At Corrdesa, we focus on electrochemical applications, including not only experimental and material characterization, but also advancing computational techniques and workflows to improve designs and reduce corrosion risks. In our latest work, we introduced a major improvement to ECM (Electrochemical Machining) simulation, maintaining stability and accuracy even under large grid deformations while significantly reducing computation time. These advances enable engineers to run longer, more realistic ECM simulations with greater accuracy and consistent results.
What is Electrochemical Machining (ECM)?
ECM is a non-traditional process that removes material via electrolytic dissolution. It applies direct current between the tool (cathode) and workpiece (anode), with electrolyte flowing through the gap to complete the circuit. As the tool approaches, material dissolves, shaping the workpiece to the tool’s contours; Figure 1 shows a pictorial representation of the process and highlights the important challenges. ECM is ideal for machining hard alloys or complex parts without thermomechanical stress or tool wear, making it popular in aerospace, automotive, and medical manufacturing.
Physical ECM Prototyping: Limitations and the Role of Simulation
Traditional electrochemical machining prototyping is often slow, costly, and relies heavily on trial and error. As part designs become more complex, physical testing alone is no longer efficient or practical. Simulation provides a powerful alternative—enabling engineers to optimize process parameters, refine tool and workpiece geometries, and predict defects such as overcut or uneven material removal before machining begins. Yet, current CFD (Computational Fluid Dynamics) models often struggle with large computational grid deformations or extended machining operations, highlighting the need for more reliable, robust and adaptable computational methods.
The Missing Pieces in ECM Simulation
Despite progress in modeling, many ECM simulation approaches still face key limitations. Existing models often overcomplicate physics yet fail to capture essential process behaviors such as anodic dissolution and tool movement. In particular, robust and adaptive remeshing remains a major challenge—without it, simulations become unstable during large deformations or over long machining periods.
Our latest work directly addresses these limitations by developing a unified, automated workflow that improves mesh handling, solver stability, and computational efficiency. The table below summarizes our approach and the significance of the proposed solution.
Table 1. State of the art for CFD workflows.
| Feature | Previous Works | This Work |
| Solver-Driven Remeshing | Single grid quality metric | Multi grid quality metrics and surface curvature |
| Mesh Handling | Manual, frequent remeshing | Fully automatic |
| Time-Step & URF | Manual tuning | Adaptive, automated |
| Electrochemistry–Flow Coupling | Fully coupled | Decoupled approach |
| Computational Efficiency | Medium/Low | High |
Breakthrough: Our Hybrid Workflow (2023)
In 2023, our team achieved a major milestone by developing a hybrid computational workflow for modeling Pulsed Electrochemical Machining (PECM). The approach combined real-time mesh quality monitoring with mesh morphing, allowing precise tracking of geometry changes during large-deformation machining. We also modeled anodic dissolution as a vector field, providing a more realistic and detailed view of the material removal process.
The workflow utilized Simcenter STAR-CCM+ for the CFD and electrochemical simulations and Fidelity Pointwise for advanced meshing, allowing the system to remain robust under large deformations and extended machining durations. Importantly, the model was validated against experimental results, confirming its accuracy and predictive reliability.
While this hybrid solution proved highly effective, it required multiple software packages and complex data exchange between them. This motivated the development of our new unified, single-solver workflow, which eliminates these dependencies while maintaining the same level of accuracy and stability.
Watch [Video 1] to see the die-sinking motion in action, demonstrating our hybrid PECM workflow maintains precision and stability under large deformations and long machining times (tool travel distance = 21.7 mm)
Read the full publication: https://doi.org/10.2514/6.2023-3435
A Unified and Efficient Computational Workflow
In spite of the efficient and accurate solution proposed, we developed a unified computational framework that addresses long-standing challenges in ECM simulation by utilizing a single CFD solver—Simcenter STAR-CCM+. This fully integrated approach eliminates the need for external meshing tools or hybrid coupling, streamlining the simulation process and improving robustness, automation, and accuracy.
