A Self-Organizing Evolutionary Method to Model and Optimize Correlated Multiresponse Metrics for Additive Manufacturing Processes

Abstract

Abstract The use of robust multiresponse constrained optimization techniques in which multiple-objective responses are involved is becoming a crucial part in additive manufacturing (AM) processes. Common and popular techniques, in most cases, rely on the assumption of independent responses. In practice, however, many of the desired quality characteristics can be correlated. In this work, we propose a technique based on three ingredients: hybrid self-organizing (HSO) method, desirability function (DF), and evolutionary algorithms to analyze, model, and optimize the multiple correlated responses for the fused deposition modeling (FDM) process, one of the most popular AM technologies. The multiobjective functions are formulated by employing the HSO method and DF, where structural integrity and process efficiency metrics are considered for the data-driven correlated multiresponse models. Subsequently, layer thickness, nozzle temperature, printing speed, and raster angles are taken as process parameters (decision variables). The operational settings and capabilities for the FDM machine are defined as boundary constraints. Different EA algorithms, the nondominated sorting genetic algorithm, and the multiobjective particle swarm optimization method, are then deployed to model the AM criteria accordingly to extract the Pareto-front curve for the correlated multiresponse functions. FDM experimental design and data collection for the proposed method are provided and used to validate our approach. This study sheds light on formulating robust and efficient data-driven modeling and optimizations for AM processes.