As more companies and researchers become interested in understanding the relationship between product design decisions and eventual environmental impact, proposed methods have explored meeting this demand. However, there are currently limited methods available for use in the early design phase to help quantify the environmental impact of making design decisions. Current methods, primarily vetted Life Cycle Assessment (LCA) methods, require the designer to wait until later in the design phase, when a product’s design is more defined; alternatively, designers are resigned to relying on prior sustainable design experience and empirical knowledge. There is a clear need to develop methods that quantitatively inform designers of the environmental impact of design decisions during the early design phase (particularly during concept generation), as this allows for reexamination of decisions before they become costly or time-intensive to change. The current work builds on previous research involving the development of a search tree of sustainable design knowledge, which, applied during the early design phase, helps designers hone in on the impact of product design decisions. To assist in quantifying the impact of these design decisions, the current work explores the development of a weighting system associated with each potential design decision. The work presented in this paper aims to quantify the general environmental impact potential design decisions have on a consumer product, by using a multi-layer perceptron neural network with back propagation training — a method of machine learning — to relate the life-cycle assessment impact of 37 case study products to product attributes. By defining the relationship between LCA data and product attributes, designers in the early design phase will be more informed of which product attributes have the largest environmental impact, such that the designer can redesign the product to have reduce this impact.

The objective of this research is to support DfX considerations in the early phases of design. In order to do conduct DfX, designers need access to pertinent downstream knowledge that is keyed to early stage design activities and problem knowledge. Product functionality is one such “key” connection between early understanding of the design problem and component choices which dictate product performance and impact, and repositories of design knowledge are one way to archive such design knowledge. However, curation of design knowledge is often a time-consuming activity requiring expertise in product modeling. In this paper, we explore a method to automate the populating of design repositories to support the overall goal of having up-to-date repositories of product design knowledge. To do this, we mine information from an existing repository to better understand the relationships between the components, functions, and flows of products. The resulting knowledge can be applied to automate functional decompositions once a product's components have been entered and thus reliably provide that “key” between early design activities and the later, component dependent characteristics.

Engineering designers are constantly seeking ways to be more innovative, decisive, and informed of emerging technologies in the design of consumer products. Design tools, such as functional decomposition, morphology, and Pugh charts help stimulate the design process. However, many earlydesign-phase design tools require designers to have experiential or empirical design knowledge; many of these approaches are intractable for use by novice designers or designers with little experience designing for certain new objectives. In contrast to these current tools, using repositories to store product design information can provide additional and extensive design knowledge to the global design community. Using repository data-and resultant data-driven design approaches-in the design of new products can be especially impactful for DfX design objectives such as product sustainability, about which many engineering designers have limited knowledge. In this paper, we discuss the creation of a sustainable design repository -a collection of product data that includes environmental impact information. Through the initialization of a 47-product repository case study, we seek to create data-driven design processes that can influence designers to consider environmental sustainability. We found, for example, that in the first year of a product's life, 29-64% of the environmental impact occurs during the product's use phase, and that uncertainty in input data (such as component manufacturing location and disposal method) can significantly contribute to environmental impact variation. The creation of this sustainable design repository highlights the need for the consideration of input uncertainties when conducting environmental impact analysis. Additionally, the repository has also been used in tandem with machine learning to understand design decisions that lead to more sustainable products. This sustainable design repository enables subsequent data-driven design research in that it provides a large dataset on which machine learning approaches can operate.

Global concerns about climate change and resource management have escalated the need for sustainable consumer products. In light of this, sustainable design methodologies that supplement the product design process are needed. Current research focuses on developing sustainable design curricula, adapting classical design methods to accommodate environmental sustainability, and sustainability tools that are applicable during the early design phase. However, concurrent work suggests that sustainability-marketed and innovative products still lack a reduction of environmental impact compared to conventional products. Life cycle assessment (LCA) has proven to be an exceptional tool used to assess the environmental impact of a realized product. However, LCA is a reactive tool that does not proactively reduce the environmental impact of novel product concepts. Here we develop a novel methodology, the PeeP method, using historical product LCA data with kernel density estimation to provide an estimated environmental impact range for a given product design. The PeeP method is tested using a series of case studies exploring four different products. Results suggest that probability density estimations developed through this method reflect the environmental impact of the product at both the product and component level. In the context of sustainable design research, the PeeP method is a viable methodology for assessing product design environmental impact prior to product realization. Our methodology can allow designers to identify high-impact components and reduce the cost of product redesign in practice.

The fuzzy front end of engineering design can present a difficult challenge, and as such, recent engineering design research has focused on guiding and influencing the way a designer ideates. Early ideation can be especially difficult when attempting to integrate specific design objectives in product design, called Design for X (DfX). This paper presents two experiments exploring the efficacy of a structured Design for the Environment (DfE) design method called the GREEn Quiz (Guidelines and Regulations for Early design for the Environment) that provides designers with sustainable design knowledge during the conceptual design phase. The GREEn Quiz operates on a web-based platform and queries the designer about their design concepts; an end-of-quiz report provides abstract DfE knowledge to designers. While this abstract knowledge was able to be applied by designers in a former study, we hypothesize that providing targeted, specific design strategies during conceptual design will enable novice designers to better integrate DfE. In this study, we created these DfE strategies, integrated these into the GREEn Quiz, and studied the efficacy of these strategies when presented to designers at both the expert and novice levels. Results suggest that respondents with access to the strategy-based GREEn Quiz produced concepts with evidence of more sustainable design decisions and higher solution quality scores. This work shows the promise of supplemental Design for the Environment methods for concept generation to enable the design of more environmentally sustainable products.