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Enhancing FDM Rapid Prototyping for Industry 4.0 Applications Through Simulation and Optimization Techniques.

Mihalache Ghinea¹, Alex Cosmin Niculescu¹, Bogdan Dragos Rosca¹

¹Department of Robots and Manufacturing Systems, Faculty of Industrial Engineering and Robotics, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania.

Materials (Basel, Switzerland)

|October 16, 2025

Summary

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Upgrading low-cost 3D printers with Klipper firmware and a Raspberry Pi significantly reduces printing time by up to 50%. This enhancement maintains dimensional accuracy and improves surface quality for additive manufacturing applications.

Area of Science:

Additive Manufacturing
Industry 4.0 (Smart Manufacturing)

Background:

Additive manufacturing, a key pillar of Industry 4.0, utilizes technologies like Fused Deposition Modelling (FDM), Stereolithography (SLA), and Selective Laser Sintering (SLS).
Conventional consumer-grade 3D printers face performance limitations.
Klipper v.0.12 is an open-source firmware designed to overcome these limitations.

Purpose of the Study:

Identify and analyze performance bottlenecks in low-cost 3D printers.
Evaluate mitigation strategies using supplementary hardware and software, including Klipper firmware and Raspberry Pi.
Demonstrate significant upgrades to consumer-grade FDM 3D printers.

Main Methods:

Integration of Klipper firmware with a single-board computer (Raspberry Pi) to offload intensive computational tasks.

Keywords:

Klipper firmware additive manufacturing process optimization rapid prototyping

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Incorporation of supplementary hardware (additional sensors) and software (Mainsail interface).

Systematic calibration of the upgraded 3D printer system.

Main Results:

Achieved up to a 50% reduction in printing time.
Maintained dimensional accuracy of printed parts.
Improved the surface quality of the final products.

Conclusions:

Consumer-grade FDM 3D printers can be substantially upgraded through the integration of Klipper firmware, Raspberry Pi, and additional sensors.
This upgrade leads to significant improvements in printing speed, dimensional accuracy, and surface quality.
The study demonstrates a viable method for enhancing the performance and profitability of low-cost additive manufacturing systems.