Inference on errors in industrial parts: Kriging and variogram versus geometrical product specifications standard

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

Optimisation of laser welding of deep drawing steel for automotive applications by Machine Learning: A comparison of different techniques

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

Improvement of instrumented indentation test accuracy by data augmentation with electrical contact resistance

Instrumented Indentation Test allows thorough surface multi-scale mechanical characterisation by depth-sensing the indenter penetration and correlating it with the indenter-sample contact area and the applied force. Localised plastic phenomena at the indentation edge, i.e. pile-up and sink-in, may bias the characterisation results. Current approaches attempt correcting related systematic errors by numerical simulation and AFM-based techniques. However, they require careful tuning and complex and expensive experimental procedures. This work proposes a methodology based on in-situ Electric Contact Resistance which augments information on the contact area and allows edge effect correction. The methodology is demonstrated and validated on industrially relevant metallic materials

Non-Contact Articulated Robot-Integrated Gap and Flushness Measurement System for Automobile Assembly

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

Build orientation effect on Ti6Al4V thin-wall topography by electron beam powder bed fusion

Additive Manufacturing is a key enabling technology for Industry 4.0 and the Green Deal, allowing more efficient resources exploitation while providing innovative design to critical components. Electron Beam Powder Bed Fusion (EB-PBF) is an edge technology for many sectors, i.e. aerospace, medical, and automotive. The control of the surface finish by surface topography measurements is essential to engineer surface functional properties, whose specifications are application specific. This works investigates the effect of thin-wall orientation and surface inclination on the topography, described by areal field and feature parameters, to provide designers with a useful tool in the early stage of product development and tolerance specification and verificatio

Minimization of defects generation in laser welding process of steel alloy for automotive application

Laser welding (LW) thanks to its flexibility, limited energy consumption and simple realization has a prominent role in several industrial sectors. LW process requires careful parameters' tuning to avoid generating internal defects in the microstructure or a poor weld depth, which reduce the joining mechanical strength and result in waste. This work exploits a supervised machine learning algorithm to optimize the process parameters to minimize the generated defects, while catering for design specifications and tolerances to predict defect generation probability. The work outputs a predictive quality control model to reduce non-destructive controls in the LW of aluminum for automotive applications

Analysis of residual plastic deformation of blanked sheets out of automotive aluminium alloys through hardness map

Reducing overall vehicle weight is essential to reduce fuel consumption and pollutant emission and to improve noise, vibration, and harshness (NVH) performances. The substitution with lighter alloys can involve the grand majority of vehicle components, depending on the market sector. In several applications, e.g., chassis, pulleys, and viscodampers, metal sheets are formed in several steps, each of whom work-hardens the material reducing the available residual plasticity. Typically, the process is designed via FEM, whose results are affected by the initial conditions, often neglected, and is performed on pre-processed materials from suppliers. In this regard, correctly simulating the first step of the process is critical. However, the related initial conditions, in terms of residual stress and strain induced by former preliminary operations, are often neglected. This work proposes a quick and economical experimental procedure based on a hardness map to estimate initial conditions and to validate FEM results. The procedure allows evaluating the material's residual plasticity, which is necessary to process engineers to design following manufacturing steps. The approach is demonstrated on an industrially relevant case study, i.e., the blanking of an AA 5754, in use for water pump pulleys

Information-rich quality controls prediction model based on non-destructive analysis for porosity determination of AISI H13 produced by electron beam melting

The number of materials processed via additive manufacturing (AM) technologies has rapidly increased over the past decade. As of these emerging technologies, electron beam powder bed fusion (EB-PBF) process is becoming an enabling technology to manufacture complex-shaped components made of thermal-cracking sensitive materials, such as AISI H13 hot-work tool steel. In this process, a proper combination of process parameters should be employed to produce dense parts. Therefore, one of the first steps in the EB-PBF part production is to perform the process parameter optimization procedure. However, the conventional procedure that includes the image analysis of the cross-section of several as-built samples is time-consuming and costly. Hence, a new model is introduced in this work to find the best combination of EB-PBF process parameters concisely and cost-effectively. A correlation between the surface topography, the internal porosity, and the process parameters is established. The correlation between the internal porosity and the melting process parameters has been described by a high robust model (R-adj(2) = 0.91) as well as the correlation of topography parameters and melting process parameters (R-adj(2) = 0.77-0.96). Finally, a robust and information-rich prediction model for evaluating the internal porosity is proposed (R-adj(2) = 0.95) based on in situ surface topography characterization and process parameters. The information-rich prediction model allows obtaining more robust and representative model, yielding an improvement of about 4% with respect to the process parameter-based model. The model is experimentally validated showing adequate performances, with a RMSE of 2% on the predicted porosity. This result can support process and quality control designers in optimizing resource usage towards zero-defect manufacturing by reducing scraps and waste from destructive quality controls and reworks

Noise evaluation of a point autofocus surface topography measuring instrument

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

Uncertainty evaluation of small wear measurements on complex technological surfaces by machine vision-aided topographical methods

Wear assessment is an essential feature within the Industry 4.0 framework to optimise machining and control durability of components made of innovative materials. Complex topographies often make wear measurement a challenging task. Literature tackles it by comparing the final topography with the unworn state, either by empirical methods or by registration via machine vision algorithms. This paper develops a framework to evaluate the related measurement uncertainty, so far lacking, by exploiting instruments metrological characteristics and statistical modelling. This framework is applied to an industrially relevant case study to compare the performances of accredited methods for wear measurement available in literature