Current criteria used to determine whether rough surfaces affect skin friction typically rely on a single amplitude parameter to characterize the roughness. The most commonly used criteria relate the centerline averaged roughness, R_a, to an equivalent sandgrain roughness size, k_s. This paper shows that such criteria are oversimplified and that R_a/k_s is dependent on the roughness topography, namely, the roughness slope defined as the roughness amplitude normalized by the distance between roughness peaks, R_a/λ. To demonstrate the relationship, wake traverses were undertaken downstream of an aerofoil with various polished surfaces. The admissible roughness Reynolds number (ρ₁u₁R_a/μ₁) at which the drag rose above the smooth blade case was determined. The results were used to demonstrate a 400% variation in R_a/k_s over the roughness topographies tested. The relationship found held for all cases tested, except those where the roughness first initiated premature transition at the leading edge. In these cases, where the roughness was more typical of eroded aerofoils, the drag was found to rise earlier.

The continuing evolution of ultraprecision machining places an increasing need to perform surface measurement in the manufacturing environment. Development of on-machine surface measurement (OMSM) tools for ultraprecision machining processes will enable the reduction of measurement cycle time as well as the potential improvement of machining accuracy. In the present study, an in-house designed interferometer probe is integrated onto an ultraprecision diamond turning machine. System configuration, calibration scheme and various scanning strategies are first presented. The benefit of OMSM preserves the consistency between the machining and measurement coordinate system. Two applications of OMSM for ultraprecision turning process are further investigated. To further improve the surface accuracy, corrective machining is carried out based on the on-machine measured data. The profile accuracy of a cosine curve sample was improved after corrective machining with OMSM. Moreover, process investigation with OMSM was employed to model the effect of process parameters on the form error in ultraprecision cylindrical turning process. OMSM enables the consistent measurement of part coordinates for each experimental run, which is critical for acquiring a deterministic response for empirical modeling. A reduced quadratic model was built by means of response surface methodology and verified by the test for significance of the regression model. The confirmation tests show that the model predicted value conformed to the experimental value with a difference less than 4%.

In order to enhance the measurement availability for manufacturing applications, on-machine surface measurement (OMSM) is integrated onto the machine tools, which avoids the errors caused by re-positioning workpieces and utilizes the machine axes to extend the measuring range as well. However, due to the fact that measurement probe actuation is performed using the machine tool axes, the inherent kinematics error will inevitably induce additional deviations onto the OMSM results. This paper presents a systematic methodology of kinematics error modelling, measurement, and compensation for OMSM on an ultra-precision turning lathe. According to the measurement task, a selective kinematics error model is established with four primary error components in the sensitive measurement direction, based on multi-body theory and a homogeneous transformation matrix (HTM). In order to separate the artefact error from the measurement results, the selected error components are measured using the reversal method. The measured error value agrees well with the machine tool’s specification and a kinematics error map is generated for further compensation. To verify the effectiveness of the proposed kinematics error modelling, measurement, and compensation, an OMSM experiment of an optically flat mirror is carried out. The result indicates the OMSM is the superposition of the sample surface form error and the machine tool kinematics error. With the implementation of compensation, the accuracy of the characterized flatness error from the OMSM improves by 67%.

Although the effect of surface topography on fatigue life is widely accepted, the underlying role of surface roughness from first principles is still poorly understood. Currently approaches which consider the influence of surface roughness on fatigue life prediction can be broadly classified into surface corrector factors (C_s) and stress concentration factors (K_t). Those approaches describe the surface according to the manufacturing process (machined, grounded…), or using 2D height descriptor parameters (R_a, R_z…). However, these approaches are not able to correctly describe the effect of roughness on the fatigue performance where it is anticipated a richer set of surface descriptors would show correlation. The present work aims to highlight the inherent limitations of the most commonly employed 2D surface measurement and characterization techniques, and provides an insight into the application of 3D areal surface characterization processes including the use of the latest areal surface topography parameters quantifying amplitude, spatial, and hybrid topographical information which is considered to be important for fatigue performance correlation.

Electron Beam Metal Additive Manufacturing (EBAM) has been developed over recent years because of its advantages in manufacturing internal features and complex structures with relatively high productivity. The process proceeds by layer by layer melting and re-solidification of metal powder utilising an electron beam energy source. Following solidification of the build layer, the surface becomes ‘shiny’ with high reflectivity which makes in-process inspection of the surface of the build layer using fringe projection, difficult. To address this issue, a novel intelligent fringe projection technique using a support-vector-machine (SVM) algorithm is proposed to measure the 3D topography of high dynamic range surfaces on a layer by layer basis within the EBAM machine. To facilitate the SVM implementation a range of EBAM manufactured surfaces are utilised as samples for training and classification. The training measurements are based on different exposure times and saturated pixels are utilised as feature vectors to predict tested samples categories. Training errors are evaluated, and the correct recognition rate is 91%, which indicates the proposed training method can effectively predict the categories of the surfaces. Examples of melting edge swelling and powder bed inspections during a part build are used to demonstrate the system capability for inspection high dynamic range measurement within the EBAM machine. The whole inspection process lasts less than 5 s with two measurements which minimises the time penalty for the manufacturing process. Experimental results showed that the powder and the melting surface defects could be efficiently inspected using the proposed technology and the measurement result could be fed back to the build process to improve the processing quality.