Sustainable and Optimized Production in an Aluminum Extrusion Process

In discussions on environmental policies, eco-efficiency is often underlined. Eco-efficiency is defined as delivering products and services with competitive value while simultaneously reducing the ecological impacts and meeting human needs. In highly competitive industrial environments, improvements in production processes are crucial for maintaining a strong differentiated position and competitive ability. Additionally, rationalizing energy consumption and optimizing the use of natural resources are essential for sustainability. This work presents an empirical study of a Portuguese industrial company focused on minimizing scrap production in extrusion processes. This is a common challenge in industrial extrusion processes worldwide, with significant economic and environmental implications. A literature review revealed strong relationships between key extrusion process parameters, including temperature, time, speed, pressure, and geometry. The main objective of this work is to model the aluminum extrusion process in a simple and replicable way, avoiding complex models such as nonlinear optimization or finite element methods, with a view toward potential machine learning applications for scrap reduction. Thus, simple multiple linear regression models enable the identification of the most influential variables involved in the process. The results identify key variables that impact scrap generation, aligning with findings from the literature. In this dataset, geometry-related factors are the parameters with notable scrap rates.