Data-Driven Optimization in Engineering: Integrating Design of Experiments

The AUST Applied Simulation and Modeling Lab (AASML) is pleased to host a technical session titled:

“Data-Driven Optimization in Engineering: Integrating Design of Experiments, Statistical Analysis, and Machine Learning”

The session will be delivered by Asif Karim Khan, Former Research Assistant, Department of Mechanical Engineering, Ahsanullah University of Science and Technology (ME 19th Batch).

In this session, the speaker will share key insights from his published research, including:

• “Strain-rate-dependent optimization of alkali treatment in sugarcane bagasse fibers using ensemble learning and genetic algorithms” (Results in Engineering, Elsevier)

• “An integrated experimental–computational framework for optimizing SLA 3D printing parameters via machine learning and genetic algorithms” (Journal of Materials Research and Technology, Elsevier)

The talk will explore how modern data-driven methodologies can be leveraged to solve complex engineering optimization problems through the integration of:

• Design of Experiments (DoE)

• Taguchi Methods

• Response Surface Methodology (RSM)

• Machine Learning Models

• Genetic Algorithms

Additionally, the session will highlight the importance of statistical validation techniques, including the t-test and Tukey’s Honestly Significant Difference (HSD), in ensuring the reliability and robustness of experimental results.

📅 Event Details

Date: 4 April 2026
Time: 9:30 PM