A New Forecasting Model for the Diffusion of ISO 9000 Standard Certifications in European Countries

ISO 9000 standards for quality system management are involving a higher and higher number of enterprises and organizations. This paper presents a detailed analysis of certification diffusion in Italy and in some European countries with similar economic structures. Benchmarking and evolution forecasts are based on the "logistic model", traditionally used for studying biological growth phenomena. The presentation is supported by many empirical data, which show that, in many countries, the phenomenon is going to be close to saturation. Finally, some considerations about new developments, after the present "certification era", are proposed

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

Asymptotic defectiveness of manufacturing plants: an estimate based on process learning curves

The paper describes a method for a preliminary estimation of asymptotic defectiveness of a manufacturing plant based on the prediction of its learning curve estimated during a p-chart setting up. The proposed approach provides process managers with the possibility of estimating the asymptotic variability of the process and the period of revision of p-chart control limits. An application of the method is also provided

FMECA methodology applied to two pathways in an orthopaedic hospital in Milan.

INTRODUCTION: Adverse events pose a challenge to medical management: they can produce mild or transient disabilities or lead to permanent disabilities or even death; preventable adverse events result from error or equipment failure. METHODS: IRCCS Istituto Ortopedico Galeazzi implemented a clinical risk management program in order to study the epidemiology of adverse events and to improve new pathways for preventing clinical errors: a risk management FMECA-FMEA pro-active analysis was applied either to an existing clinical support pathway or to a new process before its implementation. RESULTS: The application of FMEA-FMECA allowed the clinical risk unit of our hospital to undertake corrective actions in order to reduce the adverse events and errors on high-risk procedure used inside the hospitals

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

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

Accurate coil springs axial and transverse stiffness measurements with multicomponent testing machines

Accurate characterization of coil springs, typically in terms of axial and transverse stiffness, is crucial in many applications, in particular in automotive engineering, such as suspensions, vibration reduction, seating, exhaust valves, gear engagement controls, transmission hose, fuel panels, car trunks, and engine hoods. These measurements are usually performed in spring testing machines along the vertical axis in quasi-static conditions. However, when springs are stressed along the main vertical axis, side forces, bending and torsion moments are generated, thus have to be evaluated. For this reason, a hexapod-shaped multicomponent force and moment transducer has been recently devised, realized and integrated into standard spring testing machines capable to measure the displacement along the main and transverse axes. In this way, forces, moments and displacement components generated by the springs can be measured and axial and transverse stiffness derived. In this work, two multicomponent spring testing machines with the hexapod-shaped force and moment transducer are described and measurements on different large coil springs are presented