In this work, the measurement noise of a point autofocus surface topography measuring instrument is evaluated, as the first step towards establishing a route to traceability for this type of instrument. The evaluation is based on the determination of the metrological characteristics for noise as outlined in draft ISO specification standards by using a calibrated optical flat. The static noise and repeatability of the autofocus sensor are evaluated. The influence of environmental disturbances on the measured surface topography and the built-in software to compensate for such influences are also investigated. The instrument was found to have a measurement noise of approximately 2 nm or, when expressed with the measurement bandwidth, 0.4 nm/√Hz for a single-point measurement.

The development of reliable additive manufacturing (AM) technologies to process metallic materials, e.g. selective laser melting (SLM), has allowed their adoption for manufacturing final components. To date, ensuring part quality and process control for low-volume AM productions is still critical because traditional statistical techniques are often not suitable. To this aim, extensive research has been carried out on the optimisation of material properties of SLM parts to prevent defects and guarantee part quality. Amongst all material properties, defects in surface hardness are of particular concern as they may result in an inadequate tribological and wear resistance behaviour. Despite this general interest, a major void still concerns the quantification of their extent in terms of probability of defects occurring during the process, although it is optimised. Considering these issues, this paper proposes a novel approach to quantify the probability of occurrence of defects in hardness-optimised parts by SLM. First, three process variables, i.e. laser power, scan speed and hatching distance, are studied considering their effect on hardness. Design of Experiments and Response Surface Methodology are exploited to achieve hardness optimisation by controlling process variables. Then, hardness defect probability is estimated by composing the uncertainty affecting both process variables and their relationship with the hardness. The overall procedure is applied to AlSi10Mg alloy, which is relevant for both aerospace and automotive applications. The approach this study proposes may be of assistance to inspection designers to effectively and efficiently set up quality inspections in early design phases of inspection planning.

Point autofocus instruments are often used for measuring the surface topography of objects with complex geometry. Determining the metrological characteristics of the instrument is key to ensuring a traceable areal surface topography measurement. In this work, several metrological characteristics, as outlined in ISO/FDIS 25178-600, are determined for a commercial point autofocus instrument, including flatness deviation, the amplification and linearity of the lateral and vertical axes, and the perpendicularity between the axes. Calibrated material measures including an optical flat, step heights and areal cross gratings are used to determine the metrological characteristics. The impact of the point autofocus operating principle and the evaluation method on the metrological characteristics is discussed.

This article focuses on the inference on the errors in manufactured parts controlled by using measurements devices. The characterization of the part surface topographies is core in several applications. A broad set of properties (tribological, optical, biological, mechanical, etc.) depends on the micro-and macrogeometry of the parts. Moreover, parts usually show typical deterministic geometric deviation pattern, referred to as manufacturing signatures, due to the specific manufacturing processes and process setup parameters adopted for their production. In several situations, the measurements may also be affected by systematic errors due to the measurement process, that might be caused, for example, by a poor part alignment during the measurement process. Measurement techniques and characterization methods have been standardized in the International Standard ISO 25178, defining parameters characterizing the surface topography and supplying methods and formula adapt to deal with this issue computationally. In the present article, we consider a type of spatial dependence between measured values at different points that suggest the use of the variogram to identify patterns in the parts. We offer a comparison, based on a real set of measures, between the latter approach and the conventional as a test of the efficient performance of our findings.

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Modern smart and intelligent manufacturing is characterised by an increasing use of highly engineered surfaces and quasi‐free form geometries, for example, by additive manufacturing, and the requirement for fast and informative measurement tools for quality controls in production. These have lately pulled towards the adoption of optical surface topography measuring instruments to qualify technological surfaces, which are core to being assessed to characterise the product and optimise the manufacturing process. Surface topography measurements performed by optical instruments are known to suffer from disturbances, such as non‐measured points and spikes. These are due to complex interactions between the measurand and the instrument and may result in poor measurement quality and biased characterisation results. Currently, the correction of measurement disturbances is carried out by empirical approaches which mostly involve thresholding and interpolations, whose sensitivity is often devolved to the operator. In this work, a Gaussian process regression‐based approach is outlined to identify and correct measurement disturbances exploiting spatial correlation properties of the measurements to provide a formal, robust, and univocally defined approach to manage measurement disturbances. The proposed approach, differently from currently available alternatives, is capable of managing at once the different type of disturbances by means of a supervised machine learning technique. The formal and practical advantages of the proposed method are discussed exploiting case studies of industrial interest applied on quasi‐flat surfaces to stress the disturbances effect.