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Production Rescheduling for Contract Manufacturing Industry based on Delivery Risks
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www.elsevier.com/locate/procedia 4th International Conference on System-Integrated Intelligence
4th International Conference on System-Integrated Intelligence
Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting tool Cluster identification of sensor data for machine predictive maintenance in a Manufacturing Engineering Society International Conference 2017, MESIC Selective Laser Melting machine tool 2017, 28-30 June a, Eckart Uhlmanna,b, Rodrigo PastlVigo Pontes *, Claudio Geiserta, Eckhard Hohwielera 2017, (Pontevedra), Spain a,bfor Production Systems and Design Technology a Fraunhofer Institute (IPK), Pascalstraße Berlin 10587,Hohwieler Germany Eckart Uhlmann , Rodrigo Pastl Pontesa,*, Claudio Geiserta8-9, , Eckhard a
Technische Universität Berlin for – Institute for Machine Tools and Factory Management Pascalstraße 8-9, BerlinTrade-off 10587, Germany Costing models capacity optimization in(IWF), Industry 4.0: Fraunhofer Institute for Production Systems and Design Technology (IPK), Pascalstraße 8-9, Berlin 10587, Germany Technische Universität Berlin – Institute for Machine Tools and and Factory Management (IWF), Pascalstraße 8-9, Berlin 10587, Germany between used capacity operational efficiency b
a
b
Abstract
A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb
Abstract Selective laser melting has become one of a the most current new technologies used to produce complex components in comparison University of Minho, 4800-058 Guimarães, Portugal to conventional manufacturing technologies. b Especially, existing selective laser melting machine tools are not equipped with Unochapecó, 89809-000 Chapecó, SC, Brazil Selective laser melting has become of This the most current new technologies used to produce complex components in comparison analytics tools that evaluate sensorone data. paper describes an approach to analyze and visualize offline data from different to conventional technologies. existing selective laser melting machine tools areillustrate not equipped with sources based onmanufacturing machine learning algorithms. Especially, Data from three sensors were utilized to identify clusters. They the normal analytics tools evaluate data.faulty This conditions. paper describes approach analyze and visualizesystem offlinecan databefrom different operation of thethat machine toolsensor and three With an these results,toa condition monitoring implemented sources basedthose on machine from three sensors were utilized to identify clusters. They illustrate the normal that enables machinelearning tools foralgorithms. predictive Data maintenance solutions. Abstract operation of the machine tool and three faulty conditions. With these results, a condition monitoring system can be implemented that2018 enables machine tools by forElsevier predictive maintenanceprocesses solutions. will be pushed to be increasingly interconnected, © The those Authors. Published Under the concept of "Industry 4.0", Ltd. production This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/ ) information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization © 2018 2018 The The Authors. Published by Elsevier © Authors. Published by Elsevier B.V. Ltd. Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated goes beyond traditional aim ofthecapacity maximization, contributing also for organization’s profitability and value. This is an openthe access article under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Intelligence. Peer-review under responsibilityand of the scientific committee of the 4th Internationalsuggest Conference on System-Integrated Indeed, lean management continuous improvement approaches capacity optimization Intelligence. instead of Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated maximization. The study of capacity optimization and costing models is an important research topic that deserves Intelligence. Keywords: Selective laser melting, sensor data, machine learning, clustering, predictive maintenance
contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model forSelective capacity management on different models (ABC and TDABC). A generic model has been Keywords: laser melting, sensorbased data, machine learning,costing clustering, predictive maintenance developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s 1. Introduction value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity 1. Introduction optimization might of hide inefficiency. The monitoring theoperational health conditions of the machine tools and products plays a special role [1]. The results of © 2017 The Authors. Published by Elsevier B.V. to improve the quality of their products. Selective Laser Melting (SLM) this monitoring help machine manufacturers Peer-review under responsibility of that the scientific committee of the Manufacturing Engineering Conference monitoring of the health conditions of the machine tools and products aSociety special role The results of is aThe manufacturing technology has been demanded more frequently over plays the years dueInternational to its[1]. capacity to build 2017. this monitoring help machine manufacturers to improve the quality of their products. Selective Laser Melting (SLM) metallic components with more complex geometries when compared to conventional manufacturing is a manufacturing technology that has been demanded more frequently over the years due to its capacity to build Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency metallic components with more complex geometries when compared to conventional manufacturing Introduction *1.Corresponding author. Tel.: +49-030-39006-166; fax: +49-030-39110-37. E-mail address: [email protected] * The Corresponding author. Tel.: +49-030-39006-166; +49-030-39110-37. cost of idle capacity is a fundamentalfax: information for companies and their management of extreme importance E-mail address: [email protected] 2351-9789 ©production 2018 The Authors. Published by Elsevier in modern systems. In general, it isLtd. defined as unused capacity or production potential and can be measured This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) in several ways: tonsunder of production, available hours of manufacturing, etc. The management of the idle capacity Selection peer-review responsibility of the scientific 2351-9789and © 2018 The Authors. Published by Elsevier Ltd. committee of the 4th International Conference on System-Integrated Intelligence. * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) E-mailand address: [email protected] Selection peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under of the scientificbycommittee the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018responsibility The Authors. Published Elsevier of B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence. 10.1016/j.promfg.2018.06.009
