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.

The Earlier the Better? Investigating the Importance of Timing on Effectiveness of Design for Additive Manufacturing Education

2018 ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Self Efficacy ◽  
Educational Interventions ◽  
Layer By Layer ◽  
Design For Additive Manufacturing ◽  
Layer Deposition ◽  
Subtractive Manufacturing ◽  
The Impact ◽  
Design Ideas ◽  
Am Processes

Additive Manufacturing (AM) is a novel process that enables the manufacturing of complex geometries through layer-by-layer deposition of material. AM processes provide a stark contrast to traditional, subtractive manufacturing processes, which has resulted in the emergence of design for additive manufacturing (DfAM) to capitalize on AM’s capabilities. In order to support the increasing use of AM in engineering, it is important to shift from the traditional design for manufacturing and assembly mindset, towards integrating DfAM. To facilitate this, DfAM must be included in the engineering design curriculum in a manner that has the highest impact. While previous research has systematically organized DfAM concepts into process capability-based (opportunistic) and limitation-based (restrictive) considerations, limited research has been conducted on the impact of teaching DfAM on the student’s design process. This study investigates this interaction by comparing two DfAM educational interventions conducted at different points in the academic semester. The two versions are compared by evaluating the students’ perceived utility, change in self-efficacy, and the use of DfAM concepts in design. The results show that introducing DfAM early in the semester when students have little previous experience in AM resulted in the largest gains in students perceiving utility in learning about DfAM concepts and DfAM self-efficacy gains. Further, we see that this increase relates to greater application of opportunistic DfAM concepts in student design ideas in a DfAM challenge. However, no difference was seen in the application of restrictive DfAM concepts between the two interventions. These results can be used to guide the design and implementation of DfAM education.

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 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.

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.

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.

Optimization of Metallic Powder Filaments for Additive Manufacturing Extrusion (MEX)

2021 ◽  
Author(s):  
Fábio Silva Cerejo ◽  
Daniel Gatões ◽  
Teresa Vieira
Keyword(s):  
Additive Manufacturing ◽  
Low Cost ◽  
Metallic Powder ◽  
Powder Injection ◽  
Layer By Layer ◽  
Powder Injection Moulding ◽  
Powder Particles ◽  
Powder Content ◽  
Metallic Parts ◽  
Am Processes

Abstract Additive manufacturing (AM) of metallic powder particles has been establishing itself as sustainable, whatever the technology selected. Material Extrusion (MEX) integrates the ongoing effort to improve AM sustainability, in which low-cost equipment is associated with a decrease of powder waste during manufacturing. MEX has been gaining increasing interest for building 3D functional/structural metallic parts because it incorporates the consolidated knowledge from powder injection moulding/extrusion feedstocks into the AM scope—filament extrusion layer-by-layer. Moreover, MEX as an indirect process can overcome some of the technical limitations of direct AM processes (laser/electron-beam-based) regarding energy-matter interactions. The present study reveals an optimal methodology to produce MEX filament feedstocks (metallic powder, binder and additives), having in mind to attain the highest metallic powder content. Nevertheless, the main challenges are also to achieve high extrudability and a suitable ratio between stiffness and flexibility. The metallic powder volume content (vol.%) in the feedstocks was evaluated by the critical powder volume concentration (CPVC). Subsequently, the rheology of the feedstocks was established by means of the mixing torque value, which is related to the filament extrudability performance.

