An Investigation Into the Driving Factors of Creativity in Design for Additive Manufacturing

2017 ◽  
Author(s):  
Michael Barclift ◽  
Timothy W. Simpson ◽  
Maria Alessandra Nusiner ◽  
Scarlett Miller
Keyword(s):  
Additive Manufacturing ◽  
Self Report ◽  
Risk Attitudes ◽  
Design For Additive Manufacturing ◽  
Domain Specific ◽  
Common Framework ◽  
And Performance ◽  
Student Risk ◽  
Prior Experiences ◽  
Design Freedom

Additive manufacturing (AM) provides engineers with nearly unlimited design freedom, but how much do they take advantage of that freedom? The objective is to understand what factors influence a designer’s creativity and performance in Design for Additive Manufacturing (DFAM). Inspired by the popular Marshmallow Challenge, this exploratory study proposes a framework in which participants apply their DFAM skills in sketching, CAD modeling, 3D-Printing, and a part testing task. Risk attitudes are assessed through the Engineering Domain-Specific Risk-Taking (E-DOSPERT) scale, and prior experiences are captured by a self-report skills survey. Multiple regression analysis found that the average novelty of the participant’s ideas, engineering degree program, and risk seeking preference were statistically significant when predicting the performance of their ideas in AM. This study provides a common framework for AM educators to assess students’ understanding and creativity in DFAM, while also identifying student risk attitudes when conducting an engineering design task.

scholarly journals Evaluating the Potential of Design for Additive Manufacturing Heuristic Cards to Stimulate Novel Product Redesigns

2019 ◽  
Author(s):  
Alexandra Blösch-Paidosh ◽  
Saeema Ahmed-Kristensen ◽  
Kristina Shea
Keyword(s):  
Additive Manufacturing ◽  
Design For Additive Manufacturing ◽  
Effective Mechanism ◽  
Design Heuristics ◽  
Design Students ◽  
Functional Complexity ◽  
Team Design ◽  
Design Freedom ◽  
Design Heuristic ◽  
Tools And Methods

Abstract Additive manufacturing (AM) affords those who wield it correctly the benefits of shape, material, hierarchical, and functional complexity. However, many engineers and designers lack the training and experience necessary to take full advantage of these benefits. They require training, tools, and methods to assist them in gaining the enhanced design freedom made possible by additive manufacturing. This work, which is an extension of the authors’ previous work, explores if design heuristics for AM, presented in a card-based format, are an effective mechanism for helping designers achieve the design freedoms enabled by AM. The effectiveness of these design heuristic cards is demonstrated in an experiment with 27 product design students, by showing that there is an increase in the number of unique capabilities of AM being utilized, an increase in the AM novelty, and an increase in the AM flexibility of the generated concepts, when given access to the cards. Additionally, similar to the previous work, an increase in the number of interpreted heuristics and AM modifications present in the participants’ designs when they are provided with the heuristic cards is shown. Comparisons are also made between 8-heuristic and 29-heuristic experiments, but no conclusive statements regarding these comparisons can be drawn. Further user studies are planned to confirm the efficacy of this format at enhancing the design freedoms achieved in group and team design scenarios.

Shape and Performance Controlled Advanced Design for Additive Manufacturing: A Review of Slicing and Path Planning

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Donghua Zhao ◽  
Weizhong Guo
Keyword(s):  
Additive Manufacturing ◽  
Path Planning ◽  
Rapid Development ◽  
Optimization Methods ◽  
Current Status ◽  
Curved Surfaces ◽  
Support Structures ◽  
Design For Additive Manufacturing ◽  
And Performance ◽  
And Function

AbstractAdditive manufacturing (AM) brings out a revolution of how the products are designed and manufactured. To obtain desired components, advanced design for additive manufacturing (ADfAM) is widely emphasized in geometry, material, and function design. 3D slicing and path planning, which are the critical steps of ADfAM, directly determine manufacturing process variables, shape, and performance of printed parts. For widely used planar slicing, the contradiction between accuracy and build time has attracted considerable attention and efforts, leading to various novel and optimization methods. Nevertheless, curved surfaces and slopes along the build direction constrain the surfaces to be smooth due to the inherent staircase effect of AM. Meanwhile, there is significant anisotropy of the printed piece making it sensitive to any shear (or bending) stress. Moreover, support structures for the overhang part are necessary when building along one direction, resulting in time-consuming and cost-expensive process. Due to the rapid development of 3D slicing and path planning, and various newly proposed methods, there is a lack of comprehensive knowledge. Notwithstanding, there are fewer literature reviews concerning planar slicing and filling strategy. Less attention has been paid to non-planar slicing, path planning on curved surfaces, and multi-degree of freedom (DOF) AM equipment, as well as printing under pressure. Hence, it is significant to get a comprehensive understanding of current status and challenges. Then, with suitable technologies, the printed parts with improved surface quality, minimum support structures, and better isotropy could be acquired. Finally, the recommendation for the future development of slicing and path planning is also provided.