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processes [2, 3, 4]. Nonetheless, the existing SLM machine tools are often not able to ensure the product quality, due to several factors, including failures occurred during manufacturing processes [5]. Such machine tools are not equipped with analytics tools that evaluate the machine data, which makes the identification of the factors and the conditions of operation a challenging task. A first study focusing on classification and clustering of an SLM machine tool process data and sensor data was performed in [6]. In this work, machine learning algorithms were used to classify the process status into three predefined categories and used to find clusters in the data. However, the clusters found were not well analysed and no deeper observation was carried out regarding machine behaviour conditions. Therefore, the goal of this work is to answer two main questions. The first one is to know if it is possible to identify machine tool’s behavioural groups (or clusters) using data from three of the sensors used in [6]. The second question is to discover the proper number of clusters and which statistical features could be used in order to use them to foresee the machine tool’s behaviour for the use in the context of predictive maintenance. 2. Selective Laser Melting Additive Manufacturing (AM) is a technique to manufacture workpieces through addition of material, by joining layers with equal thickness [4, 7]. SLM is a special technology of AM. It is a manufacturing process that uses a metal powder bed and a thermal energy supplied by a computer controlled and focused laser beam to build a workpiece [2, 6, 8, 9]. The layer thickness varies from 20 µm to 150 µm and the size of the metal grains has a range from 10 µm to 75 µm [7, 10]. 3. Methodology 3.1. Overview The methodology performed in this work is shown in Fig. 1. Firstly, raw machine tool sensor data from 206 manufacturing processes were acquired and stored. The SLM machine model used to gather the data is the SLM 250HL from the company SLM Solutions AG, Germany. Then, the data from three sensors related specifically to the machine operation were chosen and pre-processed. Those sensors are the platform temperature (T), oxygen percentage within the process chamber (O) and the process chamber pressure (P). They were selected because they are controlled by the machine tool’s software. Therefore, if a failure occurs during a manufacturing process, it may influence those parameters and the sensors would be able to detect a variation. SLM machine tool sensor data acquisition
Sensor data pre-processing
Cluster analysis and assessment
Conclusions
Fig. 1. Performed methodology.
After the pre-processing stage, the cluster analysis was carried out. The results of this analysis were assessed in order to find the proper number of clusters according to the statistical features from those three sensors. The program to read, pre-process the data and to analyse the clusters was written using the programming language Python (version 3.6) and the software tool Spyder 3 [11]. 3.2. SLM machine tool sensor data acquisition The used SLM machine tool delivers a table in comma separated values (CSV) format after each manufacturing process, resulting in a file. Inside this file there are data recorded every second from 20 sensors of the machine. These
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3
sensors (analogical and digital) monitor several parameters, such as the temperature of the platform, the status of the powder tank, among others. The files of 206 manufacturing processes were gathered in order to be pre-processed. 3.3. Sensor data pre-processing The pre-processing stage was performed in the following way. The 206 files were collected using the software tool Spyder 3 [11]. Then, only data within the interval from the first layer until the end of the manufacturing process were considered. It represents when a workpiece is being manufactured. Next, the data of the three sensors were separated into three independent columns according to the manufacturing process. After that, the statistical features minimum value, skewness, maximum value, mode, median, average, and standard deviation from each column (T, O, and P) were calculated and stored in a two-dimensional matrix with seven rows and three columns, representing the chosen statistical features and the observed sensors, respectively. This matrix characterizes a single manufacturing process. By taking all considered files, a three-dimensional matrix with a size 7 x 3 x 206 was created, where the last dimension represents the number of manufacturing processes. After the calculation and proper storage of all statistical features, every statistical feature (minimum value, skewness, maximum value, mode, median, average, and standard deviation) from each sensor (T, O, and P) was scaled taking into account the 206 files and the Equation 1, which shows the standardisation calculation. ‘xstandard’ is the resulting value of the statistical feature, ‘x’ is the original value, ‘µ’ is the average of the dataset related to the statistical feature, and ‘σ’ is the standard deviation of the dataset for the specific statistical feature. This was performed in order to avoid false results from the clustering algorithm due to the scale difference of each feature.
