Design for Additive Manufacturing in the Cloud Platform

2017 ◽  
Author(s):  
Yuanbin Wang ◽  
Robert Blache ◽  
Xun Xu
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Additive Manufacturing ◽  
Full Advantage ◽  
New Technologies ◽  
Design Stage ◽  
Design For Additive Manufacturing ◽  
Cloud Platform ◽  
Am Processes

Additive manufacturing (AM) has experienced a phenomenal expansion in recent years and new technologies and materials rapidly emerge in the market. Design for Additive Manufacturing (DfAM) becomes more and more important to take full advantage of the capabilities provided by AM. However, most people still have limited knowledge to make informed decisions in the design stage. Therefore, an interactive DfAM system in the cloud platform is proposed to enable people sharing the knowledge in this field and guide the designers to utilize AM efficiently. There are two major modules in the system, decision support module and knowledge management module. A case study is presented to illustrate how this system can help the designers understand the capabilities of AM processes and make rational decisions.

A Knowledge Management System to Support Design for Additive Manufacturing Using Bayesian Networks

2018 ◽  
Vol 140 (5) ◽  
Author(s):  
Yuanbin Wang ◽  
Robert Blache ◽  
Pai Zheng ◽  
Xun Xu
Keyword(s):  
Knowledge Management ◽  
Additive Manufacturing ◽  
Bayesian Networks ◽  
Management System ◽  
Knowledge Management System ◽  
Critical Parameters ◽  
Design For Additive Manufacturing ◽  
Fused Deposition ◽  
Information Layer ◽  
Am Processes

Design for additive manufacturing (DfAM) is gaining increasing attention because of the unique capabilities that additive manufacturing (AM) technologies provide. While they have the ability to produce more complex shapes at no additional cost, AM technologies introduce new constraints. A detailed knowledge of the AM process plays an important role in the design of parts in order to achieve the desired print result. However, research on knowledge management in this area is still limited. The large number of different AM processes, their individual sets of critical parameters and the variation in printing all contribute to a high level of uncertainty in this knowledge domain. Applying AM at the early stages of design projects introduces another source of uncertainty, as requirements are often not well defined at that point. In this paper, a knowledge management system using Bayesian networks (BNs) is proposed to model AM knowledge in cases where there is some uncertainty and fill the knowledge gap between designers and AM technologies. The structure of the proposed model is defined here by introducing the overview layer and detailed information layer. In each layer, different types of nodes and their causal relationships are defined. The system can learn conditional probabilities in the model from different sources of information and inferences can be conducted in both forward and backward directions. To verify the accuracy of the BNs, a sample model for dimensional accuracy in the fused deposition modeling (FDM) process is presented and the results are compared with other methods. A case study is provided to illustrate how the proposed system can help designers with different design questions understand the capabilities of AM processes and find appropriate design and printing solutions.

A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis

2018 ◽  
Author(s):  
Samyeon Kim ◽  
David W. Rosen ◽  
Paul Witherell ◽  
Hyunwoong Ko
Keyword(s):  
Additive Manufacturing ◽  
Design For Additive Manufacturing ◽  
Semantic Query ◽  
Ontology Language ◽  
Build Time ◽  
High Level ◽  
Design Freedom ◽  
Am Processes ◽  
Manufacturing Feature

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals, e.g., reducing build-time. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to formalize DFAM knowledge and support queries for retrieving that knowledge. The DFAM ontology has three high level classes to represent design rules specifically: feature, parameter, and AM capability. Furthermore, the manufacturing feature concept is defined to link part design to AM process parameters. Since manufacturing features contain information on feature constraints of AM processes, the DFAM ontology supports manufacturability analysis of design features by reasoning with Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules in this study also help retrieve design recommendations for improving manufacturability. A case study is performed to illustrate usefulness of the DFAM ontology and SQWRL rule application. This study contributes to developing a knowledge base that can be reusable and upgradable and to analyzing manufacturing analysis to provide feedback about part designs to designers.

