An artificial neural network tool to support the decision making of designers for environmentally conscious product development

The consideration of sustainability aspects in initial stages of product development is understood as an effective approach to plan a sustainable product life cycle. It can be achieved by integrating the sustainability consider-ations into decision making of companies in early design phases. This study aims to develop an Artificial In-telligence (AI) tool that can assist the designers in their decision making to choose environmentally benign design parameters of products. The proposed tool is based on an Artificial Neural Network (ANN) model which takes the life cycle design parameters (viz. size of product, density of material, manufacturing process, transport mode and recyclability) as inputs and provides the corresponding outputs in terms of 'carbon footprint' and 'life cycle cost' of a product. These outputs assist the designers to realize the trade-off between the environmental load and cost effectiveness of a design alternative. Thus, it enables the decision making of companies to select a more sus-tainable design. A Graphical User Interface (GUI) is developed for the AI tool so that the designers can efficiently use this tool. The results predicted by the proposed tool are compared with the results obtained through the life cycle assessment carried out by using GaBi 9.2. The comparison shows that the results predicted by the tool have a reasonable accuracy of more than 90% which is significant, especially in the design stages of environmentally conscious product development. Also, the time efficiency of the tool to compute the environmental impact was compared with that of GaBi 9.2 by using a T-test. Results showed that the time efficiency of the proposed AI tool is significantly higher than that of GaBi 9.2.

Automated summary

This study aims to develop an AI tool to assist designers in choosing environmentally friendly design parameters. The tool is based on an ANN model and predicts the carbon footprint and life cycle cost of a product. The tool has a high accuracy of over 90% and is significantly more time-efficient than other assessment tools.