Mining end-of-life materials suitable for material resynthesis and discovering new application domains

The United States generates more than 250 million tons of municipal solid waste (trash/garbage), with only 34% being recycled. In the broader global environment, the problem of waste management is becoming increasingly relevant, demanding innovative solutions. Traditional End-of-Life (EOL) approaches to managing waste include recycle, reuse, remanufacture and disposal. Recently, resynthesis was proposed as an alternative to traditional EOL options that combines multiple products to create a new product distinct from its parent assemblies. Resynthesis employs data mining and natural language processing algorithms to quantify assembly/subassembly combinations suitable for new product combinations. However, existing resynthesis methodologies proposed in the design community have been limited to exploring subassembly combinations, failing to explore potential combinations on a materials level. The authors of this paper propose a material resynthesis methodology that combines the materials of multiple EOL products using conventional manufacturing processes that generate candidate resynthesized materials that satisfy the needs of existing domains/applications. Appropriate applications for a resynthesized material are discovered by comparing the properties of the new material to the functional requirements of application classes which are found using clustering and latent semantic analysis. In the course of this paper, the authors present a case study that demonstrates the feasibility of the proposed material resynthesis methodology in the construction materials domain.