study of measurement process capability with non normal data distributions

Abstract The study of capability of measurement processes is becoming a common practice within Quality Management Systems. Actually, "Gauge R&R study" techniques are currently largely widespread. In general, most of the tools described in the scientific literature, prescribed by the international standards and/or implemented in practical applications are based on the normality assumption of data distributions. This work discusses some alternative approaches for "gauge R&R study" when non-normal data distributions are involved. In order to clarify the impact of this problem in practical applications, the techniques considered in the present paper are analyzed through computer simulation of real case studies in the field of dimensional tolerancing

Multivariate control charts for monitoring internal cameraparameters in digital photogrammetry for LSDM (Large-ScaleDimensional Metrology) applications

Industrial non-contact dimensional measurements using photogrammetry rely critically upon stabilityin time of camera calibration. This is particularly relevant for multi-camera systems employed for con-tinuous and/or long term monitoring of some dimensional process, e.g. dimensional checks of the samemanufactured component as it comes off the production line. In most of these cases, camera calibration isupdated regularly to ensure optimal accuracy. Specifically, the use of photogrammetric systems requiresthe knowledge of both internal and external camera parameters estimated by calibration. Constancy ofboth sets is required during use. Internal parameters, pertaining to camera-specific properties, requirestability over the operational lifespan of the system, while external parameters, concerning location andorientation, may change between calibrations. A diagnostic method for internal parameters based onmultivariate control charts is proposed. The purpose of this method is to provide a comprehensive stabil-ity control over all the performed calibrations, especially for those systems used for regular monitoring ofproduction lines. By integrating chart building into calibration software, no additional steps are added tothe operator’s workload for the calibration process. A practical application of the described methodologyis presented at the end of the paper

A new approach for evaluating experienced assembly complexity based on Multi Expert-Multi Criteria Decision Making method

In manufacturing, complexity is considered a key aspect that should be managed from the early phases of product and system design to improve performance, including productivity, efficiency, quality, and costs. The identification of suitable methods to assess complexity has always been of interest to researchers and practitioners. As complexity is affected by several aspects of different nature, it can be assessed from objective or subjective viewpoints or a combination of both. To assess experienced complexity, the analysis relies on the subjective evaluations given by practitioners, usually expressed on nominal or ordinal scales. However, methods found in the literature often violate the properties of the scales, potentially leading to bias in the results. This paper proposes a methodology based on the analysis of categorical data using the multi expert-multi criteria decision making method. A number of criteria are adopted to assess assembly complexity and, from subjective evaluations of operators, product assembly complexity is assessed at an individual level and then, aggregating results, at a global level. A comparison between experienced complexity and an objective assessment of complexity is also performed, highlighting similarities and differences. The assessment of experienced complexity is much more straightforward and less demanding than objective assessments. However, this study showed that it is preferable to use objective assessments for highly complex products as individuals do not discriminate between different complexity levels. An experimental campaign is conducted regarding a manual assembly of ball-and-stick products to show the applicability of the methodology and discuss the results

An uncertainty-based quality evaluation tool for nanoindentation systems

Instrumented Indentation Test (IIT) is a nonconventional mechanical tests allowing multi-scale mechanical characterisation. It is employed for research and quality control in strategic manufacturing fields for developing edge technologies. The state-of-the-art lacks a robust methodology to assess quality of indentations and benchmark indentation devices. This is limiting the application of IIT for specifying and verifying tolerances. This work proposes an uncertainty-based quality evaluation tool for IIT. A non-parametric uncertainty evaluation of calibration contribution is proposed. The method shows the statistical significance of indentation sets modelled by the bootstrap samples. The uncertainty is then propagated according to the law of uncertainty propagation for the evaluation of mechanical characteristics. The methodology is applied to five case studies. Results show that the uncertainty evaluation model can achieve robust and sensitive quantification of the indentation results and system quality, thus providing a useful practical tool for industrial and academic practitioners within a metrological framework

Uncertainty modeling in 3D SEM stereophotogrammetry

The scanning electron microscope (SEM) is widely used to acquire high resolution images. In order to reconstruct the third dimension of surface features, photogrammetry methods can be adopted. A specimen is imaged in the SEM acquiring two images, the stereo-pair, by scanning the same area from two different perspectives. The stereo-matching problem is solved by area- or feature-based methods implemented in commercial software. Piazzesi provided a first model for deriving surface topography from eucentric stereo-pairs. An uncertainty evaluation for the vertical elevation has been performed in a recent work for a cylindrical item. The aim of the present work is to extend the uncertainty evaluation to all surface coordinates. The proposed approach is based on the multivariate law of propagation of uncertainty (MLPU). Some preliminary results are also presented and discussed

Misurare per decidere. Misure e statistica di base.

