Session: 03-01-01: Advanced Manufacturing in Aerospace Engineering

Paper Number: 165345

Designing Complex Additively Manufactured Parts for Electrochemical Machining: A Physics-Driven and Empirical Approach 

Additive manufacturing (AM) fabricates objects layer by layer, typically utilizing 3D printing technologies. Among its many advantages, AM allows for creating highly complex geometries, lightweight structures, and customized designs often unattainable through conventional manufacturing. However, AM components frequently exhibit inherent surface roughness and porosity, necessitating post-processing techniques to achieve the desired surface quality and precision.

Electrochemical machining (ECM) is a non-traditional, subtractive manufacturing process that removes material atom by atom through anodic dissolution. Unlike conventional machining methods such as milling, turning, or grinding, ECM offers a contactless and stress-free approach that is particularly effective for processing difficult-to-machine materials such as 17-4 PH stainless steel (SS), a widely used alloy in aerospace and biomedical applications. The integration of ECM with AM has gained significant attention due to its potential for precision finishing of complex geometries, making it an advanced post-processing tool for AM-fabricated parts. Several studies have investigated the use of integral, AM-fabricated ECM tools; however, tool extraction post-ECM remains a key challenge, 

This study explores a combined experimental and Multiphysics modeling approach to enhance ECM efficiency in processing Selective Laser Melted (SLM) 17-4 PH SS lattice structures. Specifically, we investigate the relationship between inter-electrode gap (IEG), current density distribution, and mass loss rate (MLR) to develop an empirical framework for optimizing ECM parameters in AM parts. COMSOL Multiphysics simulations were employed to analyze current density variations across different IEG values, while empirical experiments validated the impact of IEG on dissolution rates. Beyond traditional parameter optimization, this research derived AM parts from discrete modeling, scalar field modeling techniques, and parts from von Mises stress analysis to guide lattice structure designs. By mapping current density variations across the anode (workpiece), we establish a method to tailor AM builds with variable densities, ensuring a balanced dissolution rate during ECM. This technique offers a paradigm shift in ECM-AM integration, allowing for field-driven material distribution that aligns with electrochemical processing requirements.

According to initial findings, structured field-driven AM designs greatly improve ECM efficiency by reducing over-corrosion, irregular mass flow, and voltage variations. Moreover, our experimentally derived empirical equations provide predicted information on current density variations, enabling dynamic design adjustment in AM prior to manufacture. Future work will extend this framework to multi-material AM components, further refining process parameters to specify the path for material dissolution, including important rudiments such as surface area, weight, and sequence to specify and drive pattern for dissolution to enhance ECM’s industrial applicability in aerospace, biomedical, and precision engineering.

This research advances the understanding of ECM for additively manufactured metals by bridging the gap between computational predictions and experimental validation. Integrating physics-based simulations and empirical data-driven insights establishes a novel methodology for designing AM parts that are inherently optimized for ECM, paving the way for next-generation manufacturing strategies prioritizing efficiency, precision, and scalability.

Presenting Author: Oluwasegun Ayoola North Carolina State University

Presenting Author Biography: (Oluwasegun) Matthew Ayoola is a PhD student in the mechanical and aerospace engineering department at North Carolina State University, Raleigh, NC, with an undergraduate degree in mechanical engineering, specializing in metal additive manufacturing and electrochemical machining (ECM) for aerospace and defense applications. His work focuses on optimizing anodic dissolution processes through advanced computational simulations and experimental methodologies.

Authors:

Oluwasegun Ayoola North Carolina State University
Arnab Chatterjee North Carolina State University
Rajeev Gupta North Carolina State University
Timothy Horn North Carolina State University