Search

The issue of monitoring profiles has been defined as being one of the most promising areas of research in statistical process control. One immediate difficulty is how to characterize a profile. As a matter of fact, the identification of a statistical model may become more difficult than expected, thus representing an obstacle to the introduction of profile monitoring in actual applications. For example, when a profile represents the physical dimensions of a machined surface, as it results in manufacturing applications, measurements data often exhibit complex spatial correlation. The aim of this work is to explore a different approach for monitoring profiles, which uses the Adaptive Resonance Theory (ART) neural network. The implementation of this neural network is based on a set of profiles which are representative of the process in its natural, or in-control, state. Throughout the paper, a real case study related to profiles data obtained by a common machining process is used. With reference to the Phase II of profile monitoring, performance of the proposed approach are compared to those of multivariate control charting of the parameters vector. Although the proposed neural network does not produce always outperforming results, it presents comparable performance in several cases. The main advantage presented by the approach is that the model of profile data is “autonomously” derived by the neural network, without requiring any further intervention by the quality practitioner. This feature may create an important bridge between profile monitoring and quality monitoring of several specifications in actual applications.

Manufacturing processes leave on the machined surface a specific “fingerprint” of the process used, which can be usefully adopted to improve the quality control strategy (i.e., to reduce the time required to detect out-of-control conditions). Approaches proposed up to now in the literature are mainly devoted to monitoring simple signatures, where data measured on the profile are not autocorrelated. Unfortunately, most of the times data collected on a machined profile are autocorrelated because they are obtained in similar condition of the machining process and because they are related to local properties of the material machined. This paper presents a novel method for monitoring bi-dimensional profiles when the autocorrelation structure is modeled as a part of the manufacturing signature. The proposed method is based on combining a regression model with autocorrelated errors to a multivariate Hotelling T2 control chart and it is applied to real process data in which the roundness of items obtained by turning has to be monitored. A simulation study indicates that the proposed approach outperforms competing method (based on monitoring the out-of-roundness value for each profile) in terms of the average number of samples required to detect out-of-control conditions. http://www.amstat-online.org/sections/qp/qprc/2006/JRC%20Presentations/Presentation_JRC_QPRC2006_Colosimo.pdf

Loading in flexible manufacturing systems (FMSs) is affected by the characteristics of the FMS under analysis, by the type of plant where the FMS is introduced, and by the production planning hierarchy where the loading module operates. We propose an analysis of the various aspects that influence the prob- lem formulation, identifying the alternatives available in real systems and possible future evolutions. We then provide a survey of different approaches proposed in the literature to tackle the loading problem. Articles are classified according to the type of FMS analyzed, the objective function, and the constraints. Finally, based on our analysis, we suggest some problem issues which need to be addressed, and also directions for future research.

Capri, Naples (Italy)

In this paper an innovative approach to develop manufacturing simulation models, refereed as UMSIS - UML Modeled SIMANO Implemented Simulation software -, is presented. The goals of the proposed approach are the definition of an object-oriented environment UML based to model highly automated manufacturing production systems and a method to translate the UML model into a process oriented simulation model. Since the UMSIS approach possesses both the characteristics of object-oriented models and of a straightforward mapping between high-level conceptual models and process-oriented simulation models, the time required to design and develop a new simulation model or to modify an existing one will be notably reduced. In the paper the proposed approach has been applied to design a simulation model of a control module of a real flexible manufacturing system. The model simulates the control software that manages the pallet flow in the reference FMS.