Advancements in Manufacturing Technology With Additive Manufacturing and Its Context With Industry 4.0

2021 ◽  
pp. 1-24
Author(s):  
Ganzi Suresh
Keyword(s):  
Additive Manufacturing ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Three Dimensional ◽  
Metal Alloys ◽  
Layer By Layer ◽  
Post Processing ◽  
Clear Insight ◽  
Am Processes ◽  
Insight Into

Additive manufacturing (AM) is also known as 3D printing and classifies various advanced manufacturing processes that are used to manufacture three dimensional parts or components with a digital file in a sequential layer-by-layer. This chapter gives a clear insight into the various AM processes that are popular and under development. AM processes are broadly classified into seven categories based on the type of the technology used such as source of heat (ultraviolet light, laser) and type materials (resigns, polymers, metal and metal alloys) used to fabricate the parts. These AM processes have their own merits and demerits depending upon the end part application. Some of these AM processes require extensive post-processing in order to get the finished part. For this process, a separate machine is required to overcome this hurdle in AM; hybrid manufacturing comes into the picture with building and post-processing the part in the same machine. This chapter also discusses the fourth industrial revolution (I 4.0) from the perspective of additive manufacturing.

Complex Solutions for Complex Problems? Exploring the Role of Design Task Choice on Learning, Design for Additive Manufacturing Use, and Creativity

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Self Efficacy ◽  
Engineering Students ◽  
Educational Interventions ◽  
Problem Statement ◽  
Design Task ◽  
Complex Solutions ◽  
Future Work ◽  
Am Processes

Abstract The integration of additive manufacturing (AM) processes in many industries has led to the need for AM education and training, particularly on design for AM (DfAM). To meet this growing need, several academic institutions have implemented educational interventions, especially project- and problem-based, for AM education; however, limited research has explored how the choice of the problem statement influences the design outcomes of a task-based AM/DfAM intervention. This research explores this gap in the literature through an experimental study with 175 undergraduate engineering students. Specifically, the study compared the effects of restrictive and dual (restrictive and opportunistic) DfAM education, when introduced through design tasks that differed in the explicit use of design objectives and functional and manufacturing constraints in defining them. The effects of the intervention were measured through (1) changes in participant DfAM self-efficacy, (2) participants' self-reported emphasis on DfAM, and (3) the creativity of participants' design outcomes. The results show that the choice of the design task has a significant effect on the participants' self-efficacy with, and their self-reported emphasis on, certain DfAM concepts. The results also show that the design task containing explicit constraints and objectives results in participants generating ideas with greater uniqueness compared with the design task with fewer explicit constraints and objectives. These findings highlight the importance of the chosen problem statement on the outcomes of a DfAM educational intervention, and future work is also discussed.

But Will it Print?: Assessing Student Use of Design for Additive Manufacturing and Exploring its Effect on Design Performance and Manufacturability

2019 ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Undergraduate Students ◽  
Educational Interventions ◽  
Support Material ◽  
Design Performance ◽  
Process Characteristics ◽  
Build Time ◽  
And Performance ◽  
Am Processes ◽  
Design For Am

Abstract Additive manufacturing (AM) enables engineers to improve the functionality and performance of their designs by adding complexity at little to no additional cost. However, AM processes also exhibit certain unique limitations, such as the presence of support material, which must be accounted for to ensure that designs can be manufactured feasibly and cost-effectively. Given these unique process characteristics, it is important for an AM-trained workforce to be able to incorporate both opportunistic and restrictive design for AM (DfAM) considerations into the design process. While AM/DfAM educational interventions have been discussed in the literature, limited research has investigated the effect of these interventions on students’ use of DfAM. Furthermore, limited research has explored how DfAM use affects the performance of students’ AM designs. This research explores this gap through an experimental study with 123 undergraduate students. Specifically, participants were exposed to either restrictive DfAM or dual DfAM (both opportunistic and restrictive) and then asked to participate in an AM design challenge. The students’ final designs were evaluated for (1) performance with respect the design objectives and constraints, and (2) the use of the various aspects of DfAM. The results showed that the use of certain DfAM considerations, such as minimum feature size and support material mass, successfully predicted the performance of the AM designs. Further, while the variations in DfAM education did not influence the performance of the AM designs, it did have an effect on the students’ use of certain DfAM concepts in their final designs. These results highlight the influence of DfAM education in bringing about an increase in students’ use of DfAM. Moreover, the results demonstrate the potential influence of DfAM in reducing build time and build material of the students’ AM designs, thus improving design performance and manufacturability.

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