Alfredo Anglani

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.

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.

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.

Robust scheduling of parallel machines with sequence-dependent set-up costs

In this paper we propose a robust approach for solving the scheduling problem of parallel machines with sequence-dependent set-up costs. In the literature, several mathematical models and solution methods have been proposed to solve such scheduling problems, but most of which are based on the strong assumption that input data are known in a deterministic way. In this paper, a fuzzy mathematical programming model is formulated by taking into account the uncertainty in processing times to provide the optimal solution as a trade-off between total set-up cost and robustness in demand satisfaction. The proposed approach requires the solution of a non-linear mixed integer programming (NLMIP), that can be formulated as an equivalent mixed integer linear programming (MILP) model. The resulting MILP model in real applications could be intractable due to its NP-hardness. Therefore, we propose a solution method technique, based on the solution of an approximated model, whose dimension is remarkably reduced with respect to the original counterpart. Numerical experiments conducted on the basis of data taken from a real application show that the average deviation of the reduced model solution over the optimum is less than 1.5%.

Long-term planning in manufacturing production systems under uncertain conditions

Nowadays, the frequency of decisions related to the configuration and capacity evaluation of manufacturing production systems is increasing in more and more industrial sectors, especially in the automotive field. This is due to a variety of factors, such as the reduction of the life cycle of the product, increasing competition, etc. In such a context, decision makers have to take their actions in shorter times than they ever did in the past: as an example, they typically need to take quick decisions about different production system alternatives. This specific problem has increased in complexity because of the necessity to take into account all the sources of variability and each related level of uncertainty in the available data definition. Two main aspects lead to such difficulties: the lack of a proper decision support system and the need to contextually model the uncertain data. This paper presents the first step in this direction. In particular, a decision support system (DSS) has been developed to help decision makers take productive capacity planning decisions according to the uncertain characterisation of the market evolution. First, a strategy evaluation tool allows the decision maker to specify several productive capacity expansion policies and, then, uses a fuzzy discrete event simulation paradigm (Fuzzy-DEVS) to compare them, providing the possibility of choosing between the different alternatives according to performance indicators. A strategy design tool helps the decision maker by inferring the best expansion policy on the basis of the system analysis conducted in the first step. Finally, our approach has been validated by means of an industrial test case in the automotive sector.

Experimental springback evaluation in hydromechanical deep drawing (HDD) of large products

Springback is a really troublesome effect in sheet metal forming processes. In fact changes in geometry after springback are a big and costly problem in the automotive industry. In this paper the authors want to analyse the springback phenomenon experimentally in sheet metal hydroforming. Compared with conventional deep drawing, sheet hydroforming technology has many remarkable advantages, such as a higher drawing ratio, better surface quality, less springback, better dimensional freezing and capability to manufacture complicated shapes. The springback phenomenon has been extensively analysed in deep drawing processes but there are not many works in the literature about springback in sheet metal hydroforming. In order to study it, the authors have performed an accurate measuring phase on the chosen test cases through a coordinate measuring machine and the obtained measurements have been utilised for the determination of springback parameters, taking into account the method proposed by Makinouchi et al. The authors have focused their attention on the possibility of adopting a modified Makinouchi et al. approach in order to measure the springback of the large size considered test cases. Through the implemented methodology it has been possible to calculate the values of the springback parameters. The obtained results correspond to the observed experimental deformations. Analysing the springback parameter values of the different combinations investigated experimentally, the authors have also studied the pre-bulging influence on the springback amount.

Virtual Tryout and Optimization of the Extrusion Process Using a Shape Variables Generator Integrated in the CAE Preprocessing Environment

CAE tools usage to evaluate process performances it has became a matter of fact in cases like: metal forming, foundry, casting and forging. Like in these applications, also for the extrusion processes CAE tools usage has became a convenient opportunity, not only to verify the designed process but also to tune it in a virtual way. In this specific application, it has been evaluated the chance to use an optimization tool in combination with a process solver. The chance to optimize the extrusion process has been investigated using shape design variables for the tool process design, in order to obtain the best extruded profile quality. The applied procedure has shown strength points like: the full integration between the preprocessor and the shape variables generator, without any need to exchange data with the CAD environment during the optimization and weak points, such has the reduced freedom for the shape variation, due to the risk of an excessive distortion of the finite elements which describe the process.

Manufacturing process quality control by means of a Fuzzy ART neural network algorithm

Neural networks are potential tools that can be used to improve process quality control. In fact, various neural algorithms have been applied successfully for detecting groups of well-defined unnatural patterns in the output measurements of manufacturing processes. This paper discusses the use of a neural network as a means for recognising changes in the state of the monitored process, rather than for identifying a restricted set of unnatural patterns on the output data. In particular, a control algorithm, which is based on the Fuzzy ART neural network, is first presented, and then studied in a specific reference case by means of Monte Carlo simulation. Comparisons between the performances of the proposed neural approach, and those of the CUSUM control chart, are also presented in the paper. The results indicate that the proposed neural network is a practical alternative to the existing control schemes.

Scheduling in dial-indexed production lines

An innovative approach to maximize throughput in dial indexes flow lines provided by an automatic and synchronous part transfer mechanism is outlined. In this class of production systems, the performance measures are directly related to the control operating method, defined as the schedule of activities performed by the part transport mechanism and the workstations during each dial cycle. Usually, a unique operating method (i.e. the main operating method) is adopted to control the production flow and is designed to process any part type workable in the system, independently by the required part program. In order to design higher-performance systems, current industrial research is focused on the implementation of more efficient hardware technologies. However, technological and economical aspects sometimes limit greatly the possibility to have remarkable results. The goal of the proposed solution was to improve system performance by optimizing the control and management methods and without any expensive change in the system hardware. The approach was based on the definition of new operating methods characterized by a throughput value higher than the one of the main operating method. In a second step, an integer linear mathematical programming model was formulated to sequence the parts at the loading station in order to maximize the system throughput. An actual application was analysed to assess the validity of the proposed approach. Computational experiments have shown a remarkable increase in system performance.

A Model-free Approach for Quality Monitoring of Geometric Tolerances

Profile monitoring can be effectively adopted to detect unnatural behaviors of machining processes, i.e., to signal when the functional relationship used to model the geometric feature monitored changes with time. Most of the literature concerned with profile monitoring deals with the issue of model identification for the functional relationship of interest, as well as with control charting of the model parameters. In this chapter, a different approach is presented for profile monitoring, with a focus on quality monitoring of geometric tolerances. This approach does not require an analytical model for the statistical description of profiles considered, and it does not involve a control charting method. An algorithm which allows a computer to automatically learn from data the relationship to represent profiles in space is described. The proposed algorithm is usually referred to as a neural network and the data set, from which the relationship is learned, consists just of profiles representative of the process in its in-control state. Throughout this chapter, a test case related to roundness profiles obtained by turning and described in Chapter 11 is used as a reference. A verification study on the efficacy of the neural network shows that this approach may outperform the usual control charting method.