1,721,203 research outputs found
On the use of profile monitoring for quality control of geometrical product specifications
No full textProfile- and Surface-Monitoring Methods for Shapes
No full textIn discrete part manufacturing, quality depends on the final geometry of the product, which is usually constrained by geometric tolerances. This is why appropriate tools for shape monitoring can highly reduce scraps and reworking. A review of approaches for monitoring the shapes of manufactured objects is given. We start from the simplest case of bi-dimensional curves (i.e., traditional profiles) to move to tri-dimensional surfaces. Critical issues of modeling and monitoring this specific type of profiles and surfaces are explained. Directions for future research are highlighte
Sistemi automatici per il controllo qualità - effetti dell’introduzione di sistemi automatici per il controllo qualità sulle principali tecniche di statistical process control (SPC)
No full textOn integrating multisensor data for quality
No full textThis paper explores advantages arising from properly combining information provided
by two sensors (contact and non-contact one) when measuring the same feature.
When both of the metrology devices are used in cooperation, datasets of different
resolution (a.k.a. multi-resolution data) have to be properly integrated in order to
reconstruct the measured surface (or any geometric feature of interest) in both the
sampled and the unsampled locations. To this aim, we propose a two-stage model,
which consists of a low-resolution data model and a linkage model connecting the lowand
the high-resolution data. The low-resolution data model is a spatial statistics
model, specifically a Gaussian Process (GP). The linkage model has been adapted
from the literature on calibrating computer simulation models of different accuracies.
The newly developed two-stage model is used for quality inspection, showing that a
model that properly combines multisensor information produces better results in terms
of form error assessment when compared to models based on each single-resolution
dataset, or with both but without structuring an appropriate data fusion model
Logistic regression analysis for experimental determination of forming limit diagrams
No full textThe forming limit diagram (FLD) is probably the most common representation of sheet metal formability and can be defined as the locus of the principal planar strains where failure is most likely to occur. Experimental determination of the FLD consists in performing a set of formability tests on a sheet metal blank, where a regular grid has been previously etched. After each test, the deformation of the grid is measured and the relative strains computed. Strains observed closely at the fracture location are related to as ‘failed’ points, while strains
observed on the sound areas of the specimens are labelled as ‘safe’ points. Starting from a set of experimental tests, the FLD should be empirically determined through a statistical analysis of collected data. In fact, statistical approaches (such as linear regression) are required to properly account for the internal randomness of failure occurrence. Linear regression, as well as most of the other empirical approaches in the
scientific literature, takes into account only information related to the safe points.
This paper proposes a different approach, the logistic regression, for the empirical determination of FLDs. Logistic regression allows to directly derive the probability of an event (e.g. the failure) as a function of different predictor variables (both the principal planar strains).
Therefore, by using logistic regression, the process designer can directly associate the failure probability to the scrapping costs, in order to economically evaluate a new sheet metal forming operation.
Logistic regression allows the determination of the FLD by including information concerning both safe and failed points
A review of the current state-of-the-art on in situ monitoring in electron beam powder bed fusion
Full text linkThe industrial development of electron beam powder bed fusion (PBF-EB) is relatively younger and much more limited in terms of global widespread and revenues compared to laser powder bed fusion (PBF-L). Nevertheless, PBF-EB has been adopted in some of the most successful industrial case studies of metal AM, as it provides specific benefits and capabilities that make it a key enabling technology in a variety of industrial applications. Moreover, the recent years have seen a rapid evolution with new actors and new systems entering the market, together with a considerable increase of research and innovation programs. A field of major interest is the development and continuous improvement of in situ sensing and monitoring methods to anticipate the detection of defects, to predict the final quality of the part, and to rethink product qualification procedures. The technological features of the PBF-EB process have motivated the development of solutions that differ from the ones in PBF-L. Some of them have reached a good maturity level, being recently integrated into industrial machines, while others still deserve further research. This study explores the current state-of-the-art on in situ and in-line monitoring of the PBF-EB process, aiming to provide an up-to-date overview of the major differences with respect to PBF-L, currently available methods and their performances, as well as open issues, challenges to be tackled, and perspective for future research and industrial developments
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