design. The use of qualitative physics as a tool for extensible knowledge-based systems points out a new and promising application area for qualitative physics.

Optimization in a Design System for Complex Products

1992 ◽  
Author(s):  
Johan Malmqvist
Keyword(s):  
Parametric Design ◽  
System Optimization ◽  
Optimization Method ◽  
Design System ◽  
Design Problems ◽  
Design And Optimization ◽  
Complex Products ◽  
Knowledge Based ◽  
Product Models ◽  
Use Of Knowledge

Abstract This paper describes a system for parametric design and optimization of complex products. In the system, the use of knowledge-based and mathematical programming methods is combined. The motivation is that while knowledge-based methods are well suited for modeling products, they are insufficient when dealing with design problems that can be given an optimization formulation. This weakness was approached by including the information necessary for stating an optimization problem in the product models. A system optimization method can then be applied. The system also performs sensitivity analysis and has an interactive optimization module. The use of the system is illustrated by an example; the design and optimization of a two-speed gearbox.

Introducing Knowledge-Based Engineering Into an Interconnected Product Development Process

1999 ◽  
Author(s):  
Daniel E. Whitney ◽  
Qi Dong ◽  
Jared Judson ◽  
Gregory Mascoli
Keyword(s):  
Development Process ◽  
Product Development Process ◽  
System Level ◽  
Knowledge Based ◽  
Design Structure ◽  
Engine Component ◽  
Level Information ◽  
Tightly Coupled ◽  
Knowledge Based Engineering ◽  
Knowledge Content

Abstract Recently, a large automobile company implemented a Knowledge-based Engineering (KBE) application to help design an engine component. While the KBE developers aimed to facilitate a single engineer’s ability to design this component using only the KBE application, it can be shown that in fact this component’s design is tightly coupled to that of several others. Can KBE handle situations like this? How common are they? To address these and other questions, Design Structure Matrix (DSM) models were made of this component at three levels: system interactions, assembly of the component, and individual parts. The size, row names, and internal entries of these matrices were compared to matrices constructed from several conventional written design guides and a flowchart of the KBE application. In each case, the DSM contained more rows or more matrix entries per row, especially at the system interaction level. Since the DSMs were constructed by interviewing experienced engineers, one implication is that while low-aggregation information may be documented, system level information at this company mostly resides in people’s heads. An informal measure of “knowledge content” based on the number of matrix entries per row was shown to be consistent with similar measurements made on DSMs obtained by several other researchers. These results indicate some of the scope and complexity challenges that KBE faces.

Passive but defiant: the role of innovative capabilities in knowledge management and corporate entrepreneurship

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Azhar Khalil ◽  
Muhammad Khuram Khalil ◽  
Rashid Khalil
Keyword(s):  
Corporate Entrepreneurship ◽  
Empirical Literature ◽  
Equation Modeling ◽  
Market Demand ◽  
Content Type ◽  
Knowledge Based ◽  
Innovative Capabilities ◽  
The Impact ◽  
The Relationship

Purpose This paper aims to examine the role of organizational innovative capabilities (OIC) on the relationship between knowledge sharing (KS), corporate entrepreneurship (CE) and firm performance (FP). Specifically, this study uses the knowledge-based view to develop a model that examines the mentioned relationship. Design/methodology/approach Using survey data from 520 participants across 75 service sector companies in Thailand, measurement and structure models are tested through structural equation modeling to quantify the impact between constructs. Findings This study shows that KS and CE positively affect OIC and FP. A positive relationship is also found between KS and CE. The mediating impact of OIC strengthens the relationship between KS and CE on FP. Research limitations/implications Like all research using survey methods, the research is prone to respondent biases and generalizability. However, this paper has put the best effort to minimize such effects by rigorous methodological testing to avoid such biases. Practical implications The findings of this study suggest that to improve organizational learning and knowledge-based performance, commitment and understanding of the employees in the entire organization is crucial. KS significantly contributes to developing innovative abilities because of its characteristics of providing firm-specific and socially complex advantages. The way a firm transforms and exploits its knowledge may ascertain its level of innovativeness, such as coming up with certain problem-solving procedures and new product development according to the rapid change in the market demand. However, organizations may only instigate to effectively organize knowledge when their employees are ready to share knowledge. Continuous KS boosts entrepreneurial practices and contributes innovativeness across individuals, groups, units or the entire organization. Originality/value The relationship between CE, organization innovative capabilities and FP in the presence of KS is rarely discussed in both theoretical and empirical literature. This study contributes to the literature by arguing that apart from the direct impact of KS on FP, KS can lead the firms toward generating important competitive advantage by forming innovative capabilities that can significantly influence FP.

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