Flatbed scanners (FBSs) provide non-contact scanning capabilities that could be used for the on-machine verification of layer contours in additive manufacturing (AM) processes. Layer-wise contour deviation assessment could be critical for dimensional and geometrical quality improvement of AM parts, because it would allow for close-loop error compensation strategies. Nevertheless, contour characterisation feasibility faces many challenges, such as image distortion compensation or edge detection quality. The present work evaluates the influence of image processing and layer-to-background contrast characteristics upon contour reconstruction quality, under a metrological perspective. Considered factors include noise filtering, edge detection algorithms, and threshold levels, whereas the distance between the target layer and the background is used to generate different contrast scenarios. Completeness of contour reconstruction is evaluated by means of a coverage factor, whereas its accuracy is determined by comparison with a reference contour digitised in a coordinate measuring machine. Results show that a reliable contour characterisation can be achieved by means of a precise adjustment of image processing parameters under low layer-to-background contrast variability. Conversely, under anisotropic contrast conditions, the quality of contour reconstruction severely drops, and the compromise between coverage and accuracy becomes unbalanced. These findings indicate that FBS-based characterisation of AM layers will demand developing strategies that minimise the influence of anisotropy in layer-to-background contrast.

Structured light-based 3D scanning systems are used for non-contact digitization of complex surfaces, with applications in quality control, reverse engineering, and biomedical engineering. The availability of low-cost sensors has expanded access to high-resolution data capture, enabling their use in applications with moderate accuracy requirements, such as capturing anatomical details for custom prostheses and diagnostics. However, the reliability of these low-cost systems for accurate surface geometry reconstruction remains uncertain. This study evaluates the performance of Apple’s TrueDepth sensor, focusing on the impact of stand-off distance variations on geometric reconstruction accuracy. Test specimens including planar, parallel planes, and hemispheric geometries were digitized using a semi-automatic test bench, and compared with reference values obtained with a coordinate measurement machine (CMM). The resulting point clouds were processed to evaluate the reliability of dimensional and geometric measurements. Key findings include significant point cloud dispersion as the sensor-to-object distance increases; i.e., the standard deviation of point-to-plane distances rose from 0.291 to 0.739 mm as the sensor-to-object distance increased from 175 to 450 mm. Similarly, the standard deviation of point-to-sphere distances range from 0.265 to 0.785 mm for the same stand-off interval. However, measurements of features derived from average values benefit from the statistical compensation of errors due to the Gaussian distribution of point dispersion. Thus, distance between planes error ranges from 0.053 to 0.355 mm within the experimental limits. These results suggest that TrueDepth may be a suitable option for fields like biomedicine, where precision requirements are less strict than those in industrial applications.

Surface quality in material extrusion of polymers (MEX/P) plays a critical role in determining the functional performance of printed parts, especially in components that must work together and in sectors such as medical and aerospace. Predicting surface quality before printing shall be therefore considered a convenient capacity. Nevertheless, while many studies suggest that surface roughness can be reliably predicted based on process parameters and material properties, some research indicates that variations in equipment and material quality may introduce significant discrepancies, leading to less accurate predictions. This work aims to challenge the assumption that results obtained from one system can be universally applied to others and to emphasise the necessity of machine-specific calibration and validation in both academic research and industrial applications. To achieve this, the study evaluates the consistency of surface quality results across three distinct material extrusion (MEX/P) machines under identical process conditions. The machines tested include a low-cost consumer-grade printer, a semi-professional system and a custom-built Test Bench printer. Polylactic acid (PLA) test specimens were fabricated using various combinations of layer height and strand width. The resulting surfaces were digitised with a laser triangulation sensor (LTS), and surface roughness ( $${R}_{a}$$ R a ) and waviness ( $${W}_{a}$$ W a ) were measured for multiple profiles. Statistical analysis was conducted to assess machine-to-machine variations under constant process parameters. The results challenge the notion that surface quality predictions can be generalised across different systems, underscoring the need for machine-specific parameter optimisation in additive manufacturing.

Material Extrusion (MEX) currently stands as the most widespread Additive Manufacturing (AM) process, but part quality deficiencies remain a barrier to its generalized industrial adoption. Quality control in MEX is a complex task as extrusion performance impacts the consistency of mechanical properties and the surface finish, dimensional accuracy, and geometric precision of manufactured parts. Recognizing the need for early-stage process monitoring, this study explores the potential of integrating Laser Triangulation Sensors (LTS) into MEX/P manufacturing equipment for layer-wise 3D inspections. Using a double-bridge architecture, an LTS-based sub-micrometric inspection system operates independently from the manufacturing process, enabling comprehensive digitization and autonomous reconstruction of the target layer’s topography. Surface texture is then computed using standardized indicators and a new approach that provides insight into layer quality uniformity. A case study evaluating two alternative extruder head designs demonstrates the efficacy of this integrated approach for layer quality characterization. Implementing a generalized layer-wise procedure based on this integration can significantly mitigate quality issues in MEX manufacturing and optimize process parameter configurations for enhanced performance.

Additive manufacturing (AM) has faced challenges in achieving the part accuracy required for industrial adoption. Most research has focused on offline inspection of geometric deviations in test parts. This paper introduces a novel approach to enhance part accuracy and process control in Polymer Extrusion AM (MEX/P) by integrating in-situ digitising and compensation systems. A custom 3D-printing test bench was developed for this purpose, incorporating a laser-stripe sensor for real-time measurement of layer contours during the printing process. The paper details the integration procedure, including sensor installation, calibration, and validation test. To validate the system accuracy, irregular-shaped test parts were scanned using the integrated sensor and compared to ground truth measurements from a Coordinate Measuring Machine. The results demonstrated close agreement between the two measurement methods. The methodology was applied to a case study, wherein digitised layer contours were recognised and their geometric deviations from the nominal contours were measured and compensated for. This process also accounted for the thermal shrinkage experienced by the part during cooling. This resulted in a significant improvement in part quality, with virtually eliminated average layer contour deviations and a reduction in error dispersion by over 50%. The proposed methodology, validated for parts with uniform complex cross-sections, can be extended to the production of serial components with any layer shape including both outer and inner contours.

This work presents a fused-filament fabrication (FFF) hot end that combines an unrestricted-rotation C-axis with a rectangular-slot nozzle and an induction-heated melt sleeve. The architecture replaces the popular resistive cartridge and heater block design with an external coil that induces eddy-current heating in a thin-walled sleeve, threaded to the heat break and nozzle, reducing thermal mass and eliminating wired sensors across the rotating interface. A contactless infrared thermometer targets the nozzle tip; the temperature is regulated by frequency-modulating the inverter around resonance, yielding stable control. The hot end incorporates an LPBF-manufactured nozzle, which transitions from a circular inlet to a rectangular outlet to deposit broad, low-profile strands at constant layer height while preserving lateral resolution. The concept is validated on a desktop Cartesian platform retrofitted to coordinate yaw with XY motion. A twin-printer testbed compares the proposed hot end against a stock cartridge-heated system under matched materials and environments. With PLA, the induction-heated, rotating hot end enables printing at 170 °C with defect-free flow and delivers substantial reductions in job time (22–49%) and energy per part (9–39%). These results indicate that the proposed approach is a viable route to higher-throughput, lower-specific-energy material extrusion.