Empirical Procedure for Modeling the Variation Transmission in a Manufacturing Process

Heredia, José Antonio; Gras, Matias

doi:10.1080/08982111003724929

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…Yet, the model does not take into account the possible correlation of the process variables and neither does it present a structure that defines the contribution of the variability in each stage with its interrelation with the ensuing stages, which makes it difficult to apply for process improvement. Similar studies were proposed in (Heredia and Gras 2011;Heredia and Gras 2010), where each stage is defined by a first-order autoregressive model that is adjusted by experimentation or using historical data. A subsequent analysis of variance makes it possible to quantify the influence of each of the process variables on the final propagation.…”

Section: Introductionmentioning

confidence: 91%

Derivation and application of the stream of variation model to the manufacture of ceramic floor tiles

Abellán-Nebot

2018

Quality Engineering

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One of the main problems in the manufacture of floor tiles is the dimensional variability of the ceramic product, which leads to the product having to be classified into different dimensional qualities with an increase in cost. In this paper we propose a novel way of modelling the dimensional variability of ceramic floor tiles by the adaptation of the Stream of Variation model. The proposed methodology and its potential applicability contributes to the integration of process knowledge in the ceramic tile industry and allow tile manufacturers have a new methodology for process improvement, variation reduction and dimensional control.

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Section: Introductionmentioning

confidence: 91%

Derivation and application of the stream of variation model to the manufacture of ceramic floor tiles

Abellán-Nebot

2018

Quality Engineering

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“…The integration of systems as a requisite to permit a multistage control in the ceramic industry has advanced in the last decades, but it is still behind the traditional chemical industry [1][2][3]. This is partly because the ceramic sector works with solids, and the level of knowledge in unit operations involving solids has progressed far less than in fluids [4].…”

Section: Introductionmentioning

confidence: 99%

“…After firing, the tiles are classified according to aesthetic properties and dimensional aspects, which are naturally related to processing and composition characteristics [9]. Some industries comprise in a single plant the Steps (1) to (3); other ones purchase the granulated powder from a third part processing unit, being restricted to Steps (2) and (3). The latest approach is followed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Influence of Manufacturing Process Variables on Dimensional Changes of Porcelain Tiles

Santos-Barbosa

Hotza

Boix

et al. 2013

Advances in Materials Science and Engineering

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A model to study the influence of main process variables (powder moisture, maximum compaction pressure, and maximum firing temperature) on the intermediate variables (mass, dry bulk density, size, and thickness) and the final dimensions of porcelain tiles is proposed. The properties of dried and fired bodies are basically determined by the process parameters when the physical, chemical, and mineralogical characteristics of the raw material are kept constant. For a given set of conditions, an equation could be sought for each property as a function of raw materials and processing. In order to find the relationship between moisture content and compaction pressure with dry bulk density, springback, and drying and firing shrinkage, a laboratory experimental design with three factors and four levels was applied. The methodology was validated in lab scale for a porcelain tile. The final size and thickness were estimated, and the influence of the main process variables was analysed.

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“…As proposed in1, these methods can be classified as data‐driven techniques or analytical methods, based on the statistical analysis of historical process data and on offline physical models of the process, respectively. The existing data‐driven techniques include the cause‐selecting control charts2–5 and the variation analysis using linear regression and analysis of variance (ANOVA) tools6–11. The analytical method is represented by ‘Stream of Variation’ (SoV) methodologies focusing on dimensional variation analysis of MMPs by adopting a linear state‐space model incorporating physical laws and engineering knowledge charts12.…”

Section: Introductionmentioning

confidence: 99%

Technological scenarios of variation transmission in multistage machining processes

Palumbo

Chiara²,

Sansone

et al. 2011

Quality & Reliability Eng

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The study of variation transmission in multistage machining processes is a strategic task. It enables us to understand how variation is added and transmitted across the process stages and, therefore, to identify opportunities to reduce variation in key characteristics at the final stage. In this paper, a data-driven technique, based on a first-order autoregressive model, is applied to a multistage machining process of an aero-engine component. Data on 15 key characteristics of 42-tracked components are taken at each of eight sequential process stages. The statistical analysis of data relative to critical key characteristics permits determining the quantity of variation that is added at each stage and the quantity that is transmitted from upstream. This differentiation between added and transmitted variation allows the discovery of which stages contribute most to variation of key characteristics at the final stage. The statistical and technological interpretation of results enables identifying three typical technological scenarios that affect multistage machining process. The knowledge of the variance transmission modalities related to each scenario is a winning factor in achieving quality improvement and cost reduction. It has a direct influence on the effectiveness of variation reduction efforts and may provide useful information to define manufacturing cycles or to select machining tolerances. The application proposed has been developed in the AVIO industry, an international aerospace company at the leading edge of propulsion technology.

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Empirical Procedure for Modeling the Variation Transmission in a Manufacturing Process

Cited by 7 publications

References 2 publications

Derivation and application of the stream of variation model to the manufacture of ceramic floor tiles

Derivation and application of the stream of variation model to the manufacture of ceramic floor tiles

Modelling the Influence of Manufacturing Process Variables on Dimensional Changes of Porcelain Tiles

Technological scenarios of variation transmission in multistage machining processes

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