Design Features to Address Security Challenges in Additive Manufacturing

2018 ◽  
pp. 23-50 ◽  
Author(s):  
Nikhil Gupta ◽  
Fei Chen ◽  
Khaled Shahin
Keyword(s):  
Additive Manufacturing ◽  
Design Features ◽  
Security Challenges

scholarly journals Assembly Based Methods to Support Product Innovation in Design for Additive Manufacturing: An Exploratory Case Study

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Floriane Laverne ◽  
Frédéric Segonds ◽  
Nabil Anwer ◽  
Marc Le Coq
Keyword(s):  
Additive Manufacturing ◽  
Product Innovation ◽  
Idea Generation ◽  
Generation Process ◽  
Design For Additive Manufacturing ◽  
Design Features ◽  
Systemic Level ◽  
Product Manufacturing

Additive manufacturing (AM) is emerging as an important manufacturing process and a key technology for enabling innovative product development. Design for additive manufacturing (DFAM) is nowadays a major challenge to exploit properly the potential of AM in product innovation and product manufacturing. However, in recent years, several DFAM methods have been developed with various design purposes. In this paper, we first present a state-of-the-art overview of the existing DFAM methods, then we introduce a classification of DFAM methods based on intermediate representations (IRs) and product's systemic level, and we make a comparison focused on the prospects for product innovation. Furthermore, we present an assembly based DFAM method using AM knowledge during the idea generation process in order to develop innovative architectures. A case study demonstrates the relevance of such approach. The main contribution of this paper is an early DFAM method consisting of four stages as follows: choice and development of (1) concepts, (2) working principles, (3) working structures, and (4) synthesis and conversion of the data in design features. This method will help designers to improve their design features, by taking into account the constraints of AM in the early stages.

Design Modification of Additive Manufacturing Parts Using Texture Information of 3D Model

2019 ◽  
Vol 825 ◽  
pp. 19-30
Author(s):  
Tsung Chien Wu ◽  
Jiing Yih Lai ◽  
Yu Wen Tseng ◽  
Chao Yaug Liao ◽  
Ju Yi Lee
Keyword(s):  
Additive Manufacturing ◽  
3D Model ◽  
Three Dimensional ◽  
Texture Mapping ◽  
3D Models ◽  
Surface Shape ◽  
Design Features ◽  
Design Modification ◽  
Texture Information ◽  
Triangular Model

Additive manufacturing (AM) has been commonly used for the prototyping of three-dimensional (3D) models. The input model of the AM technology is a triangular model representing the surface shape of an object. The design features on a triangular model are generally not clear as the vertices are irregularly distributed. If design modification is necessary, it is difficult to segment and extract the meshes from the model. The objective of this study is to propose a method for extracting the design features on an object model by using the texture information. A 3D color model including a triangular model representing the object shape and a texture map describing the object texture is employed. The 3D model is generated by using a set of object images captured from different views surrounding the object. A texture mapping algorithm is then employed to generate the texture map corresponding to the 3D model. With both meshes and texture displayed in a texture mode, a region extraction technique is employed to extract the design features. All parts separated can then be fabricated with an AM machine, and assembled for checking the feasibility of design modification. Several products are employed to demonstrate the feasibility of the proposed technique.

scholarly journals DESIGN COMPLEXITY AS A DRIVER FOR ADDITIVE MANUFACTURING PROCESS IMPROVEMENT

2020 ◽  
Author(s):  
Nishkal George ◽  
Boppana Chowdary
Keyword(s):  
Additive Manufacturing ◽  
Design Space Exploration ◽  
Performance Indicator ◽  
Extrusion Process ◽  
Experimental Models ◽  
Production Costs ◽  
Design Features ◽  
Design Complexity ◽  
Filter Model ◽  
Material Extrusion

Design complexity in additive manufacturing (AM) is a current issue in the research community, fueled by the well-known phrase “complexity for free”. This statement has promoted the assumption that complex geometries may be achieved without any increase in the cost of production. However, recent research has indicated that increasing shape complexity produces an increase in production costs for the material extrusion process. This challenges the mainstream assumption that AM technologies provide ‘complexity for free’. The AM community requires further investigation of design complexity and its impact on sustainable production when used as a Design for Manufacturing (DfM) tool. This paper proposes a data-driven method which uses design complexity as an AM performance indicator for the material extrusion process. The manufacturing responses included build time (BT), dimensional accuracy (DA) and complexity index (CI). Design space exploration of an automotive air filter model was achieved by varying five critical design features which impact complexity. The study utilized a Face Centered Central Composite Design (FCCCD) of three levels for the design features, comprising 32 experimental models. The optimal model was manufactured based on multi-objective optimization using the MINITAB© response optimizer. This method exploits the design features to achieve target performance and manufacturability. The viability of design complexity as an AM performance indicator was discussed leading to three major improvements to the Product Design and Development (PDD) process for AM. The proposed improvements have the potential to reduce process times and minimize resources, providing a sustainable AM approach for developing regions.