standard
(1)
σ
3.4. Cluster analysis and assessment
Sum of Square Errors (SSE)
After the data were prepared and scaled in the pre-processing stage, the algorithm k-means was used in order to perform the identification of clusters. This algorithm separates the observed points into k groups, where an observed point is associated with the closest average of a predefined cluster [12]. This algorithm needs a specified number of clusters in order to separate the data. Therefore, the number of clusters was defined using the elbow method.
Elbow Method 10,000 10000 7,500 7500 5000 5,000 2,500 2500 00
Elbow
1
2
3
4 5 Number of clusters k
6
7
8
Fig. 2. Example of an elbow method chart.
By using the elbow method, the number of clusters can be estimated observing the behaviour of a generated curve that takes the sum of squared errors (SSE) into account. Defining a range of values from clusters k on the dataset (for instance, from one to eight clusters), the SSE is calculated for each number of clusters k. It results in a line chart similar to Fig. 2. In the figure, the form of an elbow appears with number of clusters k as ‘3’. Such point indicates the most adequate number of clusters for the dataset. Experts of the process can confirm this clusters number. Then, each sensor was considered as a dimension, resulting in a three-dimensional space, where the combination of each feature from the sensors was performed. Consequently, the best combination was analyzed using the calculation of distortion (the mean sum of squared distances to centers) within the dataset.
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4. Results The resulting number of features combinations was 343. Fig. 3 shows examples of elbow method charts and the respective charts representing the clusters for the triple (T, O, P) for two combinations. Fig. 3a and Fig. 3c are related to the combination number 94, and Fig. 3b and Fig. 3d are the results for the Combination 234. It can be noticed that both examples had the three clusters of operation. The number of clusters varied from two to four depending on the combination of features of three sensors. If those clusters represent failures, it is not known at this phase. What has been confirmed at this point is the possibility of identifying groups in the given data. This answers one of the given questions at the introduction section.
Combination 94 – Estimated Clusters
Combination 234 – Estimated Clusters 800
Sum of Square Errors (SSE)
600 400 200 0
a)
1
2
3 4 5 6 Number of clusters k
600 400 200 0
7
1
b)
3 4 5 6 Number of clusters k
d)
c) Legend:
7
Combination 234 – Clusters Distribution
Pressure P (minimum value)
Combination 94 – Clusters Distribution
2
Pressure P (maximum value)
Sum of Square Errors (SSE)
800
X : cluster centroid
cluster 1
cluster 2
cluster 3
Fig. 3. Examples of the clustering identification from two combinations: a) elbow method chart for combination 94; b) Clusters from combination 94; c) elbow method chart for combination 234; d) Clusters from combination 234.
In order to discover the proper number of clusters and statistical features to use for behaviour prediction of the machine tool, the mean sum of squared distances to centers (distortion), for every combination and number of clusters was calculated. The ten best results are given in Table 1. The lower the distortion, the more accurate the number of clusters. The highlighted lines in the same table show the chosen features and clusters to be monitored.
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Table 1. Ten first results of features and number of clusters according to distortion. Combination number
Temperature
Oxygen
Pressure
Number of clusters
Distortion
337
Standard Deviation
Maximum
Minimum
4
0.45
343
Standard Deviation
Standard Deviation
Standard Deviation
4
0.52
335
Standard Deviation
Standard Deviation
Skewness
4
0.58
337
Standard Deviation
Maximum
Minimum
3
0.65
343
Standard Deviation
Standard Deviation
Standard Deviation
2
0.67
338
Standard Deviation
Maximum
Standard Deviation
2
0.70
335
Standard Deviation
Standard Deviation
Skewness
3
0.71
320
Standard Deviation
Maximum
Skewness
3
0.71
334
Standard Deviation
Standard Deviation
Minimum
3
0.74
89
Skewness
Standard Deviation
Skewness
3
0.83
Fig. 4 brings the clusters visualization of both combination 337 and 343. Fig. 4a shows the clusters of combination 337 while Fig. 4b represents the clusters of combination 343. According to interviews with the operators and engineers that are responsible for using this specific SLM machine tool, the number of clusters represents four real behaviours of the machine. The data in the process log files of the manufacturing processes confirm those behaviours. These four clusters represent the normal operation, failures from the pressure system, failures from the protection gas system and when the machine is stopped due to a general faulty situation.
