This study aims to explore the impact of various infill patterns on the mechanical properties of 3D-printed polylactic acid (PLA) specimens, particularly focusing on the minimum cross-sectional area, which correlates with mechanical strength. A randomized controlled trial design was employed, testing four different infill patterns: concentric, gyroid, 3D honeycomb, and rays. Each pattern was printed in PLA using a standard material extrusion 3D printer. The specimens were subjected to tensile stress using a universal testing machine, following the ASTM D638-14 standard. The cross-sectional area at gauge length was calculated using the line-plane intersection method from GCODE analysis, highlighting the minimum cross-sectional area: the weakest sections theoretically prone to failure. This work involved testing specimens with varying infill patterns to determine their influence on the mechanical integrity and performance of the parts. The concentric infill pattern exhibited the highest relative line-plane intersection points (RLPI) and maintained minimal variability in mechanical properties across the sample size. Experimental results demonstrated that different infill patterns significantly affect tensile strength, with the concentric pattern providing the most favorable outcomes in terms of strength and reliability. The choice of infill pattern in material extrusion 3D printing of PLA significantly influences the mechanical properties, particularly the tensile strength and distribution of material within the cross-sectional area. The concentric pattern consistently outperformed other types in maintaining structural integrity under stress. These findings provide crucial insights for optimizing 3D printing settings to enhance the durability and performance of printed parts.

An image acquisition and processing system to measure the width and inspect the quality of the stainless steel strip in a production line is presented in this paper. It is based on the real-time processing of the images acquired by a twin linear camera system. Image processing algorithms to detect defects and anomalies in the edges have been implemented. The core of the image processing is submitted to the GPU of the graphic card to reduce the total processing time. A system like this has been proved and installed in a stainless steel production line for quality control purposes.

In this paper we present an image processing algorithm to detect and measure the amount of residual oxide remaining on the surface of stainless steel coils for quality control in a production line. This algorithm processes in real time the images taken by the acquisition system which we have designed for this purpose and which has been installed in the finish line of a stainless steel production factory. We present here a more robust and reliable algorithm than the initial one, which has been adapted to deal with non-ideal conditions such as non-perfect homogeneous lighting, different surface finish, water marks, etc., which usually occur in practice.

Fused deposition modeling (FDM) is one of the most widely adopted additive manufacturing (AM) technologies due to its accessibility and versatility; however, ensuring process reliability and product quality remains a significant challenge. This work introduces a novel methodology to evaluate the fidelity of programmed extruder head trajectories and speeds against those executed during the printing process. The approach integrates infrared thermography and image processing. A type-V ASTM D638-14 polylactic acid (PLA) specimen was fabricated using 16 layers, and its G-code data were systematically compared with kinematic variables extracted from long-wave infrared (LWIR) thermal images. The results demonstrate that the approach enables the detection of deviations in nozzle movement, providing valuable insights into layer deposition accuracy and serving as an early indicator for potential defect formation. This thermal image–based monitoring can serve as a non-invasive tool for in situ quality control (QC) in FDM, supporting process optimization and improved reliability of AM polymer components. These findings contribute to the advancement of smart sensing strategies for integration into industrial additive manufacturing workflows.

A real time system to measure without contact the length of stainless steel sheets in a production line is presented in this paper. The items are measured in real time as they are cut in the production line and are moved forward by a conveyor passing down a differential laser sensor device which measures and integrates its perpendicular speed. One challenge of the system is to deal with different length and speed maintaining the accuracy. Also installation, calibration and supervision procedures have been established. In addition the designed system must be robust enough to be installed in the hard industrial environment of the iron and steel works. The presented system has been designed and installed for quality control purpose in a stainless steel factory.