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.

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 for a single-point measurement.

Laser welding is particularly relevant in the industry thanks to its simplicity, flexibility and final quality. The industry 4.0 and sustainable manufacturing framework gives massive attention to in situ and non‐destructive inspection methods to predict laser weld final quality. Literature often resorts to supervised Machine Learning approaches. However, selecting the ApTest method is non‐trivial and often decision making relies on diverse and unclearly defined criteria. This work addresses this task by proposing a statistical comparison method based on nonparametric tests. The method is applied to the most relevant supervised Machine Learning approaches exploited in literature to predict laser weld quality, specifically, considering the optimisation of a new production line, hence focussing on supervised Machine Learning methods that do not require massive data set, that is, Generalized Linear Model (GLM), Gaussian Process Regression, Support Vector Machine, Classification and Regression Tree, and Genetic Algorithms. The statistical comparison is carried out to select the best‐performing model, which is then exploited to optimise the production process. Additionally, an automatic process to optimise Machine Learning models and process parameters is resorted to, basing on Bayesian approaches, to reduce operator effect. This work provides quality and process engineers with a simple framework to compare Machine Learning approaches performances and select the most suitable process modelling technique.

Virtual experiments (VEs) and digital twins (DTs), pivotal for realizing European strategic policies on sustainability and digitalization within Industry 4.0 and the European Green Deal, simulate physical systems and characteristics in a virtual environment, with DTs incorporating dynamic inputs from and outputs to the real-world counterpart. To ensure confidence in their use and outcomes, traceability and methods to evaluate measurement uncertainty are needed, topics that are hardly covered by the literature so far. This paper provides a harmonized definition of VEs and DTs and introduces a framework for evaluating measurement uncertainty. Furthermore, it discusses how to propagate the uncertainty of the contributions coming from the different parts of the DT. For the core part of the DT, the framework derived for VEs can be used. For the physical-to-virtual (P2V) connection and the virtual-to-physical (V2P) connection, additional sources of uncertainty need to be considered. This paper provides a metrological framework for taking all these uncertainty contributions into account while describing a framework to establish traceability for DTs. Two case studies are presented to demonstrate the proposed methodology considering industrially relevant measuring instruments and devices, namely, a coordinate measuring machine (CMM) and a collaborative robot arm (cobot).

The paper proposes and metrologically characterizes a gap and flushness optical measurement system based on machine vision. The system is developed for an operator-free application as a plug-and-play feature for articulated robotic arms. The system is designed for use in Stop-and-Go quality control point of vehicle assembly process. Non-contact measurement system that consists of an ultraviolet line laser with a sensitive camera and complemented with an advanced machine vision measurement algorithm is developed. The system is directly calibrated according to state-of-the-art literature and the measurement uncertainty within the laboratory conditions is derived according to Guide to the Expression of Uncertainty in Measurement. Measurements on the real vehicle body is done to elicit the difference. The expanded uncertainty achieved by the system is 0.221 mm and 0.177 mm for gap and flushness respectively, significantly smaller than the sole resolution of the most adopted manual feeler gauge verification method.