Hybrid additive manufacturing-electroforming for personalized metal parts

This project investigates a hybrid manufacturing approach that combines fused deposition modeling (FDM) of polymer molds with electroforming to produce personalized, high-precision metal components. The process uses low-cost additive manufacturing to create geometrically unique tooling and electroforming to build the final metal part. Multiple unique molds can be printed in parallel and electroformed together, allowing production cost and time per part to decrease even when every component has a different geometry.^[1]

The research also addresses a major limitation of electroforming: surface quality changes as the electrolyte evolves with use. An experimental dataset and machine-learning framework were developed to relate process settings, electrolyte properties, pulse-current behavior, and measured surface quality. This provides a foundation for adaptive parameter selection in evolving electroforming baths.

The project was developed through doctoral research in Mechanical Engineering at Concordia University. It was experimentally demonstrated using personalized copper flexures and a functional X-Y micro-positioning stage.^[2]

Mass production lowers unit cost by spreading tooling and setup expenses across many identical products. Personalized production breaks this relationship because each design can require different tooling, process planning, validation, and inspection. Direct metal additive manufacturing offers geometric freedom but can remain expensive and may require substantial finishing to achieve the required surface quality and dimensional precision.

The central research question was:

Can low-cost polymer additive manufacturing and batch electroforming be combined to produce geometrically unique, high-precision metal components while recovering some of the cost-scaling behavior of mass production?

A second question emerged during process development:

Can measurable bath properties and electrical pulse responses be used to estimate electrolyte condition and predict final surface quality as the bath evolves?

• Develop dissolvable, electrically conductive mold assemblies suitable for electroforming delicate personalized metal structures.
• Demonstrate dimensional capability using micro-hinges and a functional flexure mechanism.
• Evaluate how parallel 3D printing and batch electroforming affect production time and unit cost.
• Build a structured experimental dataset across different electrolyte compositions, aging conditions, and pulse-reverse settings.
• Identify measurable indicators of electrolyte bath state.
• Compare machine-learning models for predicting surface roughness and gloss.
• Establish a pathway toward adaptive, data-driven electroforming control.

The approach separates personalization from metal formation:

1. Personalization through FDM: Each mold is generated from a unique CAD design and printed in a dissolvable polymer.
2. Conductive mold preparation: The polymer mold assembly receives an electrically conductive surface where copper deposition is required.
3. Parallel production: Multiple different molds can be printed simultaneously using several low-cost printers.
4. Batch electroforming: The different mold assemblies can be processed together in a common electroforming stage.
5. Part release: The polymer tooling is dissolved, avoiding mechanical removal that could damage thin flexures or micro-features.
6. Quality monitoring: Bath measurements and electrical pulse-response features are recorded and associated with surface measurements.
7. Data-driven prediction: Machine-learning models estimate roughness and gloss and can support selection of compensating process parameters.

This architecture does not depend on producing identical parts. It seeks cost scaling by standardizing and parallelizing manufacturing operations rather than standardizing product geometry.

The selected mold-fabrication approach was based on directly printing the mask on a conductively coated polymer substrate. In the reported experiments, this method performed better than glued and hot-pressed mold assemblies.^[2]

1. Create the personalized component and corresponding mold geometry in CAD.
2. Print the polymer substrate using FDM.
3. Apply a compatible conductive coating to the deposition region.
4. Print or assemble the polymer mask on the conductive substrate.
5. Seal unintended conductive regions so that metal grows only inside the mold cavity.
6. Connect the conductive mold to the electroforming apparatus.
7. Electroform copper to the specified charge or target thickness using controlled pulse-reverse deposition.
8. Rinse the mold assembly and inspect the deposit.
9. Perform optional in-mold surface finishing when required.
10. Dissolve the ABS mold in a compatible solvent under controlled laboratory conditions.
11. Rinse, dry, and characterize the released metal component.

The method was validated through:

• circular flexure hinges with nominal thicknesses of 140, 170, 200, 230, and 260 micrometres;
• a double-compensated X-Y flexure stage with 150-micrometre hinges; and
• a representative part thickness of approximately 0.75 mm for the X-Y stage.

These demonstrations were selected because delicate flexures can be damaged during conventional mechanical demolding and require controlled dimensional accuracy.