A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis

2019 ◽  
Vol 19 (4) ◽  
Author(s):  
Samyeon Kim ◽  
David W. Rosen ◽  
Paul Witherell ◽  
Hyunwoong Ko
Keyword(s):  
Additive Manufacturing ◽  
Design For Additive Manufacturing ◽  
Design Features ◽  
Design Rules ◽  
Semantic Query ◽  
Ontology Language ◽  
Design Freedom ◽  
Am Processes ◽  
Manufacturing Feature

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure the manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals. Furthermore, the wide variety of AM processes, materials, and machines creates challenges in determining manufacturability constraints. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to semantically model DFAM knowledge and retrieve that knowledge. The goal of the proposed DFAM ontology is to provide a structure for information on part design, AM processes, and AM capability to represent design rules. Furthermore, the manufacturing feature concept is introduced to indicate design features that are considerably constrained by given AM processes. After developing the DFAM ontology, queries based on design rules are represented to explicitly retrieve DFAM knowledge and analyze manufacturability using Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules enable effective reasoning to evaluate design features against manufacturing constraints. The usefulness of the DFAM ontology is demonstrated in a case study where design features of a bracket are selected as manufacturing features based on a rule development process. This study contributes to developing a reusable and upgradable knowledge base that can be used to perform manufacturing analysis.

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

scholarly journals An Optimization Workflow in Design for Additive Manufacturing

2021 ◽  
Vol 11 (6) ◽  
pp. 2572
Author(s):  
Stefano Rosso ◽  
Federico Uriati ◽  
Luca Grigolato ◽  
Roberto Meneghello ◽  
Gianmaria Concheri ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Structural Optimization ◽  
Geometric Model ◽  
Complex Geometries ◽  
Design Phase ◽  
Design For Additive Manufacturing ◽  
Engineering Software ◽  
Embodiment Design ◽  
Pros And Cons

Additive Manufacturing (AM) brought a revolution in parts design and production. It enables the possibility to obtain objects with complex geometries and to exploit structural optimization algorithms. Nevertheless, AM is far from being a mature technology and advances are still needed from different perspectives. Among these, the literature highlights the need of improving the frameworks that describe the design process and taking full advantage of the possibilities offered by AM. This work aims to propose a workflow for AM guiding the designer during the embodiment design phase, from the engineering requirements to the production of the final part. The main aspects are the optimization of the dimensions and the topology of the parts, to take into consideration functional and manufacturing requirements, and to validate the geometric model by computer-aided engineering software. Moreover, a case study dealing with the redesign of a piston rod is presented, in which the proposed workflow is adopted. Results show the effectiveness of the workflow when applied to cases in which structural optimization could bring an advantage in the design of a part and the pros and cons of the choices made during the design phases were highlighted.

Development of a Design for Additive Manufacturing Worksheet for Medical Casts

2021 ◽  
Author(s):  
Heena Noh ◽  
Kijung Park ◽  
Kiwon Park ◽  
Gül E. Okudan Kremer
Keyword(s):  
Additive Manufacturing ◽  
High Strength ◽  
Design Guidelines ◽  
Operational Performance ◽  
Design For Additive Manufacturing ◽  
Trade Offs ◽  
Novice Designers ◽  
Criteria Importance ◽  
Design For Am

Abstract Traditional plaster casts often cause dermatitis due to disadvantages in usability and wearability. Additive manufacturing (AM) can fabricate customized casts to have light-weight, high strength, and better air permeability. Although existing studies have provided design for additive manufacturing (DfAM) guidelines to facilitate design applications for AM, most relevant studies focused on the mechanical properties of outputs and too general/specific design guidelines; novice designers may still have difficulty understanding trade-offs between functional and operational performance of various DfAM aspects for medical casts. As a response, this study proposes a DfAM worksheet for medical casts to effectively guide novice designers. First, important DfAM criteria and their possible solutions for medical casts are examined through a literature review to construct a basic DfAM framework for medical casts. Next, a scoring system that considers relative criteria importance and criteria evaluation from both functional and operational perspectives is developed to identify the overall suitability of a medical cast design for AM. A case study of finger cast designs was performed to identify the DfAM performance of the sample designs along with redesign requirements suggested by the worksheet. The proposed worksheet would be used to achieve rapid medical cast design by objectively assessing its suitability for AM.

