Evaluating Additive Manufacturing for Metallic Facade Components

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Abstract

The state-of-the-art of Additive Manufacturing (AM), popularly known as 3D printing, shows its prospective future in the Architecture-Engineering-Construction (AEC) industry, side-by-side with the conventional manufacturing (CM). AM building components are likely to have higher energy efficiency and environmental sustainability than the CM counterparts. However, currently AEC practitioners do not consider AM’s application in their projects because they are not informed about AM’s characteristics compared to CM in a rapid and consistent manner required for AEC projects. If AEC practitioners had systematic means to rapidly and consistently evaluate the choice between AM and CM for producing building components through transparent criteria such as cost and environmental impacts, they could make well-informed decisions about utilizing AM in their projects.

Firstly, well-documented comparative analyses of AM vs. CM for producing two metallic curtainwall components were carried out in collaboration with a global curtainwall contractor. These case studies included Life Cycle Assessment (LCA), cost and schedule analyses and demonstrated that AM for metallic curtainwall components is technologically feasible and can lower environmental impacts by up to 87%, but is cost-prohibitive today. Secondly, based on the case studies, a 7-activity assessment framework was developed that allows AEC practitioners a systematic way to assess the applicability (A), schedule (S), environmental impacts (E), and cost (C) of AM vs. CM to produce building components.

Finally, introduced in this paper, the application of this semi-automated framework showed that it speeds up the assessment of all four ASEC analyses for more than 22 times, and makes them more consistent. However, the consistency analysis of the ASEC results demonstrated that the inconsistencies of the E results are further expected due to current uncertainty sources related to human factors and LCA tools, having greater impact on the assessment than the framework. Furthermore, regular updates of the framework’s databases are required to maintain the consistency of the A, S and C results.

The use of the formalized framework showed the superiority of the speed and the consistency of the AM vs. CM assessment over the assessment without the framework for specific metallic facade components. With the framework AEC practitioners could make well-informed decisions about utilizing AM in their projects in a timely and consistent manner. Future work includes fuller automation of the framework and testing its generality on building components in various material types other than metals.

Introduction

Four years ago, the hype around Additive Manufacturing (AM) technologies, popularly known as 3D printing, brought attention to the features and benefits these technologies are providing to other industries, like

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Background

To assess the potential worth of the research and determine the availability of data, the authors first developed and conducted the case studies, and then formalized an assessment framework based

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Validation of the Formalized Framework

To understand and quantify the value of the formalized framework presented above, the impact of this semi-automated framework on the effort required for the ASEC analyses was assessed. The speed

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Validation Results - Speed of the Assessment

The analysis of the time required for each assessment step showed that the use of the formalized framework speeds up the assessment by 97.1% in total per case for the

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Validation Results - Consistency of the Assessment Results

The formal, partially automated application of the framework improves the consistency of results for all four ASEC analyses. The use of the framework produced identical A, S and C results

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Conclusion and Future Work

The foreseeable future brings the application of AM technologies to the AEC industry. The case studies of AM building components showed AM’s potential to reduce the environmental impacts over CM

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Acknowledgements

This research was conducted with the support of Stanford University, Center of Integrated Facility Engineering (CIFE) at Stanford, and with the support and in collaboration with the Permasteelisa’s R&D Group.

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