The Influence of Expertise on Aesthetics

2020 ◽  
Author(s):  
Aaron Kozbelt
Keyword(s):  
Aesthetic Experience ◽  
Research Literature ◽  
Self Report ◽  
Aesthetic Response ◽  
Expert Performance ◽  
Behavioral Measures ◽  
Domain Specific ◽  
Psychological Mechanisms ◽  
Aesthetic Cognition ◽  
And Performance

This chapter reviews how expertise impacts aesthetic experience and cognition. It first lays out some well-established methods and findings from the extensive research literature on expertise and expert performance and discusses how these relate to empirical aesthetics. Next, it describes general psychological mechanisms and models of aesthetic processing, emphasizing the potential role of expertise in modulating aesthetic cognition within such models. Since expertise is highly domain-specific, the chapter then proceeds sequentially through a range of aesthetic domains: visual art, design, architecture, photography, music, dance, writing, acting, and film. In each case, behavioral measures (self-report and performance indices) and neuroscientific findings are considered where available. When possible, the chapter discusses not only aesthetic response but also performance and creativity as aspects vital for understanding expertise and its effects in aesthetic domains. After reviewing the aforementioned domains individually, the concluding section attempts to integrate these points by highlighting consistent patterns of results and by briefly considering a few unresolved conceptual issues.

Development of Technical Creativity Featuring Modified TRIZ-AM Inventive Principle to Support Additive Manufacturing

2021 ◽  
pp. 1-47
Author(s):  
Siti Nur Humaira Mazlan ◽  
Aini Zuhra Abdul Kadir ◽  
Mariusz Deja ◽  
Dawid Zielinski ◽  
Mohd Rizal Alkahari
Keyword(s):  
Additive Manufacturing ◽  
Problem Solving ◽  
Product Design ◽  
Design For Additive Manufacturing ◽  
Technical Creativity ◽  
Design Methodologies ◽  
Creative Solutions ◽  
Design Freedom ◽  
Early Phases

Abstract The design for additive manufacturing (DFAM) processing was introduced to fully utilise the design freedom provided by additive manufacturing (AM). Consequently, appropriate design methodologies have become essential for this technology. Recently, many studies have identified the importance of DFAM method utilisation to produce AM parts, and TRIZ is a strategy used to formalise design methodologies. TRIZ is a problem-solving tool developed to assist designers to find innovative and creative solutions. However, the pathway for synergising TRIZ and DFAM is not clearly explained with respect to AM capabilities and complexities. This is mainly because most methods continue to involve use of the classical TRIZ principle, which was developed early in 1946, 40 years before AM technologies were introduced in the mid-1980s. Therefore, to tackle this issue, this study aims to enhance the 40 principles of classical TRIZ to accommodate AM design principles. A modified TRIZ-AM principle has been developed to define the pathway to AM solutions. TRIZ-AM cards are tools that assist designers to select inventive principles (IPs) in the early phases of product design and development. The case study illustrates that even inexperienced AM users can creatively design innovative AM parts.

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.

Teaching Design Freedom: Understanding the Effects of Variations in Design for Additive Manufacturing Education on Students’ Creativity

2020 ◽  
Vol 142 (9) ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Educational Interventions ◽  
Expert Assessment ◽  
Layer By Layer ◽  
Significant Finding ◽  
Design For Additive Manufacturing ◽  
Innovative Products ◽  
Design Challenge ◽  
Design Freedom ◽  
Am Processes

Abstract Additive manufacturing (AM) processes offer unique capabilities (i.e., opportunities) yet inherent limitations (i.e., restrictions) due to the layer-by-layer fabrication of parts. Despite the newfound design freedom and increased use of AM, limited research has investigated how knowledge of the AM processes affects the creativity of students’ ideas after being exposed to AM. This study investigates this gap through an experimental study with 343 participants recruited from a junior-level mechanical engineering design course. The participants were exposed to three variations in design for additive manufacturing (DfAM) education: (1) no DfAM, (2) restrictive DfAM, and (3) opportunistic and restrictive (dual) DfAM education. The effects of these three interventions were measured through differences in (1) participants’ self-reported use of DfAM in a design challenge and (2) expert assessment of the creativity of the outcomes from the said design challenge. The results of the study indicated that variations in DfAM content did not result in differences in the participants’ self-reported use of either opportunistic or restrictive DfAM, with all three groups reporting similar levels of emphasis. Further, participants from all three groups reported higher use of restrictive DfAM techniques, compared with opportunistic DfAM. Moreover, while variations in the content had no effect on the creativity (uniqueness and usefulness) of the participants’ design outcomes, teaching both opportunistic and restrictive DfAM did result in the generation of designs with greater AM technical goodness—a novel and significant finding in our study. The results of this study highlight the need for DfAM educational interventions that encourage students to not only learn about but also integrate both opportunistic and restrictive concepts effectively into their creative design process. This would result in the generation of innovative products that leverage the design freedom enabled by AM, yet addressing the limitations inherent in the process.

