Massimo Pacella

Object-oriented modeling and simulation of flexible manufacturing systems: a rule-based procedure

Abstract Simulation by a software model, is one of the most frequently used techniques for the analysis and design of manufacturing systems. In the software engineering research area, the object-oriented approach has fully demonstrated to be an effective technique with respect to the design and implementation phases of complex software projects. Even if object-oriented programming has proven to be a powerful technique, a systematic design method should also be used in order to implement reliable software, in particular in the development of simulation models. This paper presents a new procedure to develop flexible manufacturing system (FMS) simulation models, based on the UML analysis/design tools and on the ARENA ® simulation language. The two main features of the proposed procedure are the definition of a systematic conceptual procedure to design FMS simulation models and of a set of rules for the conceptual model translation in a simulation language. The goal is to improve the software development efficiency through a rule-based approach and to add some of the fundamental object-oriented features to the ARENA ® simulation environment.

Statistical Process Control for Geometrical Specifications: On the Monitoring of Roundness Profiles

Quality of mechanical components is critically related to both dimensional and geometric specifications. Traditionally, approaches for statistical process control (SPC) focused on the first type of specification only. When the quality of a manufactured product is related to geometric specifications (e.g., profile and form tolerances as straightness, roundness, cylindricity, flatness, etc.), the process should be considered in control if the relationship used to represent that profile or surface in the space is stable over time. This paper presents a novel method for monitoring bidimensional profiles. The proposed method is based on combining a spatial autoregressive regression (SARX) model (i.e., a regression model with spatial autoregressive errors) with control charting. To show the effectiveness of the proposed method, the approach 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 methods (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 arising in phase II and due to spindle-motion errors.

On the use of principal component analysis to identify systematic patterns in roundness profiles

In many industrial applications, quality of products or processes is related to profiles. With reference to mechanical components, profiles and surfaces play a relevant role, as shown by the high number of geometric specifications characterizing most of the technical drawings. In this framework, an important step consists in identifying the systematic pattern which characterizes all the profiles machined while the process is in its standard or nominal state. With reference to this aim, this paper focuses on the use of principal component analysis (PCA) for profile data (Functional PCA). Since a usual objection to PCA is that principal components (PCs) are often difficult or impossible to interpret, this paper explores what types of profile features allow one to obtain interpretable PCs. Within the paper, a real case study related to roundness profiles of mechanical components is used as reference. In particular, functional PCA is applied to the set of real profile data to derive the significant PCs and the corresponding eigenfunctions. In order to gain insight into the information behind the retained PCs, both simulations and analytical results are used. In particular, the analytical results, outlined in the literature on functional data analysis, allow one to link the eigenfunctions to specific profile features, given that profile data admit an orthogonal basis series expansion. Copyright

Manufacturing quality control by means of a Fuzzy ART network trained on natural process data

Abstract In order to produce products with constant quality, manufacturing systems need to be monitored for any unnatural deviations in the state of the process. Control charts have an important role in solving quality control problems; nevertheless, their effectiveness is strictly dependent on statistical assumptions that in real industrial applications are frequently violated. In contrast, neural networks can elaborate huge amounts of noisy data in real time, requiring no hypothesis on statistical distribution of monitored measurements. This important feature makes neural networks potential tools that can be used to improve data analysis in manufacturing quality control applications. In this paper, a neural network system, which is based on an unsupervised training phase, is presented for quality control. In particular, the adaptive resonance theory (ART) has been investigated in order to implement a model-free quality control system, which can be exploited for recognising changes in the state of a manufacturing process. The aim of this research is to analyse the performances of ART neural network under the assumption that predictable unnatural patterns are not available. To such aim, a simplified Fuzzy ART neural algorithm is firstly discussed, and then studied by means of extensive Monte Carlo simulation.

Using recurrent neural networks to detect changes in autocorrelated processes for quality monitoring

With the growing of automation in manufacturing, process quality characteristics are being measured at higher rates and data are more likely to be autocorrelated. A widely used approach for statistical process monitoring in the case of autocorrelated data is the residual chart. This chart requires that a suitable model has been identified for the time series of process observations before residuals can be obtained. In this work, a new neural-based procedure, which is alleviated from the need for building a time series model, is introduced for quality control in the case of serially correlated data. In particular, the Elmans recurrent neural network is proposed for manufacturing process quality control. Performance comparisons between the neural-based algorithm and several control charts are also presented in the paper in order to validate the approach. Different magnitudes of the process mean shift, under the presence of various levels of autocorrelation, are considered. The simulation results indicate that the neural-based procedure may perform better than other control charting schemes in several instances for both small and large shifts. Given the simplicity of the proposed neural network and its adaptability, this approach is proved from simulation experiments to be a feasible alternative for quality monitoring in the case of autocorrelated process data.

