Int. J. Bio-Inspired Computation, Vol. 4, No. 2, 2012
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A monitoring and control framework for lost foam casting manufacturing processes using genetic programming Alaa F. Sheta Computer Science Department, Faculty of Information Technology, The World Islamic Science and Education (WISE) University, P.O. Box 1101, Amman 11947, Jordan E-mail:
[email protected]
Peter Rausch* Computer Science Department, Georg Simon Ohm University of Applied Sciences, Kesslerplatz 12, 90489, Nuremberg, Germany E-mail:
[email protected] *Corresponding author
Alaa S. Al-Afeef School of Computing Science, University of Glasgow, Room G102, Sir Alwyn Williams Building, Lilybank Gardens, Glasgow, G12 8QQ, Scotland E-mail:
[email protected] Abstract: Monitoring and control of manufacturing processes is an essential part of any industry. Being able to collect sensor measurements, analyse the measurements in an intelligent way, select appropriate actions and validate the desired results of these actions is a tremendous goal to be achieved. In this paper, we propose a monitoring and control framework of a multi-tier closedloop controlling lost foam casting (LFC) system. The proposed system consists of several subsystems like production activity control (PAC), enterprise resource planning (ERP), and business intelligence (BI). Another essential part of the system is the electrical capacitance tomography (ECT) subsystem. This subsystem is in charge of collecting measurements from the LFC process, develops an evolutionary model-based genetic programming (GP) of the process and reconstructs an image of the casting process. The proposed framework can be used to improve the quality of manufacturing processes and to enhance process reliability which, as a result, will increase companies’ profit. The proposed framework can be extended to a variety of applications. Keywords: electrical capacitance tomography; ECT; process tomography; image reconstruction; genetic programming; quality management. Reference to this paper should be made as follows: Sheta, A.F., Rausch, P. and Al-Afeef, A.S. (2012) ‘A monitoring and control framework for lost foam casting manufacturing processes using genetic programming’, Int. J. Bio-Inspired Computation, Vol. 4, No. 2, pp.111–118. Biographical notes: Alaa F. Sheta received his BE, MSc in Electronics and Communication Engineering from the Faculty of Engineering, Cairo University in 1988 and 1994, respectively. He received his PhD from the Computer Science Department, School of Information Technology and Engineering, George Mason University, Fairfax, VA, USA in 1997. Currently, he is a faculty member with the Computer Science Department, WISE University, Amman, Jordan. He is on leave from the Computers and Systems Department, Electronics Research Institute (ERI), Cairo, Egypt. He published over 80 papers, book chapters and two books in the area of image processing and evolutionary computations. His research interests include evolutionary computation, image processing, and automatic control.
Copyright © 2012 Inderscience Enterprises Ltd.
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A.F. Sheta et al. Peter Rausch received his Diploma in Business Administration from Goethe University Frankfurt, Germany. He received his PhD from the same university. He spent several years working in the fields of software development, business process optimisation and consulting. From 2004 to 2008, he has been a Professor of Information Systems at Coburg University of Applied Sciences, Germany. Since 2008, he is a Professor of Information Systems at Georg Simon Ohm University of Applied Sciences Nuremberg, Germany. His research interests include evolutionary computation, business intelligence, performance management, controlling systems, and process management. Alaa S. Al-Afeef is a PhD scholar at the School of Computing Science of the University of Glasgow. He received his BSc in Computer and Computer Information Systems from Philadelphia University in 2005, and MSc in Computer Science from Al-Balqa Applied University in 2010. His research interests include evolutionary computation, genetic programming, and image processing.
1 Introduction
Currently, many automotive companies struggle with quality issues. To reinforce quality management and to reduce defects of parts or products, tomography can be deployed. Tomography is a method of producing a sectional image of the internal structures of an object using waves of energy (Isaksen, 1996; Al-Afeef, 2010; Al-Afeef et al., 2010, 2011). Technically, tomography involves taking direct sectional images (for instance X-ray, infrared or ultrasound tomogram) or reconstructing indirect sectional images using boundary measurements based on the internal characteristics of the monitored object (for instance electrical tomogram) (Beck and Williams, 1996). It is one of the few feedback tools that give information about what is actually happening inside an industrial process (Marashdeh et al., 2006; Hoyle, 1996). This information is extremely important to support quality management, to develop processes efficiently and to reduce production costs. Additionally, it can contribute to a simplification of processes and to the maturity of products. In this paper, we propose a general framework for monitoring and control of industrial manufacturing processes which supports quality management. A description of the main components of the proposed system architecture will be given in Section 2. The adopted genetic programming (GP) technique is presented in Section 3. In Section 4, the electrical capacitance tomography (ECT)-based GP toolbox architecture will be shown. The GP solver model is outlined in Section 5. Afterwards in Section 6, the simulation results which are based on the collected experimental data are illustrated. The framework also embeds the idea of a closed-loop control including remote access options for mobile devices. The multi-tier quality control system is described in Section 7. Furthermore, additional aspects from a business perspective are discussed in Section 8. Section 9 summarises the paper and includes a future outlook. Despite the fact, that the ideas discussed in this paper are focused on lost foam casting (LFC) processes in the automotive industry, it is important to notice, that the results of our research can be transferred to many fields of application as well.
