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.

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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.

The School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University is home to one of the largest academic Mechanical Design groups in the country. As a leader in undergraduate design education, we have been able to keep in touch with a large group of mechanical design graduates, and as such are capable of assessing how students retain information learned in undergraduate coursework to see how this understanding is employed in real-world engineering practice. However, the principles governing the design of sustainable products and processes are relatively novel and are only now being integrated into the undergraduate and graduate mechanical design curriculum. It is our hypothesis that particular means of learning and understanding sustainable design — via lectures, homework assignments, design projects, and the use of various sustainability-related LCA tools — will enable the highest retention of sustainable design understanding, and a higher likelihood that this sustainable design knowledge will be propagated into design practice in industry. Multiple curricular studies that explore dissemination and retention of sustainable design skills are being explored, including a junior-level introductory mechanical design course and a graduate level sustainable product development course. In the junior-level course, baseline sustainability knowledge is tested by allowing students to make sustainable design decisions by applying varied skill sets, including general principles, a list of sustainable design guidelines, and an innovative online survey (The GREEn Quiz). The graduate-level course, which employs sustainable design principles within a larger product development architecture, will capitalize on more “expert” knowledge. Future work will also be discussed, including planned validation studies and curriculum improvements, as well as the means of quantifying the retention of sustainable design information.