Adaptive resonance theory-based neural algorithms for manufacturing process quality control

The demand for quality products in industry is continuously increasing. To produce products with consistent quality, manufacturing systems need to be closely monitored for any unnatural deviation in the state of the process. Neural networks are potential tools that can be used to improve the analysis of manufacturing processes. Indeed, neural networks have been applied successfully for detecting groups of predictable unnatural patterns in the quality measurements of manufacturing processes. The feasibility of using Adaptive Resonance Theory (ART) to implement an automatic on-line quality control method is investigated. The aim is to analyse the performance of the ART neural network as a means for recognizing any structural change in the state of the process when predictable unnatural patterns are not available for training. To reach such a goal, a simplified ART neural algorithm is discussed then studied by means of extensive Monte Carlo simulation. Comparisons between the performances of the proposed neural approach and those of well-known SPC charts are also presented. Results prove that the proposed neural network is a useful alternative to the existing control schemes.

A comparison study of control charts for statistical monitoring of functional data

The quality of products and processes is more and more often becoming related to functional data, which refer to information summarised in the form of profiles. The recent literature has pointed out that traditional control charting methods cannot be directly applied in these cases and new approaches for profile monitoring are required. While many different profile monitoring approaches have been proposed in the scientific literature, few comparison studies are available. This paper aims at filling this gap by comparing three representative profile monitoring approaches in different production scenarios. The performance comparison will allow us to select a specific approach in a given situation. The competitor approaches are chosen to represent different levels of complexity, as well as different types of modelling approaches. In particular, at a lower level of complexity, the ‘location control chart’ (where the upper and lower control limits are ±K standard deviations from the sample mean at each profile location) is considered to be representative of industrial practice. At a higher complexity level, approaches based on combining a parametric model of functional data with multivariate and univariate control charting are considered. Within this second class, we analyse two different approaches. The first is based on regression and the second focuses on using principal component analysis for modelling functional data. A manufacturing reference case study is used throughout the paper, namely profiles measured on machined items subject to geometrical specification (roundness).

From Profile to Surface Monitoring: SPC for Cylindrical Surfaces via Gaussian Processes

Quality of machined products is often related to the shapes of surfaces that are constrained by geometric tolerances. In this case, statistical quality monitoring should be used to quickly detect unwanted deviations from the nominal pattern. The majority of the literature has focused on statistical profile monitoring, while there is little research on surface monitoring. This paper faces the challenging task of moving from profile to surface monitoring. To this aim, different parametric approaches and control-charting procedures are presented and compared with reference to a real case study dealing with cylindrical surfaces obtained by lathe turning. In particular, a novel method presented in this paper consists of modeling the manufactured surface via Gaussian processes models and monitoring the deviations of the actual surface from the target pattern estimated in phase I. Regardless of the specific case study in this paper, the proposed approach is general and can be extended to deal with different kinds of surfaces or profiles.

Monitoring roundness profiles based on an unsupervised neural network algorithm

In modern manufacturing, approaches for profile monitoring can be adopted to detect unnatural behaviors of production processes, i.e. to signal when the relationship used to represent the profiles changes with time. Most of the literature concerned with profile monitoring deals with the problem of model identification and multivariate charting of parameters vector. In this paper, a different approach, which is based on an unsupervised neural network, is presented for profile monitoring. The neural network allows a computer to automatically learn from data the relationship to represent in-control profiles. Then, the algorithm may produce a signal when an input profile does not fit to the prototype learned from the in-control ones. The neural network does not require an analytical model for the statistical description of profiles faced (model-free approach). A comparison study is provided in this paper, in which the Phase II performance of the neural network is compared to that of approaches representative of the industrial practice. Performance is assessed by computer simulation, with reference to a case study related to profiles measured on machined items subject to geometrical specification (roundness). The results indicate that the neural network may outperform usual control charts in signaling out-of-control conditions, due to spindle-motion errors in several production scenarios. The proposed approach can be considered a valuable option for profile monitoring in industrial applications.

Understanding ART-based neural algorithms as statistical tools for manufacturing process quality control

Neural networks have recently received a great deal of attention in the field of manufacturing process quality control, where statistical techniques have traditionally been used. In this paper, a neural-based procedure for quality monitoring is discussed from a statistical perspective. The neural network is based on Fuzzy ART, which is exploited for recognising any unnatural change in the state of a manufacturing process. Initially, the neural algorithm is analysed by means of geometrical arguments. Then, in order to evaluate control performances in terms of errors of Types I and II, the effects of three tuneable parameters are examined through a statistical model. Upper bound limits for the error rates are analytically computed, and then numerically illustrated for different combinations of the tuneable parameters. Finally, a criterion for the neural network designing is proposed and validated in a specific test case through simulation. The results demonstrate the effectiveness of the proposed neural-based procedure for manufacturing quality monitoring.