Hybrid artificial neural network and genetic algorithm-based grey relational analysis for bi-objective optimization of 3D-printed polylactic acid parts

Key Points

• This optimization approach improves the performance of 3D-printed parts, reducing defects while maximizing utility.
• Achieving enhanced results, the procedure notably improves dimensional accuracy by 30%, crucial for effective product quality.
• Analysis employing a hybrid artificial neural network and genetic algorithm refined the production parameters for polylactic acid parts.
• The findings support advanced manufacturing realms, highlighting the need for comprehensive optimization techniques.