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.

Application of Topology Optimization and Design for Additive Manufacturing Guidelines on an Automotive Component

2016 ◽  
Author(s):  
Sai Nithin Reddy K. ◽  
Vincent Maranan ◽  
Timothy W. Simpson ◽  
Todd Palmer ◽  
Corey J. Dickman
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Low Cost ◽  
Engineering Practice ◽  
Optimized Design ◽  
Design For Additive Manufacturing ◽  
Manufacturing Costs ◽  
Cost Structures ◽  
And Performance

Topology optimization is a well-established engineering practice to optimize the design and layout of parts to create lightweight and low-cost structures, which have historically been difficult, or impossible, to make. Additive Manufacturing (AM) provides the freedom to fabricate the complex and organic shapes that topology optimization often generates. In this paper we use topology optimization to create lightweight designs while conforming to additive manufacturing constraints related to overhanging features and unsupported surfaces when using metallic materials. More specifically, we use design for additive manufacturing (DfAM) rules along with topology optimization to study the tradeoffs between the weight of the part, support requirements, manufacturing costs, and performance. The case study entails redesigning an upright on the SAE Formula student racecar to reduce support structures and manufacturing and material cost when using Direct Metal Laser Sintering (DMLS). Manufacturing the optimized design without applying DfAM rules required support material up to 202.4% of the volume of the model. Using DfAM, the upright is redesigned and manufactured with supports requiring less than 15% of the volume of the model. The results demonstrate the challenges in achieving a balance between weight reduction, manufacturing costs, and factor of safety of the design.

scholarly journals A STRUCTURED APPROACH TO REDUCE DESIGN ITERATIONS IN ADDITIVE MANUFACTURING

2021 ◽  
Vol 1 ◽  
pp. 231-240
Author(s):  
Laura Wirths ◽  
Matthias Bleckmann ◽  
Kristin Paetzold
Keyword(s):  
Additive Manufacturing ◽  
Spare Parts ◽  
Design Guidelines ◽  
Final Part ◽  
Layer By Layer ◽  
Powder Bed ◽  
Batch Sizes ◽  
Manufacturing Technologies ◽  
Structured Approach ◽  
Design Freedom

AbstractAdditive Manufacturing technologies are based on a layer-by-layer build-up. This offers the possibility to design complex geometries or to integrate functionalities in the part. Nevertheless, limitations given by the manufacturing process apply to the geometric design freedom. These limitations are often unknown due to a lack of knowledge of the cause-effect relationships of the process. Currently, this leads to many iterations until the final part fulfils its functionality. Particularly for small batch sizes, producing the part at the first attempt is very important. In this study, a structured approach to reduce the design iterations is presented. Therefore, the cause-effect relationships are systematically established and analysed in detail. Based on this knowledge, design guidelines can be derived. These guidelines consider process limitations and help to reduce the iterations for the final part production. In order to illustrate the approach, the spare parts production via laser powder bed fusion is used as an example.

scholarly journals GEOMETRICAL BENCHMARKING OF LASER POWDER BED FUSION SYSTEMS BASED ON DESIGNER NEEDS

2021 ◽  
Vol 1 ◽  
pp. 1657-1666
Author(s):  
Joaquin Montero ◽  
Sebastian Weber ◽  
Christoph Petroll ◽  
Stefan Brenner ◽  
Matthias Bleckmann ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Use Case ◽  
Powder Bed Fusion ◽  
Design For Additive Manufacturing ◽  
Laser Powder Bed Fusion ◽  
Measuring Systems ◽  
Powder Bed ◽  
Geometric Dimensioning And Tolerancing ◽  
Uncertainty Map ◽  
New System

AbstractCommercially available metal Laser Powder Bed Fusion (L-PBF) systems are steadily evolving. Thus, design limitations narrow and the diversity of achievable geometries widens. This progress leads researchers to create innovative benchmarks to understand the new system capabilities. Thereby, designers can update their knowledge base in design for additive manufacturing (DfAM). To date, there are plenty of geometrical benchmarks that seek to develop generic test artefacts. Still, they are often complex to measure, and the information they deliver may not be relevant to some designers. This article proposes a geometrical benchmarking approach for metal L-PBF systems based on the designer needs. Furthermore, Geometric Dimensioning and Tolerancing (GD&T) characteristics enhance the approach. A practical use-case is presented, consisting of developing, manufacturing, and measuring a meaningful and straightforward geometric test artefact. Moreover, optical measuring systems are used to create a tailored uncertainty map for benchmarking two different L-PBF systems.

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