• FDM printer: Ultimaker 2+
• Mold material: black ABS filament
• Nozzle diameter used in the flexure study: 250 micrometres
• CAD and slicing software appropriate to the printer and mold geometry
• Conductive aerosol coating compatible with the polymer substrate

• Copper sulfate-sulfuric acid electrolyte without organic additives
• Temperature-controlled electroforming cell
• Copper counter electrode
• Potentiostat or programmable electrochemical power supply
• Pulse-reverse deposition control
• Electrical-current data acquisition
• Appropriate fixtures, masking materials, wiring, and agitation equipment

• Conductivity meter
• Density or specific-gravity meter
• Temperature measurement
• Surface roughness measurement
• 20-degree and 60-degree gloss measurement
• Microscopy and dimensional inspection as required

This is research documentation, not a complete bill of materials. Exact equipment models, calibration procedures, geometries, chemical volumes, electrical limits, and risk controls should be defined before attempting replication.

The doctoral research constructed an experimental dataset containing:

• 114 electrolyte bath conditions;
• 360 electroplated samples;
• four baseline copper sulfate concentrations: 0.1, 0.3, 0.5, and 0.7 M;
• different bath-use and aging stages;
• pulse-reverse electroforming conditions;
• conductivity and specific-gravity measurements;
• process settings and pulse-current statistics; and
• output measurements including surface roughness and gloss at 20 and 60 degrees.^[1]

The results showed that conductivity or specific gravity alone could not uniquely represent bath condition. Baths with different composition and histories could produce overlapping values. Combining physicochemical measurements with electrical pulse-response features provided a more informative representation of the evolving process.

1. Clean and structure the bath, process, electrical-response, and surface-quality data.
2. Generate statistical features from deposition and polishing pulse-current signals.
3. Compare feature sets based on process parameters, bath measurements, current features, and selected correlated variables.
4. Train and tune regression models using repeated cross-validation and randomized train-test splits.
5. Compare linear regression, k-nearest neighbors, support vector regression, Random Forest, and Gaussian Process Regression.
6. Evaluate predictions for surface roughness and gloss.
7. Test model robustness under changing bath composition and sulfuric-acid concentration.
8. Use the selected model to explore compensating parameter changes for fresh, semi-aged, and aged baths.

Nonlinear models substantially outperformed linear regression for the measured surface properties. Process settings combined with bath descriptors generally provided the most informative feature configuration. Random Forest was selected as the most practical overall model because it provided stable prediction performance, comparatively short training time, limited tuning requirements, and interpretable feature-importance results.^[1]

• Delicate copper micro-hinges were successfully fabricated across the targeted dimensional range.
• A functional double-compensated X-Y micro-positioning stage demonstrated fabrication of a more complex personalized flexure mechanism.
• Directly printing the mold mask on a conductively coated polymer substrate performed better than the glued and hot-pressed alternatives evaluated in the study.
• Dissolving the polymer mold allowed thin metal features to be released without destructive mechanical demolding.

The production model showed that parallel FDM printing followed by batch electroforming could significantly reduce manufacturing time and cost per personalized part. In the modeled 16-printer scenario, total fabrication time per part decreased by approximately 94% relative to a single-printer scenario. Under the study's assumed low-cost-printer and electroforming rates, the estimated manufacturing cost for the personalized X-Y stage fell below CAD 10 per part. These are modeled results based on stated equipment-rate and parallelization assumptions; they are not universal production quotations.^[2]

• Electrolyte condition followed a nonlinear, history-dependent trajectory as baths were used and replenished.
• Static process settings alone were insufficient for reliable surface prediction.
• Pulse-current features encoded information about changing electrochemical behavior.
• Combining process, bath, and electrical-response information improved predictions of roughness and gloss.
• Random Forest offered the best balance of accuracy, robustness, training efficiency, and interpretability for practical implementation.

The thesis and principal publications are publicly accessible or linked below. The research describes the system architecture, experimental methodology, process variables, results, and limitations. However, this Appropedia page should not be presented as a complete open-source hardware release until editable design files, complete code, raw or processed data, a bill of materials, calibration instructions, and a defined hardware/software licence are publicly available.

