Quantifying the Impact of Sustainable Product Design Decisions in the Early Design Phase Through Machine Learning

Abstract

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.

A Sustainable Design Repository for Influencing the Eco-Design of New Consumer Products

“…Recently, the SDR has added in increasing designer knowledge in the DfX area of sustainable design through the use machine learning to estimate the environmental impact of a design given a list of product attributes [14].The work presented in this paper has identified shortcomings that have been resolved with future research and continual development of the SDR. Furthermore, and more importantly, the SDR initiation has provided the start of a design repository that can provide valuable and presently relevant design for sustainability information to all designers.…”

“…Connecting Life Cycle Analysis (LCA) indicators to design decisions is a viable way to improve the potential sustainability of a product during the design phase [14]. Introducing the concept of environmental sustainability and LCA data in the early design is of meaningful interest since 70% of product cost and 80% of environmental impact is embedded into a product during the early design phase [15].…”

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

“…As can be inferred from Table 2, only one document 17 meets the four search criteria, one document 13 covers three search criteria. Most of the documents [61][62][63][64]68,70,73] cover two search criteria, the most common being the conjunction of criteria I and II. Only two documents [71,73] meet a single search criterion.…”

“…The design and production of products has been analyzed. For this purpose, LCA was obtained from the data generated in the conceptual design [64] or by analyzing sustainability in manufacturing [65].…”

“…The conceptual design of products [64] or the early design stage of building [71] are a source of data that can be used in the environmental assessment of products [64] or buildings [65].…”