Optimization of recycled aluminum alloy rolling in an automotive supplier plant: a case study

Thesis Abstract

This study addresses the efficiency, quality, and sustainability challenges associated with rolling recycled aluminum alloy within an automotive supplier environment, where variability in feedstock composition and process settings can degrade final product properties and throughput. The aim is to develop an integrative optimization framework that links feedstock characterization, rolling process parameters, and downstream performance to maximize material utilization, mechanical performance, and energy efficiency while maintaining dimensional accuracy. Specific objectives are (1) to characterize the composition and response variability of recycled Al–Si–Mg alloys sourced from automotive scrap streams; (2) to quantify the effects of hot and cold rolling parameters (temperature, reduction per pass, mill speed, lubrication regime) on microstructure, extrusion, and surface integrity; (3) to evaluate energy consumption and emissions associated with alternative rolling schedules; (4) to develop and validate a multi-objective optimization model that balances product quality, material yield, and energy use; and (5) to propose a robust operational framework for real-time control and quality assurance in the plant. The methodology adopts a mixed-methods sequential design. The population comprises aluminum rolling operations at a tier-one automotive supplier plant with a billet casting and sheet manufacturing line. A stratified sampling approach yields a laboratory-scale pilot set of 180 coupon samples across four rolling passes and three feedstock categories, complemented by shop-floor data from 12 production campaigns totaling 6,000 finished sheets. Data collection employs (i) chemical and microstructural analyses by X-ray fluorescence spectroscopy, scanning electron microscopy, and electron backscatter diffraction to monitor phase distribution and grain size; (ii) mechanical testing including tensile strength, elongation, hardness, and fatigue life; (iii) process monitoring via in-line infrared thermography, torque, and mill current sensors; and (iv) energy and emissions accounting using plant metering data and life-cycle assessment inputs. Instrument validity and reliability are established through calibration curves, pilot-lab repeatability studies (n=30 replicates per condition), and cross-validation with shop-floor records. Data analysis uses regression and ANOVA to quantify parameter effects on product properties, multivariate statistical process control to detect deviations, and a non-dominated sorting genetic algorithm (NSGA-II) for the multi-objective optimization, with constraints reflecting automotive specifications and energy targets. A material-property model links thermomechanical processing to resultant microstructure, informed by the Hall–Péconda solution and constitutive flow equations. A systems dynamics model integrates feedstock variability, rolling operations, and downstream forming to assess end-to-end performance. Ethical considerations address data confidentiality and safety protocols. Expected findings indicate that optimized rolling temperatures and reductions per pass can significantly improve ductility and strength while reducing oxide inclusions in recycled Al–Si–Mg alloys, with energy reductions of up to 12% under optimized schedules. The optimization model is anticipated to identify a Pareto frontier that achieves a 6–10% improvement in material yield and a 5–8% decrease in energy consumption without compromising surface quality or dimensional tolerances. The study is also expected to reveal critical thresholds for recycled alloy variability beyond which process stability deteriorates, necessitating tighter feedstock sorting or adaptive control. Contribution to knowledge includes (i) a validated, plant-relevant framework for optimizing recycled aluminum rolling that integrates material science, process engineering, and sustainability metrics; (ii) empirical relationships between feedstock composition, rolling parameters, and mechanical performance for recycled Al–Si–Mg alloys in automotive applications; (iii) an actionable real-time control architecture and decision-support toolkit for production engineers, incorporating NSGA-II-based recommendations and monitoring dashboards. The study advances understanding of circular material use in precision sheet production and demonstrates how multi-objective optimization can harmonize quality, yield, and energy efficiency under feedstock variability. The main conclusion posits that an integrated optimization of rolling schedules with feedstock characterization can substantially enhance product quality and resource efficiency in recycled aluminum sheet production for automotive applications. Practical recommendations include implementing adaptive rolling schedules guided by in-line chemical sensing, deploying the NSGA-II optimization framework within the plant’s manufacturing execution system, and investing in enhanced scrap pre-processing to reduce variance in alloy composition. Further research should explore real-time machine learning models for soft-sensor estimation of material properties from process signals and extend the framework to other recycled aluminum alloys and downstream forming steps.