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.

A hybrid machine learning approach for additive manufacturing design feature recommendation

2017 ◽  
Vol 23 (6) ◽  
pp. 983-997 ◽  
Author(s):  
Xiling Yao ◽  
Seung Ki Moon ◽  
Guijun Bi
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Conceptual Design ◽  
Learning Algorithm ◽  
Design Feature ◽  
Machine Learning Algorithm ◽  
Design Features ◽  
Content Type ◽  
Conceptual Design Phase ◽  
Hybrid Machine

Purpose This paper aims to present a hybrid machine learning algorithm for additive manufacturing (AM) design feature recommendation during the conceptual design phase. Design/methodology/approach In the proposed hybrid machine learning algorithm, hierarchical clustering is performed on coded AM design features and target components, resulting in a dendrogram. Existing industrial application examples are used to train a supervised classifier that determines the final sub-cluster within the dendrogram containing the recommended AM design features. Findings Through a case study of designing additive manufactured R/C car components, the proposed hybrid machine learning method was proven useful in providing feasible conceptual design solutions for inexperienced designers by recommending appropriate AM design features. Originality/value The proposed method helps inexperienced designers who are newly exposed to AM capabilities explore and utilize AM design knowledge computationally.

Mechanical Design Features of a Small Gas Turbine for Power Generation in Unmanned Aerial Vehicles

2015 ◽  
Author(s):  
Caleb S. Cunningham ◽  
David Ransom ◽  
Jason Wilkes ◽  
John Bishop ◽  
Benjamin White
Keyword(s):  
Power Generation ◽  
Additive Manufacturing ◽  
Unmanned Aerial Vehicles ◽  
Gas Turbine ◽  
Natural Frequencies ◽  
Mechanical Design ◽  
Lubrication System ◽  
Oil Lubrication ◽  
Design Features ◽  
Manufacturing Techniques

As part of the Intelligence Advanced Research Projects Activity (iARPA) Great Horned Owl (GHO) program, Southwest Research Institute® (SwRI®) developed and tested a small gas turbine for power generation in Unmanned Aerial Vehicles (UAV). This development program focused on advancing the state of the art in UAV power systems by meeting key metrics in weight, fuel efficiency, and noise generation. Design, assembly, and testing of the gas turbine were completed in-house at SwRI. Fundamental mechanical design features of the gas turbine include an integrated 7 kW motor-generator, minimal oil lubrication system, cantilevered compressor/turbine assembly, and can combustor with air-atomizing fuel nozzles. The compressor/turbine assembly is cantilevered directly off of the motor-generator shaft, which spins on hybrid ceramic bearings. Due to potential rotor natural frequencies in the design operating range, the rotor-dynamic design of this configuration was a special design challenge. The outboard rotor bearing is softly supported on O-rings to provide compliance and drive shaft natural frequencies below the operating range. The lube oil system is another interesting design feature of the GHO gas turbine. It is based on a minimal oil lubrication system previously used at SwRI. The minimal oil lubrication system relies on low oil flow rates and cooling air to pull droplets of oil through the bearing. The oil passes through the machine and is consumed during combustion. This system eliminates traditional oil recirculation hardware for simplicity and weight savings. The can combustor features a modular design and uses additive manufacturing techniques to facilitate easy and cost effective prototyping. All combustor components are manufactured from Inconel 718 using direct metal laser sintering (DMLS) with additional post-machining. These parts are particularly challenging for DMLS because of their thin walls and high aspect ratio. The custom air-atomizing fuel nozzles also highlight one of the exciting advantages of the DMLS process. Each nozzle would be difficult to machine using traditional techniques because of the tight internal flow passages; however, they are simple to construct using additive manufacturing. The GHO turbine developed by SwRI demonstrates interesting design features including a minimal oil lubrication system, a cantilever shaft with softly supported bearing, and combustor components built using additive manufacturing techniques. This design provides a platform for further development, testing, and demonstration of small gas turbine technology for UAV power generation.

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