Nel campo tecnico-scientifico molte decisioni sono supportate da misurazioni. Ma per poter decidere correttamente è importante assegnare ai risultati di misura il loro effettivo significato. Ciò è soprattutto importante, ed espressamente richiesto, quando si opera in Sistemi Qualità. In tal caso la gestione delle misure e prove deve essere rigorosa, e può trovare un concreto supporto negli argomenti qui trattati, per l’attenzione posta a curare insieme la correttezza sostanziale e l’eliminazione di vincoli inutili. Giulio Barbato, Alessandro Germak e Gianfranco Genta sono docenti di “Statistica sperimentale e Misure Meccaniche” ed “Experimental Statistics and Mechanical Measurement” presso il Politecnico di Torino. Giulio Barbato, professore ordinario di Misure Meccaniche e Termiche presso il Politecnico di Torino, ha lavorato per oltre vent’anni presso l’Istituto di Metrologia “G. Colonnetti” del C.N.R. (ora confluito a formare l’INRiM) ove si è occupato sia dei campioni primari nazionali di forza e durezza, sia degli accreditamenti dei Centri di taratura SIT (ora LAT-ACCREDIA). Dal 1997 è titolare di corsi di Misure Meccaniche e Statistica Applicata alla Sperimentazione. Alessandro Germak, primo tecnologo all’Istituto Nazionale di Ricerca Metrologica dove svolge attività di ricerca da oltre trent’anni, è responsabile dei campioni primari di forza e durezza e dei metodi primari per la misura dell’accelerazione di gravità locale. È esperto tecnico per gli accreditamenti dei Centri di taratura LAT-ACCREDIA ed è membro dei Comitati Consultivi del CIPM e dei comitati tecnici EURAMET per le grandezze di interesse. Gianfranco Genta è ricercatore in “Tecnologie e Sistemi di Lavorazione” presso il Dipartimento di Ingegneria Gestionale e della Produzione del Politecnico di Torino, dove ha conseguito nel 2010 il titolo dottore di ricerca in “Metrologia: Scienza e Tecnica delle Misure”. Si occupa, principalmente, di metrologia industriale, ingegneria della qualità e applicazione di metodi statistici in ambito tecnologico

Defect prediction model for wrapping machines assembly

Purpose – Development of a defect prediction model for the assembly of wrapping machines. Design/methodology/approach – The assembly process of wrapping machines is firstly decomposed into several steps, called workstations, each one potentially critical in generating defects. According to previous studies, two assembly complexity factors related to the process and the design are evaluated. Experimental defect rates in each workstation are collected and a bivariate prediction model is developed. Findings – Defects occurring in low-volume production, such as those of wrapping machines, may be predicted by exploiting the complexity based on the process and the design of the assembly. Research limitations/implications – Although the defect prediction model is designed for the assembly of wrapping machines, the research approach can provide a framework for future investigation on other low-volume productions of similar electromechanical and mechanical products. Practical implications – The defect prediction model is a powerful tool for quantitatively estimating defects of newly developed wrapping machines and supporting decisions for assembly quality-oriented design and optimisation. Originality/value – The proposed model is one of the first attempts to predict defects in low-volume production, where the limited historical data available and the inadequacy of traditional statistical approaches make the quality control extremely challenging

Inspection planning by defect prediction models and inspection strategy maps

Designing appropriate quality-inspections in manufacturing processes has always been a challenge to maintain competitiveness in the market. Recent studies have been focused on the design of appropriate in-process inspection strategies for assembly processes based on probabilistic models. Despite this general interest, a practical tool allowing for the assessment of the adequacy of alternative inspection strategies is still lacking. This paper proposes a general framework to assess the efectiveness and cost of inspection strategies. In detail, defect probabilities obtained by prediction models and inspection variables are combined to defne a pair of indicators for developing an inspection strategy map. Such a map acts as an analysis tool, enabling positioning assessment and benchmarking of the strategies adopted by manufacturing companies, but also as a design tool to achieve the desired targets. The approach can assist designers of manufacturing processes, and particularly low-volume productions, in the early stages of inspection planning