Creating Superior Knowledge Discovery Solutions

2011 ◽  
pp. 795-799 ◽  
Author(s):  
Nilmini Wickramasinghe
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Decision Support ◽  
Knowledge Discovery ◽  
High Performance ◽  
New Technologies ◽  
Relevant Information ◽  
Customer Relationship ◽  
Business Strategies ◽  
Competitive Edge

The information age has made information communication technology (ICT) a necessity for conducting business. This in turn has led to the exponential increase in the electronic capture of data and its storage in vast data warehouses. In order to respond quickly to fast changing markets, organizations must maximize these raw data and information resources. Specifically, they need to transform them into germane knowledge to aid superior decision-making (Wickramasinghe & von Lubitz, 2006). To do this effectively not only involves the analysis of the data and information but also requires the use of sophisticated tools to enable such analyses to occur. Knowledge discovery technologies represent a spectrum of new technologies that facilitate the analysis of data to find relationships from the data to finding reasons behind observable patterns (i.e., transform the data into relevant information and germane knowledge). Such new discoveries can have a profound impact on decision making in general and the designing of business strategies. With the massive increase in data being collected and the demands of a new breed of intelligent applications like customer relationship management, demand planning, and predictive forecasting, these knowledge discovery technologies are becoming competitive necessities for providing a high performance and feature rich intelligent application servers for intelligent enterprises. Knowledge management (KM) tools and technologies are the systems that integrate various legacy systems, databases, ERP systems, and data warehouse to help facilitate an organization’s knowledge discovery process. Integrating all of these with advanced decision support and online real time events enables an organization to understand customers better and devise business strategies accordingly. Creating a competitive edge is the goal of all organizations employing knowledge discovery for decision support (Thorne & Smith, 2000). The following provides a synopsis of the major tools and critical considerations required to enable an organization to successfully effect appropriate knowledge sharing, knowledge distribution, knowledge creation, as well as knowledge capture and codification processes and hence embrace effective knowledge management (KM) techniques and advanced knowledge discovery.

The Launch of Web-based Collaborative Decision Support at NASA

2008 ◽  
pp. 113-125
Author(s):  
Irma Becerra-Fernandez ◽  
Matha Del Alto ◽  
Helen Stewart
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Decision Makers ◽  
Web Based ◽  
Management Platform ◽  
Individual Person ◽  
Mission Critical ◽  
Multiple Domains ◽  
Collaborative Decision

Today, organizations rely on decision makers to make mission-critical decisions that are based on input from multiple domains. The ideal decision maker has a profound understanding of specific domains coupled with the experience that allows him or her to act quickly and decisively on the information. Daily, decision makers face problems and failures that are too difficult for any individual person to solve; therefore, teams are now required who share their knowledge in spontaneous collaborations. Since requisite expertise may not all reside in the same organization, nor be geographically colocated, virtual networked teams are needed. This chapter presents a case study describing the development and use of Postdoc, the first Web-based collaborative and knowledge management platform deployed at NASA.

Selection of additive manufacturing processes

2017 ◽  
Vol 23 (2) ◽  
pp. 434-447 ◽  
Author(s):  
Yuanbin Wang ◽  
Robert Blache ◽  
Xun Xu
Keyword(s):  
Additive Manufacturing ◽  
New Technologies ◽  
Rapid Development ◽  
A Priori ◽  
Design Stage ◽  
Selection Methods ◽  
Sequential Decision ◽  
Process Selection ◽  
New Approach ◽  
Content Type

Purpose This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user’s choices are in abundance, finding the right choice can be a non-trivial task. Design/methodology/approach AM process selection methods are reviewed based on decision theory. The authors also examine how the user’s preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM. Findings All current studies follow a sequential decision process and focus on an “a priori” articulation of preferences approach. This kind of method has limitations for the user in the early design stage to implement the DfAM process. An “a posteriori” articulation of preferences approach is proposed to support DfAM and an iterative design process. Originality/value This paper reviews AM process selection methods in a new perspective. The users need to be aware of the underlying assumptions in these methods. The limitations of these methods for DfAM are discussed, and a new approach for AM process selection is proposed.

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