The workflow is fully implemented in Simcenter STAR-CCM+ and operates through a fully automated process that uses real-time mesh quality checks, a variable time-stepping scheme to prevent mesh overstretching, and adaptive mesh refinement (AMR) based on the Laplacian of the Electric Current Density field. These two new capabilities increase the robustness of the new computational tool since it adjusts computation parameters on the fly based on the solution field. For example, it increases the URF (Under-Relaxation Factor) when the linear solver does not meet the established tolerance, while we reduce the time step when we find large deformation relative to the active area of the workpiece. In other words, we go fast when the computation is stable, but we tend to go slower as we start to see evidence of instability.
Unlike the general remeshing feature in STAR-CCM+, which re-tessellates the entire computational domain, our unified workflow focuses exclusively on the deformed (machined) workpiece, while maintaining the native CAD representation of the tool. Figure 3 shows the cross-sectional view from the numerical prediction of the workpiece and tool configurations after 60 minutes of ECM. It compares three approaches—the Unified Workflow, the Hybrid Workflow, and the standard General Remeshing model in STAR-CCM+. The prediction on the far right represents the solution obtained with the general remeshing technique (Out-Of-The-Box) in Simcenter Star-CCM+, highlighting the main limitation due to repeated re-tessellation. Video 2 illustrates this behavior, the observable changes in tool geometry caused by frequent re-tessellation during the spiral motion. While the solution on the far left is the latest workflow, “Unified”, the one in the center represents our previous effort with Fidelity Pointwise + Simcenter Star-CCM+. Relative to the experimental data, we found the largest deviation to between 70-80 microns.
As shown, the new strategy is not only faster but also retains the positive properties of the previously validated computational workflow. Therefore, we ensure accurate tool geometry throughout each remeshing cycle and eliminate the artificial tool deformation that is a common limitation in conventional ECM simulations.
ECM Simulation Using “Frozen Flow” Approach
In our study, we demonstrated that electrochemical effects driven by anodic dissolution dominate the flow dynamics, outweighing the influence of Joule heating and turbulence. This produces a solution that is periodic in the flow direction, confirming that the Pulsed Electrochemical Machining (PECM) or “Frozen Flow” approach effectively captures the key physical mechanisms governing ECM. However, this strategy is most effective when flow channels and the cathode holder are carefully designed to minimize recirculation and flow separation. Doing so allows us to decouple electromagnetic transport from the Navier–Stokes equations, greatly simplifying and accelerating the computational modeling process.
In the proposed solution, we first start by adopting a “Flow-only Evaluation” stage. In this step, we solve only the RANS equations to assess the flow features and identify regions with large recirculation and separation. The idea is to resolve or minimize recirculation, stagnation, and uneven velocity distributions—factors that can reduce machining uniformity.
Once the flow is optimized, the Unified Workflow stage integrates electrochemical effects, including potential distribution, current density, and anodic dissolution. This approach maintains stability and accuracy while simplifying the coupling between flow and electrochemical models, enabling faster and more realistic ECM simulations.
Validate “Frozen Flow” Approach
We implemented this workflow on a spiral motion configuration, with detailed simulations using a single-cutting cell assembly designed for helical groove machining under controlled conditions, refer to Figure 4.
We began with a flow-only evaluation stage, where we analyzed and optimized the system to minimize recirculation and stagnation. The predicted flow features are shown in Figure 5.
In the second stage, we simulated the machining process using the unified workflow. The predicted profiles closely aligned (shown in Figure 6) with the expected tool motion and material-removal behavior. Cross-sectional results accurately captured the land and groove geometry at multiple tool positions, showing the model remains stable and precise even under large deformations. This validated workflow provides engineers a powerful, efficient tool for optimizing tool design, electrolyte flow, and flushing strategies within a single, integrated CFD environment.
Impact on Industry Applications
This research effort provides a powerful new tool for industries that rely on precision ECM. By streamlining simulation workflows and enhancing predictive accuracy, it enables engineers to optimize processes, refine tool designs, and improve electrolyte flow and flushing strategies—all within a single, integrated CFD environment.
This validation demonstrates that our fully integrated STAR-CCM+ framework can accurately simulate complex ECM processes without the need for external meshing tools or manual intervention, paving the way for more efficient, automated design and optimization of industrial ECM applications.
Want to learn more? Download the full study or reach out to us at Contact page. We’d be happy to answer your questions and show how our simulation expertise can support your projects.
Authors: Bashir Alnajar, and Julio Mendez.