2 A monitoring and control manufacturing processes
framework
for
At a glance, we propose a remote monitoring and control framework which includes a multi-tier closed-loop controlling system. Figure 1 shows a block diagram of the proposed framework. At first, a general description of the components is given.
2.1 Production activity control Usually, manufacturing processes involve a production activity control (PAC) subsystem which is in charge of implementing the master production schedule and material supply. The system also concerns with the optimisation of labour and equipment, and minimises work-in-process inventory. A PAC system must guarantee that the necessary resources are accessible to manufacture the products according to a time plan that meets the schedule of product delivery (McMahon and Browne, 1993). In most cases they exchange data with enterprise resource planning (ERP) systems by automated interfaces.
2.2 Enterprise resource planning An ERP system incorporates the management of internal and external management information of the whole organisation. This includes production planning, material management, human resources, controlling, finance, sales and distribution (Brown and Vessey, 1999). ERP systems support handling and analyses of this information by using software (Bingi et al., 1999). Based on the PAC data, further analyses can be done by the analysis and reporting components of the ERP system. For instance, the evaluation of the number of errors by means of current cost parameters might be useful for several stakeholders, like production controller, production planner or quality manager. The results can also be useful for the accounting department which may need the data for calculating the costs of single items of work. Additionally, comparisons of actual figures, for example the error rates of two following periods, can be generated by the ERP-system.
A monitoring and control framework for lost foam casting manufacturing processes Figure 1
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Remote monitoring and control framework (see online version for colours)
access by mobile devices
Matlab-server
ECT-system
production activity control
production supervision/ quality management
2.3 Business intelligence On the strategic level it is also conceivable to use business intelligence (BI) tools for further analyses. The BI level mainly addresses management and the controlling department. For example, the analysis of the quantities of parts with blowholes in terms of production lines and time is possible using online analytical processing (OLAP). Expenses such as those caused by process errors can be analysed in terms of the criterion ‘time’. Moreover, it is possible to distribute the analysis results for each controlling level to mobile clients for persons needing such information. So, for instance, production managers can remotely control their production processes via notebooks or mobile phones. Figure 2
Cylinder head and block with foam casting
Source: Barnett (2002)
2.4 LFC and ECT LFC is a casting process that uses foam patterns as molds in which the molten metal decomposes the foam pattern and creates a casting in its shape (Al-Afeef et al., 2010; Abdelrahman et al., 2006, 2009). LFC is among those
ERP-system
production planning/ production controlling/ quality management
BI-system
business analysis by e.g. management/ controlling
applications that use the ECT technique to express the metal fill profile and to simulate the properties of molten metal inside the foam patterns during the casting process (Abdelrahman et al., 2009). LFC is very simple, and it is cheap to cast very complex patterns. The control of LFC processes can save a lot of money (Barnett, 2002). In Figure 2, we show an example of a cylinder head and block with foam casting which was presented in Barnett (2002). The authors report that (Barnett, 2002): “General Motors is demonstrating an improved aluminum cast molding process for their L61 engine block and head that uses accurate methods of measuring refractory coating thickness and other key quality factors that will improve overall production efficiency, and lower production costs and emissions......A 5% to 8% improvement in product quality is expected with the improved process. A plant producing 2,100 L61 heads and blocks a day for 220 days a year should reduce energy costs by $604,000 to $967,000 annually.”