Pressure P (standard deviation)
Combination 343 – Clusters Distribution
Pressure P (minimum value)
Combination 337 – Clusters Distribution
a)
b)
Legend:
X : cluster centroid
Normal operation
Pressure failure
Protection gas failure
Machine stopped
X : cluster centroid
Normal operation
Pressure failure
Machine stopped
Protection gas failure
Fig. 4. Clusters distribution: a) four clusters of the combination 337; b) four clusters of the combination 343.
This SLM machine tool does not recognize all types of failures, and therefore, in such occasions no failure message is provided. One example is the variation of the platform temperature during operation. Although the machine tool controller does not understand this as a faulty situation, this is a situation where the quality of the manufactured
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workpiece can be influenced. In this situation, the temperature gradient between the bed powder and the melted pool is high, leading to higher stress inside the workpiece and, consequently, to a defective workpiece. When a malfunction in the protection gas system appears, the percentage of oxygen varies until it reaches its maximum permitted value inside the chamber, stopping the machine. Therefore, some systems are turned off, including the platform heater. After analyzing the root cause of the failure, identified that cause was that the inlet control valve from the protection gas system was not working properly, not allowing the protection gas to reach the process chamber within the desired range. Hence, the cluster region “Protection gas failure” from both diagrams could be used to act proactively in the process in order to avoid defective components. In addition, this could also show, for instance, failures in those two systems due to poorly realized maintenance activities. 5. Conclusion This work presented the possibility to identify machine tool’s operational groups using data from sensors of the platform temperature, oxygen percentage in the process chamber and process chamber pressure. Moreover, seven statistical features from those three sensors were combined resulting in 343 diagrams. Then, those combinations were assessed according to their distortion to determine which combination of features has the proper number of clusters. Two combinations were chosen. The first one is the Temperature standard deviation, Oxygen maximum value, Pressure minimum value with 4 clusters. The second combination also has 4 clusters and takes into account the standard deviation of the Temperature, the Oxygen, and the Pressure. The machine tool’s process log data and e perts that use the machine tool confirmed all four clusters from both combinations. Those clusters represent the normal conditions of operation, faulty conditions related to the protection gas system, faulty conditions related to pressure system and faulty conditions that maintain the machine tool in a standby situation. These results help to answer the two questions made at the beginning of this work. The outcomes are a first step towards the implementation of a condition monitoring system that enables SLM machine tools for predictive maintenance solutions, because the actual condition of the machine and process is monitored and an early fault detection of specific systems can be performed. As predictive maintenance for SLM machine tools is a relative new topic, future works will address the online observation of the combinations during a manufacturing process in order to identify more faulty situations. This could lead to a quicker reaction to those situations by predicting them, avoiding waste of resources and decreasing the maintenance costs. Moreover, more analysis of the data will be carried out to have more precise information with regard to the possibility to identify single failures in the dataset and the relationship between different failures. References [1] E. Uhlmann, C. Geisert, E. Hohwieler, Lifecycle Monitoring – Integration in Service-Prozesse und Produktdatenmanagement. In: Biedermann, H. (edt.): Total Productive and Safety Maintenance. TÜV Media GmbH, Köln 2012, pp. 125 – 132. [2] J. P. Kruth, Material Increase Manufacturing by Rapid Prototyping Techniques, CIRP Annals – Manufacturing Technology, 40/2, 1991, pp. 603-614. [3] J. P. Kruth, M. C. Leu, T. Nakawa, Progress in Additive Manufacturing and Rapid Prototyping, CIRP Annals – Manufacturing Technology 47/2, 1998, pp. 525-540. [4] M. F. Zaeh, Wirtschaftliche Fertigung mit Rapid-Technologien – AnwenderLeitfaden zur Auswahl geeigneter Verfahren. Munich: Hanser, 2006. [5] E. Uhlmann, A. Bergmann, Markt- und Trendstudie 2013 Laserstrahlschmelzen; ISBN: 978-3-945406-00-7; Fraunhofer IPK, 2014. [6] E. Uhlmann, R. P. Pontes, A. Laghmouchi, A. Bergmann, Intelligent pattern recognition of a SLM machine process and sensor data. Procedia CIRP, Volume 62, 2017, pp. 464-469, ISSN 2212-8271. [7] A. Gebhardt, J. Hötter, Additive Manufacturing: 3D Printing for Prototyping and Manufacturing. Munich: Hanser, 2016. [8] K. McAlea, P. Forderhase, U. Hejmadi, C. Nelson, Materials and Applications for the Selective Laser Sintering Process. Proceedings of the 7th International Conference on Rapid Prototyping, San Francisco, 1997, pp. 23-33. [9] G. N. Levy, R. Schindel, J.-P. Kruth, Rapid Manufacturing and Rapid Tooling with Layer Manufacturing (LM) Technologies, State Of the Art and Future Perspectives. CIRP Annals, Vol. 52/2, 2003. [10] E. Uhlmann, K. Urban, Markt- und Trendstudie 2010 Laserstrahlschmelzen; ISBN 978-3-9814405-1-5; 2011 Fraunhofer IPK. [11] The Spyder Project Contributors. 2018. Spyder documentation. Available at: http://pythonhosted.org/spyder/. Accessed 22 February 2018. [12] J. Zhou, D. Wen, An improved K-means Clustering Algorithm, Xi’an, 27th-29th May, 2011, pp. 44-46.
