In Situ Error Correction for 3D Printed Objects with Integrated Electronics on 5-Axis Printers

Abstract

3D printing has altered manufacturing by enabling the production of complex objects. The emerging field of printed electronics makes it possible to integrate functional parts with embedded electronics. The full potential of combining 3D printing and printed electronics is unlocked by 5-axis printing. This technique allows circuits to be routed along the surfaces of complex objects. This thesis presents a method to reliably print electronics, correct upcoming errors during printing, and generate a digital twin of the printed object, all achieved on low-cost hardware. 5-axis printing on low-cost printers is challenging due to misalignments in the rotary axes. This challenge is amplified for printed electronics that require a constant distance to the surface and continuous deposition along the path. To address this issue, this work presents a software pipeline that measures the misalignments of the printer's rotary axes and incorporates them into a URDF based model of the printer. An IK solver uses this model to generate compensated toolpaths that account for these deviations. To achieve continuous deposition, surface normals extracted from the underlying object are incrementally adapted along the path. This enables reliable deposition of printed electronic wires onto arbitrary surfaces using imprecise low-cost hardware. Even with a well calibrated system, errors can still occur during printing, and printed electronics are highly sensitive to these faults. A small imperfection in a wire can make the circuit nonfunctional and cause failure of the entire object. This work presents a method for in itu error detection and repair to address this problem. A neural network segments the wires in images captured during the printing process. By comparing the segmented wires with the intended toolpath, defects are identified. From the identified defects, repair toolpaths are generated to fix them. This results in more reliable electronics with known circuit properties. For structural objects, reliability of the printing process is also crucial, especially in critical industries such as medical or aerospace. The first step toward quality control and certification is the creation of a digital twin of the printed object. Each printed layer is reconstructed by segmentation with a neural network using two inputs: an image of the current layer and an image of the previous layer. By stacking these individual layer segmentations, a 3D reconstruction of the printed object is created. The reconstruction archives high precision with a resolution of 12µm per pixel and a mean geometric deviation of 61.5µm. This digital twin is accurate enough to enable future quality inspection, adjust printing parameters, and serve as a basis for certification. In conclusion, the methods proposed in this thesis enable 5-axis printing of objects with embedded electronics on low-cost hardware, ensure the reliability of the printed electronics, and generate accurate reconstructions of structural objects.