Over the last three decades, much effort has been made for developing a wide variety of imaging techniques for industrial process applications (Kalpakjian and Schmid, 2006; Degarmo et al., 2003). Among these techniques, ECT-based approaches have become very popular. ECT has been applied successfully to study various industrial processes using capacitance measurements to generate images, for instance gas/liquid flows (White, 2002), water/oil/gas separation processes (Neumayer et al., 2011) and many others. ECT is a method which determines the dielectric permittivity distribution in the interior of an object from external capacitance measurements. It has the advantage of not requiring direct contact between the sensors and the object under inspection (non-invasive) and is not changing the characteristics of the object being explored (non-intrusive) (Lei et al., 2008). Compared to hard field tomography, it is fast and relatively inexpensive. Safety for humans and the environment are additional advantages. However, the quality and accuracy of the reconstructed
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images of ECT measurement systems are often insufficient (Hoyle et al., 1992). Especially in terms of the mentioned challenges concerning car parts’ quality, this is an important issue. It can be managed with evolutionary software which is a component of the presented framework in Figure 1. There are two computational problems in ECT image reconstruction (Alme and Mylvaganam, 2006). They can be summarised:
base. The results produced by the PAC component can be evaluated by quality and production managers. They have the possibility to control processes and to take action in time, if incidents occur. As mentioned before, it is also possible to establish an ERP interface and to supply a BI system with data. As a crucial part of data supply, the GP-based approach will be outlined in the next sections.
•
3 Genetic programming
The first is the forward problem in which the capacitance measurement Cij between electrodes i and j is determined from the permittivity distribution ε(x, y) [see equation (1)]. Cij = F (ε(x, y))
•
(1)
Additionally, the inverse problem has to be solved which is the process of finding the inverse relationship such that the permittivity distribution is estimated using capacitance measurements and (as a result) constructing a visual image by using a reconstructing algorithm. This process is called image reconstruction process. The inverse relationship can be expressed as in equation (2). ε(x, y) = F −1 (C12 , C13 , C14 , . . . , Cij , . . . , CN −1,N )
(2)
To see how this idea works, consider the ECT non-linear inverse system with a set of capacitance inputs C1 , C2 , . . . , Cm variables and an image vector G which contains image pixels [G1 , G2 ,. . . , Gp ]. The relationship between these variables can be represented as: Gi = F (C1 , C2 , ..., Cm )
(3)
Our objective is to find a set of inverse solver that describes the non-linear relationship F between the input Ci , i = 1, . . . , m and the output image G, as shown in Figure 3. Figure 3
Representation of the inverse solver system (see online version for colours)
GP is a branch of the evolutionary computation (EC) techniques, in which it repeatedly searches for a global optimal solution of a given problem. The user has to define the function set and the model input variables along with the tuning parameters of the evolutionary process. During the past few decades, GP has been used to solve diversity of problems in computer science and engineering such as data mining (Colin¸ 1997), robotic control (Akbarzadeh-T et al., 2000), software cost estimation (Sheta and Al-Afeef, 2003) and bioinformatics (Ritchie et al., 2007). The theoretical foundations of GP are summarised in Langdon and Poli (2002). GP proceeds by initially generating a population of random trees (i.e., programmes) and computing their fitness as a measure of the solution quality for the given problem. The better solutions are selected for genetic breeding (i.e., crossover, reproduction, and mutation) to form a new population. This process of selection and breeding iterates until some stopping criterion is satisfied (Koza, 1992). The key advantage of GP when compared to traditional modelling approaches is that it does not assume any a priori functional structure of the solution. For instance, in a typical regression method, a model structure is defined in advance and the model coefficients are determined. For neural networks (NN), the time-consuming task of initially defining the network structure has to be undertaken and then the coefficients (i.e., weights) are found by the learning algorithm. GP has many advantages over NN, since GP finds both the function which describes the relationship between the model input and output along with the values of the model parameters without any interference of the user.