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www.elsevier.com/locate/procedia 4th International Conference on System-Integrated Intelligence
4th International Conference on System-Integrated Intelligence
Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting tool Cluster identification of sensor data for machine predictive maintenance in a Manufacturing Engineering Society International Conference 2017, MESIC Selective Laser Melting machine tool 2017, 28-30 June a, Eckart Uhlmanna,b, Rodrigo PastlVigo Pontes *, Claudio Geiserta, Eckhard Hohwielera 2017, (Pontevedra), Spain a,bfor Production Systems and Design Technology a Fraunhofer Institute (IPK), Pascalstraße Berlin 10587,Hohwieler Germany Eckart Uhlmann , Rodrigo Pastl Pontesa,*, Claudio Geiserta8-9, , Eckhard a
Technische Universität Berlin for – Institute for Machine Tools and Factory Management Pascalstraße 8-9, BerlinTrade-off 10587, Germany Costing models capacity optimization in(IWF), Industry 4.0: Fraunhofer Institute for Production Systems and Design Technology (IPK), Pascalstraße 8-9, Berlin 10587, Germany Technische Universität Berlin – Institute for Machine Tools and and Factory Management (IWF), Pascalstraße 8-9, Berlin 10587, Germany between used capacity operational efficiency b
a
b
Abstract
A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb
Abstract Selective laser melting has become one of a the most current new technologies used to produce complex components in comparison University of Minho, 4800-058 Guimarães, Portugal to conventional manufacturing technologies. b Especially, existing selective laser melting machine tools are not equipped with Unochapecó, 89809-000 Chapecó, SC, Brazil Selective laser melting has become of This the most current new technologies used to produce complex components in comparison analytics tools that evaluate sensorone data. paper describes an approach to analyze and visualize offline data from different to conventional technologies. existing selective laser melting machine tools areillustrate not equipped with sources based onmanufacturing machine learning algorithms. Especially, Data from three sensors were utilized to identify clusters. They the normal analytics tools evaluate data.faulty This conditions. paper describes approach analyze and visualizesystem offlinecan databefrom different operation of thethat machine toolsensor and three With an these results,toa condition monitoring implemented sources basedthose on machine from three sensors were utilized to identify clusters. They illustrate the normal that enables machinelearning tools foralgorithms. predictive Data maintenance solutions. Abstract operation of the machine tool and three faulty conditions. With these results, a condition monitoring system can be implemented that2018 enables machine tools by forElsevier predictive maintenanceprocesses solutions. will be pushed to be increasingly interconnected, © The those Authors. Published Under the concept of "Industry 4.0", Ltd. production This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/ ) information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization © 2018 2018 The The Authors. Published by Elsevier © Authors. Published by Elsevier B.V. Ltd. Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated goes beyond traditional aim ofthecapacity maximization, contributing also for organization’s profitability and value. This is an openthe access article under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Intelligence. Peer-review under responsibilityand of the scientific committee of the 4th Internationalsuggest Conference on System-Integrated Indeed, lean management continuous improvement approaches capacity optimization Intelligence. instead of Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated maximization. The study of capacity optimization and costing models is an important research topic that deserves Intelligence. Keywords: Selective laser melting, sensor data, machine learning, clustering, predictive maintenance
contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model forSelective capacity management on different models (ABC and TDABC). A generic model has been Keywords: laser melting, sensorbased data, machine learning,costing clustering, predictive maintenance developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s 1. Introduction value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity 1. Introduction optimization might of hide inefficiency. The monitoring theoperational health conditions of the machine tools and products plays a special role [1]. The results of © 2017 The Authors. Published by Elsevier B.V. to improve the quality of their products. Selective Laser Melting (SLM) this monitoring help machine manufacturers Peer-review under responsibility of that the scientific committee of the Manufacturing Engineering Conference monitoring of the health conditions of the machine tools and products aSociety special role The results of is aThe manufacturing technology has been demanded more frequently over plays the years dueInternational to its[1]. capacity to build 2017. this monitoring help machine manufacturers to improve the quality of their products. Selective Laser Melting (SLM) metallic components with more complex geometries when compared to conventional manufacturing is a manufacturing technology that has been demanded more frequently over the years due to its capacity to build Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency metallic components with more complex geometries when compared to conventional manufacturing Introduction *1.Corresponding author. Tel.: +49-030-39006-166; fax: +49-030-39110-37. E-mail address: [email protected] * The Corresponding author. Tel.: +49-030-39006-166; +49-030-39110-37. cost of idle capacity is a fundamentalfax: information for companies and their management of extreme importance E-mail address: [email protected] 2351-9789 ©production 2018 The Authors. Published by Elsevier in modern systems. In general, it isLtd. defined as unused capacity or production potential and can be measured This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) in several ways: tonsunder of production, available hours of manufacturing, etc. The management of the idle capacity Selection peer-review responsibility of the scientific 2351-9789and © 2018 The Authors. Published by Elsevier Ltd. committee of the 4th International Conference on System-Integrated Intelligence. * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) E-mailand address: [email protected] Selection peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under of the scientificbycommittee the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018responsibility The Authors. Published Elsevier of B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated Intelligence. 10.1016/j.promfg.2018.06.009
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processes [2, 3, 4]. Nonetheless, the existing SLM machine tools are often not able to ensure the product quality, due to several factors, including failures occurred during manufacturing processes [5]. Such machine tools are not equipped with analytics tools that evaluate the machine data, which makes the identification of the factors and the conditions of operation a challenging task. A first study focusing on classification and clustering of an SLM machine tool process data and sensor data was performed in [6]. In this work, machine learning algorithms were used to classify the process status into three predefined categories and used to find clusters in the data. However, the clusters found were not well analysed and no deeper observation was carried out regarding machine behaviour conditions. Therefore, the goal of this work is to answer two main questions. The first one is to know if it is possible to identify machine tool’s behavioural groups (or clusters) using data from three of the sensors used in [6]. The second question is to discover the proper number of clusters and which statistical features could be used in order to use them to foresee the machine tool’s behaviour for the use in the context of predictive maintenance. 2. Selective Laser Melting Additive Manufacturing (AM) is a technique to manufacture workpieces through addition of material, by joining layers with equal thickness [4, 7]. SLM is a special technology of AM. It is a manufacturing process that uses a metal powder bed and a thermal energy supplied by a computer controlled and focused laser beam to build a workpiece [2, 6, 8, 9]. The layer thickness varies from 20 µm to 150 µm and the size of the metal grains has a range from 10 µm to 75 µm [7, 10]. 3. Methodology 3.1. Overview The methodology performed in this work is shown in Fig. 1. Firstly, raw machine tool sensor data from 206 manufacturing processes were acquired and stored. The SLM machine model used to gather the data is the SLM 250HL from the company SLM Solutions AG, Germany. Then, the data from three sensors related specifically to the machine operation were chosen and pre-processed. Those sensors are the platform temperature (T), oxygen percentage within the process chamber (O) and the process chamber pressure (P). They were selected because they are controlled by the machine tool’s software. Therefore, if a failure occurs during a manufacturing process, it may influence those parameters and the sensors would be able to detect a variation. SLM machine tool sensor data acquisition
Sensor data pre-processing
Cluster analysis and assessment
Conclusions
Fig. 1. Performed methodology.
