Background
This thesis is positioned at the intersection of design and engineering perspectives in material selection and adoption. In response to the transition toward renewable resource-based materials and the challenges of bio-based material adoption before the pilot test phase, this research aims to advance their selection and adoption by developing a novel material selection tool that combines technical and experiential perspectives to uncover their full potential.
Design question
How might designing a material selection tool incorporating technical properties and a materials experience approach facilitate bio-based materials decision-making and adoption in DIY material approach and lab-scale for design and engineering practitioners in product development?
Research question
How can decision-making and the adoption of bio-based materials in product development be facilitated at the production, DIY, and lab scales to support Sustainable Development Goal 12 (responsible consumption and production) by integrating technical (tangible) and experiential (intangible) properties to understand material qualities better and advance their adoption beyond the pilot test scale?
Over the last 30 years, the growing demand for plastics has led to excessive waste accumulation, polluting oceans, rivers, and landfills, with severe consequences for the environment and human health (Bilo et al., 2018; Magalhães et al., 2020; Melchor-Martínez et al., 2022). Renewable, bio-based materials have emerged as promising alternatives to mitigate this crisis (Spierling et al., 2018). These materials have gained interest among engineers, designers, and manufacturers as substitutes for petroleum-based plastics. They could potentially aid in the transition toward sustainable goals aligned with the United Nations Sustainable Development Goals (SDGs), particularly Goal 12 (responsible consumption and production), and contribute to Goals 11, 13, 14, and 15.
Despite their potential, bio-based materials face barriers such as technical limitations, higher costs, and negative perceptions of durability and scalability (Bos et al., 2024; Melchor-Martínez et al., 2022). This perception is often rooted in conventional material selection methods, which inadequately capture the unique qualities of emerging materials due to limited data and the lack of more inclusive evaluation criteria. To address these issues, material selection processes must integrate diverse criteria reflecting the complexity of emerging materials.
Multidisciplinary collaboration among designers, engineers, manufacturers, and other stakeholders in material selection and adoption presents a significant opportunity to tackle plastic pollution and accelerate the shift from petroleum-based materials to renewable alternatives, and advance a circular economy (CE) (Korhonen et al., 2018; Kovacic et al., 2019).
Material Selection
Material selection is an interdisciplinary effort that combines social, economic, and environmental domains. Materials pass through different disciplinary communities in their journey from laboratory to marketplace, influencing how we interact with them through products (Veelaert et al., 2016; Wilkes et al., 2016). Consequently, technical, sensorial, and intangible aspects must all be considered. In other words, properties from the different levels shown in Figure 3 must be considered to increase the adoption of bio-based materials.
Material Selection Perspectives
Addressing material sustainability in academic and industrial contexts often reveals a narrow focus on specific technical or experiential perspectives, overlooking alternative approaches to sustainable material selection and application (Italia et al., 2023). Contemporary material selection in product development encompasses various factors, such as technical aspects, material perception and meaning, criticality and material sourcing, and cost (Henriksson, 2021). Two primary groups of material characterization, technical and experiential properties, facilitate decision-making and informed selections by designers and engineers at the lab scale.
Technical selection
Established material selection methods follow an analytical approach, which can be expressed in the form of knowledge-based approaches (Porsch et al., 2019), simulation-based approaches (Wu & Liu, 2014; Cho et al., 2013), or more classical analytical approaches (Xia et al., 2014), which consider characteristics such as design requirements, materials in products, technical and mechanical properties.
Design-based selection
In addition to meeting technical requirements, materials integrated or applied into a product must also appeal to human senses and convey intended meanings (Karana et al., 2010). These concerns have been incorporated into design methodologies, such as design for experience (Schifferstein & Hekkert, 2007), design for emotions (Desmet, 2002), and multi-sensory design (Schifferstein & Spence, 2008). Integrating these perspectives enables designers to create products that evoke specific experiences through tangible and intangible material characteristics.
This difference in perspective impacts the economics of material selection. Limited technical and experiential properties integration in material selection has been made to understand the material's complexity. This disciplinary disconnection hinders evaluating the full potential of bio-based materials, particularly those still in development.
Approach challenges and opportunities
Challenges
A major constraint in integrating technical and experiential perspectives is data representation variability, which can range from numeric metrics to anecdotal insights from prior applications used to present material properties and attributes.
Opportunities
With the two perspectives – technical and design-based – in material selection, there are several opportunities to explore different material selection tools that characterize materials by an integration of the perspectives and evaluate the impact on emerging materials—alternatively, a new framework to overcome the challenges of experiential material characterization to improve data uncertainty. Another consideration is the impact of this approach on decision-making and when it should be implemented in the product development process.