4 ECT-GP modelling toolbox
The proposed inverse solver system consists of 64 models (Al-Afeef et al., 2011). We propose a GP-based model in our case. Each model is responsible on deriving a relationship between capacitance measurements C1 . . . C66 and each pixel of the 64 pixels in the ECT image. Deploying the ECT approach in combination with the GP-based image reconstruction, data from the manufacturing process execution layer can be monitored and analysed using a controlling system on a regular
ECTGP is a MATLAB toolbox for solving the non-linear inverse problems of ECT (Al-Afee, 2010). The toolbox helps in setting experiments setup, analyse and report results by using a graphical user interface (GUI). ECTGP is integrated with the Lilgp (C language package for developing GP applications). ECTGP was developed to provide a free toolbox that can be used and further developed by others. At a glance, Figure 4 shows the components of the ECT GP . There are four main modules, namely:
A monitoring and control framework for lost foam casting manufacturing processes •
view ECT module
•
GP module
•
GP performance analysing module
•
ECT result analysing module
Each module represents an interaction point with the user. The first module can be executed after loading the input data, while the last three modules are executed from top to bottom (i.e., sequentially). All modules are executed using a GUI Matlab callback which enables the user to work with the ECTGP toolbox graphically. Figure 4
The architecture of the ECT-GP system (see online version for colours)
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pixel P of image vector G which represents the distribution of metal in the imaging area (Al-Afee, 2010; Al-Afeef et al., 2011), as in equation (4). Gp = F (C1 , C2 , . . . , C N (N −1) )
(4)
2
In our case, the inverse solver consists of 64 GP models. Each model is responsible for deriving a relationship between capacitance measurements [C1 − C66 ] and a specific pixel in an ECT image of 64 pixels. In another word, P in equation (4) is ranging from 1 to 64 pixels, N is 12 electrodes. In Figure 5, the structure of the proposed system is shown. The number of measurements is 66 representing all the unique combinations of measurements between the 12 electrodes distributed around the measuring area. These measurements are represented by the symbols C1 , ..., C66 . One at a time, all the measurements are presented to each of the 64 GP systems. Figure 5 ECT-GP proposed system (see online version for colours)
6 Simulation results
Meanwhile, it is also possible to use a MATLAB mobile lightweight desktop on a mobile device. So, the users can work remotely, send commands, run scripts and see results, for instance a graphical representation of figures on a smart phone. The mobile device can remotely access the simulation of the manufacturing processes and provides an update of the simulations’ progress for controlling purposes.
5 GP inverse solver model Our objective is to find a GP inverse solver that mathematically describes the non-linear relationship F between the capacitance input variables C and the image
The GP was trained using Lilgp with ANSYS generated examples (Abdelrahman et al., 2009; Al-Afeef et al., 2011). A training set of 268 ECT images with the corresponding capacitance measurements was provided to the GP system. To test the performance of the GP models, a testing set of size 67 was used. One of the major problems in GP is over-fitting in which the algorithm tends to follow a pattern based on the learning samples. To resolve this problem, different datasets for testing and training were used in which the selected testing dataset was different enough for testing the performance of the GP Inverse Solver. The Best-so-far curve of the run is shown in Figure 6. It visualises errors (y-axis) of the generations (x-axis) for all models (z-axis).
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Figure 6
Best-so-far curve of the GP developed models (see online version for colours)
Table 1 GP control parameters
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Fitness ( Error )
50 40 30 20 10
0 20 40 60 80
20
10
100
Generation
60
50
40
30
Gset − OAE , where Gset β α ∑ ∑ Actual G OAE = − GEstimated
EPset =
(5)
where set is either the training or testing set of experiment. EP is the error percentage of a given set. Gset is the total number of pixels of all images in that set. Overall absolute error (OAE) is the summation of the absolute difference between the actual and the GP estimated image pixels of all images in the set. α is the total number of images in the set. β represents the image-size (i.e., the total number of pixels in a single image in the experiment). GActual and GEstimated are the actual and estimated pixels. The overall error percentage for the developed GP simulation in the training and testing cases were 2.53% and 2.63%. A subset of these patterns and their corresponding GP estimated patterns are shown in Figure 7. A sample of actual and estimated patterns using GP: (1–3) training cases, (4–5) testing cases
Actual 1
Actual 2
Actual 3
Actual 4
Actual 5
8
8
8
8
8
6
6
6
6
6
4
4
4
4
4
2
2
2
2
2
4
6
8
2
Estimated
4
6
8
2
Estimated
4
6
8
2 2
Estimated
4
6
8
2
Estimated
8
8
8
8
6
6
6
6
6
4
4
4
4
4
2
2
2
2
2
4
6
8
2
4
6
8
2
|Error|=0.52774
4
6
8
4
6
8
2
|Error|=0.86542
8
8
8
8
6
6
6
6
6
4
4
4
4
4
2
2
2
2
2
4
6
8
2
4
6
8
2
30,000 100 90% 10% 17 6 0% 0% Half and half Tournament, size = 7 Used Not used
4
6
8
8
2
4
6
4
6
8
7 Multi-tier quality control The image representations can be used for the process of quality control and for further analyses of the manufacturing process. For example it is possible to monitor and analyse data from the operational process layer on a regular base by means of the proposed controlling system. Key figures, like the error rate ERt which indicates the probability of quality problems during a certain period t, can be used for quality management [see equation (6)]. ERt =
P Et ∗ 100 P Et + P S t
(6)
P Et represents the number of manufacturing process iterations with errors in terms of the image analysis. P St denotes the number of successful process iterations. The validity of the error rate depends on the analysed sample size. If the relative amount of deviations between the image representations and the expected values increases, it might be necessary for the production supervision or the quality manager to take action. Dependent on the industry and the quality management system, the quality requirements can be very high. For LFC processes, a real time reporting of possible error cases as well as successful filling performances of sample sets to the PAC system is necessary. The results produced by the PAC can be shared by the users remotely. If a certain key figure violates a threshold, action might be required promptly. Additionally, ideas to improve the production processes can be generated. During the following process iterations, the positive or negative effects of those changes will be measured, and process changes can be evaluated. So, a closed-loop controlling is achieved.