After the pre-processing stage, the cluster analysis was carried out. The results of this analysis were assessed in order to find the proper number of clusters according to the statistical features from those three sensors. The program to read, pre-process the data and to analyse the clusters was written using the programming language Python (version 3.6) and the software tool Spyder 3 [11]. 3.2. SLM machine tool sensor data acquisition The used SLM machine tool delivers a table in comma separated values (CSV) format after each manufacturing process, resulting in a file. Inside this file there are data recorded every second from 20 sensors of the machine. These
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sensors (analogical and digital) monitor several parameters, such as the temperature of the platform, the status of the powder tank, among others. The files of 206 manufacturing processes were gathered in order to be pre-processed. 3.3. Sensor data pre-processing The pre-processing stage was performed in the following way. The 206 files were collected using the software tool Spyder 3 [11]. Then, only data within the interval from the first layer until the end of the manufacturing process were considered. It represents when a workpiece is being manufactured. Next, the data of the three sensors were separated into three independent columns according to the manufacturing process. After that, the statistical features minimum value, skewness, maximum value, mode, median, average, and standard deviation from each column (T, O, and P) were calculated and stored in a two-dimensional matrix with seven rows and three columns, representing the chosen statistical features and the observed sensors, respectively. This matrix characterizes a single manufacturing process. By taking all considered files, a three-dimensional matrix with a size 7 x 3 x 206 was created, where the last dimension represents the number of manufacturing processes. After the calculation and proper storage of all statistical features, every statistical feature (minimum value, skewness, maximum value, mode, median, average, and standard deviation) from each sensor (T, O, and P) was scaled taking into account the 206 files and the Equation 1, which shows the standardisation calculation. ‘xstandard’ is the resulting value of the statistical feature, ‘x’ is the original value, ‘µ’ is the average of the dataset related to the statistical feature, and ‘σ’ is the standard deviation of the dataset for the specific statistical feature. This was performed in order to avoid false results from the clustering algorithm due to the scale difference of each feature.
standard
(1)
σ
3.4. Cluster analysis and assessment
Sum of Square Errors (SSE)
After the data were prepared and scaled in the pre-processing stage, the algorithm k-means was used in order to perform the identification of clusters. This algorithm separates the observed points into k groups, where an observed point is associated with the closest average of a predefined cluster [12]. This algorithm needs a specified number of clusters in order to separate the data. Therefore, the number of clusters was defined using the elbow method.
Elbow Method 10,000 10000 7,500 7500 5000 5,000 2,500 2500 00
Elbow
1
2
3
4 5 Number of clusters k
6
7
8
Fig. 2. Example of an elbow method chart.
By using the elbow method, the number of clusters can be estimated observing the behaviour of a generated curve that takes the sum of squared errors (SSE) into account. Defining a range of values from clusters k on the dataset (for instance, from one to eight clusters), the SSE is calculated for each number of clusters k. It results in a line chart similar to Fig. 2. In the figure, the form of an elbow appears with number of clusters k as ‘3’. Such point indicates the most adequate number of clusters for the dataset. Experts of the process can confirm this clusters number. Then, each sensor was considered as a dimension, resulting in a three-dimensional space, where the combination of each feature from the sensors was performed. Consequently, the best combination was analyzed using the calculation of distortion (the mean sum of squared distances to centers) within the dataset.
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4. Results The resulting number of features combinations was 343. Fig. 3 shows examples of elbow method charts and the respective charts representing the clusters for the triple (T, O, P) for two combinations. Fig. 3a and Fig. 3c are related to the combination number 94, and Fig. 3b and Fig. 3d are the results for the Combination 234. It can be noticed that both examples had the three clusters of operation. The number of clusters varied from two to four depending on the combination of features of three sensors. If those clusters represent failures, it is not known at this phase. What has been confirmed at this point is the possibility of identifying groups in the given data. This answers one of the given questions at the introduction section.
