Evaluating the influence of machine type on surface roughness in material extrusion

Abstract 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 ( $${R}_{a}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>R</mml:mi> <mml:mi>a</mml:mi> </mml:msub> </mml:math> ) and waviness ( $${W}_{a}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>W</mml:mi> <mml:mi>a</mml:mi> </mml:msub> </mml:math> ) 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.