On-Machine CIS SoC-Based Layerwise Inspection System for MEX Additive Manufacturing

Resumen

Additive manufacturing processes build three-dimensional objects usually following a layer-upon-layer strategy. An interesting feature of this strategy is that each layer could be inspected before the next one is deposited. On-machine integration of layerwise inspection systems would not only allow for early characterization of the dimensional and geometric quality of the part, but also for the detection of intralayer defects. Contact image sensors (CIS), such as those used in desktop flatbed scanners, could be used for this purpose since they would provide bi-dimensional digital images of the whole layer and its neighborhood. CIS images combine high resolutions with a reduced acquisition time. In this work, a material extrusion (MEX) additive manufacturing system, with layerwise inspection capabilities is proposed. The system has been equipped with the CIS that Epson uses in its Perfection V39 flatbed scanner. The sensor provides two analog output signals, each one consisting on 2584 voltage levels, that represent the amount of light reflected by the material. This analog information is sent to a parallel AD converter, where an 8-bit encoding is assigned to each one of the pixels on the digitized image. To overcome microcontroller-related problems, a Zynq®-7000 system-on-chip (SoC) has been used. This SoC integrates an ARM® based processor, with the hardware programming of a field programmable gate array (FPGA). This architecture ensures an accurate and controlled readout of the various AD converters. The resultant digital image of each layer could then be then processed using different algorithms to detect defects, extract the geometry of the layer contour and characterize the dimensional and geometric quality of the object. In the example provided, a forced error consisting on 0.2 mm height local deviations, caused by a variation in extrusion temperature, was identified from 2D grayscale images obtained with the CIS sensor.