But Will It Build? Assessing Student Engineering Designers’ Use of Design for Additive Manufacturing Considerations in Design Outcomes

2020 ◽  
Vol 142 (9) ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Undergraduate Students ◽  
Contact Area ◽  
Educational Interventions ◽  
Support Material ◽  
Design Task ◽  
Process Characteristics ◽  
Build Time ◽  
And Performance ◽  
Part Complexity

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. These limitations 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, few studies have objectively assessed the integration of DfAM in student engineering designers’ design outcomes. Furthermore, limited research has explored how the use of DfAM affects the students’ AM designs’ achievement of design task objectives. This research explores this gap in literature through an experimental study with 301 undergraduate students. Specifically, participants were exposed to either restrictive DfAM or dual DfAM (both opportunistic and restrictive) and then asked to participate in a design challenge. The participants’ final designs were evaluated for (1) build time and build material (2) the use of the various DfAM concepts, and (3) the features used to manifest these DfAM concepts. The results show that the use of certain DfAM considerations, such as part complexity, number of parts, support material mass, and build plate contact area (corresponding to warping tendency), correlated with the build material and build time of the AM designs—minimizing both of which were objectives of the design task. The results also show that introducing participants to opportunistic DfAM leads to the generation of designs with higher part complexity and lower build plate contact area but a greater presence of inaccessible support material.

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.

scholarly journals DECOMPOSITION ALGORITHM FOR TOOL PATH PLANNING FOR WIRE-ARC ADDITIVE MANUFACTURING

2018 ◽  
Vol Vol.18 (No.1) ◽  
pp. 96-107 ◽  
Author(s):  
Lam NGUYEN ◽  
Johannes BUHL ◽  
Markus BAMBACH
Keyword(s):  
Additive Manufacturing ◽  
Path Planning ◽  
Computer Aided Design ◽  
Tool Path ◽  
Heuristic Method ◽  
Support Material ◽  
Build Time ◽  
Cad Models ◽  
Aided Design ◽  
Wire Arc Additive Manufacturing

Three-axis machines are limited in the production of geometrical features in powder-bed additive manufacturing processes. In case of overhangs, support material has to be added due to the nature of the process, which causes some disadvantages. Robot-based wire-arc additive manufacturing (WAAM) is able to fabricate overhangs without adding support material. Hence, build time, waste of material, and post-processing might be reduced considerably. In order to make full use of multi-axis advantages, slicing strategies are needed. To this end, the CAD (computer-aided design) model of the part to be built is first partitioned into sub-parts, and for each sub-part, an individual build direction is identified. Path planning for these sub-parts by slicing then enables to produce the parts. This study presents a heuristic method to deal with the decomposition of CAD models and build direction identification for sub-entities. The geometric data of two adjacent slices are analyzed to construct centroidal axes. These centroidal axes are used to navigate the slicing and building processes. A case study and experiments are presented to exemplify the algorithm.

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.

scholarly journals Optimization of Build Time and Support Material Quantity for the Additive Manufacturing of Non-Assembly Mechanisms

Procedia CIRP ◽  
2019 ◽  
Vol 84 ◽  
pp. 271-276 ◽  
Author(s):  
Martin Hallmann ◽  
Stefan Goetz ◽  
Benjamin Schleich ◽  
Sandro Wartzack
Keyword(s):  
Additive Manufacturing ◽  
Support Material ◽  
Build Time

scholarly journals On the Impact of Additive Manufacturing Processes Complexity on Modelling

2021 ◽  
Vol 11 (16) ◽  
pp. 7743
Author(s):  
Panagiotis Stavropoulos ◽  
Panagis Foteinopoulos ◽  
Alexios Papapacharalampopoulos
Keyword(s):  
Additive Manufacturing ◽  
Performance Indicators ◽  
Part Quality ◽  
Build Time ◽  
Modelling Techniques ◽  
And Performance ◽  
The Many ◽  
Process Complexity ◽  
The Impact ◽  
Am Processes

The interest in additive manufacturing (AM) processes is constantly increasing due to the many advantages they offer. To this end, a variety of modelling techniques for the plethora of the AM mechanisms has been proposed. However, the process modelling complexity, a term that can be used in order to define the level of detail of the simulations, has not been clearly addressed so far. In particular, one important aspect that is common in all the AM processes is the movement of the head, which directly affects part quality and build time. The knowledge of the entire progression of the phenomenon is a key aspect for the optimization of the path as well as the speed evolution in time of the head. In this study, a metamodeling framework for AM is presented, aiming to increase the practicality of simulations that investigate the effect of the movement of the head on part quality. The existing AM process groups have been classified based on three parameters/axes: temperature of the process, complexity, and part size, where the complexity has been modelled using a dedicated heuristic metric, based on entropy. To achieve this, a discretized version of the processes implicated variables has been developed, introducing three types of variable: process parameters, key modeling variables and performance indicators. This can lead to an enhanced roadmap for the significance of the variables and the interpretation and use of the various models. The utilized spectrum of AM processes is discussed with respect to the modelling types, namely theoretical/computational and experimental/empirical.

Complex Solutions for Complex Problems?: Exploring the Effects of Task Complexity on Student Use of Design for Additive Manufacturing and Creativity

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

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 222 undergraduate engineering students. Specifically, the study compared the effects of restrictive and dual (restrictive and opportunistic) DfAM education, when introduced through either a simple or complex design task. The effects of the intervention were measured through (1) changes in student DfAM self-efficacy, (2) student self-reported emphasis on DfAM, and (3) the creativity of student AM designs. The results show that the complexity of the design task has a significant effect on the participants’ self-efficacy with, and self-reported emphasis on, certain DfAM concepts. The results also show that the complex design task results in participants generating ideas with greater median uniqueness compared to the simple design task. These findings highlight the importance of the chosen problem statement on the outcomes of a DfAM educational intervention, and future work is also discussed.

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