Current status:

• Public thesis: available
• Peer-reviewed process publications: available
• Experimental validation: completed
• Independent replication: not yet documented
• Editable CAD files: not yet published on this page
• Dataset: not yet published on this page
• Analysis and machine-learning code: not yet published on this page
• Complete bill of materials: not yet published on this page
• Hardware licence: not yet published on this page
• Software licence: not yet published on this page

The project is relevant to distributed and open manufacturing because it investigates how lower-cost polymer printing infrastructure can support production of functional metal components without requiring direct metal additive-manufacturing equipment for every geometry. Potential sustainability benefits include:

• extending the useful manufacturing capability of desktop polymer 3D printers;
• reducing dedicated hard tooling for low-volume personalized parts;
• producing different geometries within the same electroforming batch;
• enabling process monitoring that may reduce rejected parts and unnecessary bath replacement;
• supporting repair, biomedical, research, and specialized low-volume applications; and
• creating a structured experimental foundation for transparent and reproducible process optimization.

These potential benefits require further validation through life-cycle assessment, material-flow analysis, solvent recovery studies, and comparison with alternative manufacturing routes. Electroforming also consumes electricity and chemicals and generates metal-bearing waste, so it should not be described as inherently sustainable without system-level evidence.

This process involves corrosive acids, copper salts, electrical equipment, solvents, and metal-bearing waste. It should only be performed by trained personnel in an appropriately equipped laboratory.

Required controls include:

• a written chemical-risk assessment and review of current safety data sheets;
• acid-resistant gloves, splash goggles, laboratory coat, and task-appropriate face protection;
• compatible secondary containment and clearly labelled chemical storage;
• effective ventilation or a certified fume hood where required;
• electrical isolation and current/voltage limits appropriate to the apparatus;
• temperature monitoring and prevention of unintended short circuits;
• compatible solvent handling and fire-safety controls during ABS dissolution;
• spill-response materials and trained personnel;
• collection of copper-containing electrolyte, rinse water, contaminated consumables, and solvent as regulated hazardous waste; and
• compliance with institutional, municipal, provincial/state, and federal environmental and occupational-safety requirements.

Do not discharge copper-containing solutions or acidic electrolyte into a drain. Do not use household spaces or food-contact equipment for this process.

• Dimensional capability depends strongly on printer resolution, mold design, coating uniformity, sealing quality, and electroforming current distribution.
• Dissolvable molds add chemical-handling and waste-management requirements.
• The reported process was validated primarily for copper and specific flexure geometries.
• Model performance is bounded by the experimental domain represented in the training data.
• Predictions may fail under electrolyte compositions or operating conditions far outside the training range.
• Independent replication has not yet been documented on this page.
• Life-cycle and environmental benefits have not yet been quantified across the complete process.

• Release an anonymized, documented version of the electroforming dataset.
• Publish analysis code and trained-model examples under an appropriate open-source software licence.
• Convert representative mold designs into editable open CAD formats.
• Create a complete bill of materials and calibration protocol for a lower-cost open electroforming platform.
• Test recycled or lower-impact polymer tooling where chemically and dimensionally appropriate.
• Evaluate electrolyte recovery, copper recovery, and closed-loop rinse-water management.
• Perform a comparative techno-economic and life-cycle assessment against CNC machining, wire EDM, and direct metal additive manufacturing.
• Conduct independent replication across laboratories and electroforming systems.
• Extend the method to repair parts, scientific hardware, assistive devices, and low-volume industrial components.

• Aghili, S. (2026). Enabling Cost-Scalable Mass Personalization through Hybrid Additive Manufacturing-Electroforming and Data-Driven Process Modeling. Ph.D. thesis, Concordia University.
• Aghili, S.; Zheng, Z.; Wuthrich, R. (2023). Low-cost manufacturing of high-precision personalized flexures by a hybrid 3D printing-electroforming technique. The International Journal of Advanced Manufacturing Technology, 128, 2333-2346.
• Hamed, H.; Aghili, S.; Wuthrich, R.; Abou-Ziki, J.D. (2024). Electroforming of Personalized Multi-Level and Free-Form Metal Parts Utilizing Fused Deposition Modeling-Manufactured Molds. Micromachines, 15(6), 734.
• Zheng, Z.; Aghili, S.M.; Wuthrich, R. (2022). Towards electroforming of copper net-shape parts on fused deposition modeling (FDM) printed mandrels. The International Journal of Advanced Manufacturing Technology, 122, 1055-1067.
• Mike (SMA) Aghili - Google Scholar

• Additive manufacturing
• Electroforming
• Open-source metal 3-D printer
• Open source hardware
• Distributed recycling and additive manufacturing
• Industry 4.0