|Error|=0.825
8
2
6
2 2
|Error|=1.4944
4
Estimated
8
|Error|=0.000515
Value
Population size Max. no. of generations to be run Prob. of crossover Prob. of reproduction Max. size for S-expressions created in the run Max. size for initial random S-expressions Prob. of mutation Prob. of permutation Random population generating method Selection method Adjusted fitness Over selection
Pixel ( Run )
The fitness function selected to develop the 64 GP models was defined as the error percentage (EP) for the developed models (Al-Afeef et al., 2011). The EP is given in equation (5).
Figure 7
Parameter
8
8 Additional aspects from a business perspective 2
4
6
8
The GP control parameters are listed in Table 1 with a default set of breeding parameters that emulates (Koza, 1992).
Due to the accelerated distribution of information, action can be taken as fast as possible in case key figures violate thresholds. High rejection and reworking costs which increase when defects are detected very late may
A monitoring and control framework for lost foam casting manufacturing processes be avoided. Furthermore, production and quality managers also benefit from the improved transparency. Problems concerning the product development process as well as issues on the operating level become immediately visible. Performance monitoring and process analyses help to establish continuous improvements and indicate, whether the actual quality goals can be achieved. The fully automated transfer of quality data to an information system also saves costs. Additionally, permanent comparisons of the actual and the planned quality parameters help to prevent contractual penalties which are charged in case of poor quality deliveries. Product recalls are avoided, and positive impacts on the image can be achieved. On the strategic level the management is supported by efficient planning and controlling instruments. Current reports contribute to the basis of information for decisions. On the other hand, costs for technical equipment have to be considered. These costs include, for instance, expenses for the ECT system, software components, setup and training costs, data transfers and operating costs. It should be taken into account that external consultants may be needed. Additionally, the employees have to be trained. It is also advisable to couple the quality management approach with an incentive system. Improvements in performance should be communicated and rewarded. Nevertheless, solely the avoidance of rejection and reworking costs can save a lot of money. So, it can be assumed that the benefits will exceed costs in most cases. Nevertheless, it is recommended to analyse costs and benefits in detail before introducing the presented technologies.
9 Conclusions and future work The multi-tier quality controlling approach and the implementation of ECT can be an important contribution to companies’ quality management. Expensive product recalls and the related image damages can be avoided or at least reduced. On the other hand, the benefit of this technology depends on the quality of the ECT reconstructed images. In the past, accuracy and quality of the reconstruction results were not always sufficient for many industries. In this paper, a new technique for solving the non-linear inverse problem of ECT has been introduced. The technique is based on GP to identify the models relating the sensors’ capacitance measurements to the permittivity distributions. The modelling process is described and the estimated models are validated for cases of single and multiple filling points. The presented technique showed promising results in terms of accuracy, the quality of reconstruction results and convergence rates. Furthermore, it could be shown that companies can benefit a lot from the proposed framework. The main limitation of the GP approach is the training time needed. Sufficient training data has to be provided, and data has to be expressive of the problem in order to have a successful prediction. As a future work, it is intended to investigate other metal distributions and to apply GP to the forward problem of ECT. Additionally, the development of a process in order
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to improve the reconstructed images using look-up tables with all possibilities of grid formation to reduce the training time is needed. It would be also very interesting to extend the analysis and reporting components.
Acknowledgements ANSYS data for LFC was provided by Drs. Mohamed Abdelrahman and Wael Deabes, Tennessee Technological University. Data was produced in conjunction with research project GO14228 supported by the US Department of Energy (DOE), USA.
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