Combination 94 – Estimated Clusters
Combination 234 – Estimated Clusters 800
Sum of Square Errors (SSE)
600 400 200 0
a)
1
2
3 4 5 6 Number of clusters k
600 400 200 0
7
1
b)
3 4 5 6 Number of clusters k
d)
c) Legend:
7
Combination 234 – Clusters Distribution
Pressure P (minimum value)
Combination 94 – Clusters Distribution
2
Pressure P (maximum value)
Sum of Square Errors (SSE)
800
X : cluster centroid
cluster 1
cluster 2
cluster 3
Fig. 3. Examples of the clustering identification from two combinations: a) elbow method chart for combination 94; b) Clusters from combination 94; c) elbow method chart for combination 234; d) Clusters from combination 234.
In order to discover the proper number of clusters and statistical features to use for behaviour prediction of the machine tool, the mean sum of squared distances to centers (distortion), for every combination and number of clusters was calculated. The ten best results are given in Table 1. The lower the distortion, the more accurate the number of clusters. The highlighted lines in the same table show the chosen features and clusters to be monitored.
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Table 1. Ten first results of features and number of clusters according to distortion. Combination number
Temperature
Oxygen
Pressure
Number of clusters
Distortion
337
Standard Deviation
Maximum
Minimum
4
0.45
343
Standard Deviation
Standard Deviation
Standard Deviation
4
0.52
335
Standard Deviation
Standard Deviation
Skewness
4
0.58
337
Standard Deviation
Maximum
Minimum
3
0.65
343
Standard Deviation
Standard Deviation
Standard Deviation
2
0.67
338
Standard Deviation
Maximum
Standard Deviation
2
0.70
335
Standard Deviation
Standard Deviation
Skewness
3
0.71
320
Standard Deviation
Maximum
Skewness
3
0.71
334
Standard Deviation
Standard Deviation
Minimum
3
0.74
89
Skewness
Standard Deviation
Skewness
3
0.83
Fig. 4 brings the clusters visualization of both combination 337 and 343. Fig. 4a shows the clusters of combination 337 while Fig. 4b represents the clusters of combination 343. According to interviews with the operators and engineers that are responsible for using this specific SLM machine tool, the number of clusters represents four real behaviours of the machine. The data in the process log files of the manufacturing processes confirm those behaviours. These four clusters represent the normal operation, failures from the pressure system, failures from the protection gas system and when the machine is stopped due to a general faulty situation.
Pressure P (standard deviation)
Combination 343 – Clusters Distribution
Pressure P (minimum value)
Combination 337 – Clusters Distribution
a)
b)
Legend:
X : cluster centroid
Normal operation
Pressure failure
Protection gas failure
Machine stopped
X : cluster centroid
Normal operation
Pressure failure
Machine stopped
Protection gas failure
Fig. 4. Clusters distribution: a) four clusters of the combination 337; b) four clusters of the combination 343.
This SLM machine tool does not recognize all types of failures, and therefore, in such occasions no failure message is provided. One example is the variation of the platform temperature during operation. Although the machine tool controller does not understand this as a faulty situation, this is a situation where the quality of the manufactured
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workpiece can be influenced. In this situation, the temperature gradient between the bed powder and the melted pool is high, leading to higher stress inside the workpiece and, consequently, to a defective workpiece. When a malfunction in the protection gas system appears, the percentage of oxygen varies until it reaches its maximum permitted value inside the chamber, stopping the machine. Therefore, some systems are turned off, including the platform heater. After analyzing the root cause of the failure, identified that cause was that the inlet control valve from the protection gas system was not working properly, not allowing the protection gas to reach the process chamber within the desired range. Hence, the cluster region “Protection gas failure” from both diagrams could be used to act proactively in the process in order to avoid defective components. In addition, this could also show, for instance, failures in those two systems due to poorly realized maintenance activities. 5. Conclusion This work presented the possibility to identify machine tool’s operational groups using data from sensors of the platform temperature, oxygen percentage in the process chamber and process chamber pressure. Moreover, seven statistical features from those three sensors were combined resulting in 343 diagrams. Then, those combinations were assessed according to their distortion to determine which combination of features has the proper number of clusters. Two combinations were chosen. The first one is the Temperature standard deviation, Oxygen maximum value, Pressure minimum value with 4 clusters. The second combination also has 4 clusters and takes into account the standard deviation of the Temperature, the Oxygen, and the Pressure. The machine tool’s process log data and e perts that use the machine tool confirmed all four clusters from both combinations. Those clusters represent the normal conditions of operation, faulty conditions related to the protection gas system, faulty conditions related to pressure system and faulty conditions that maintain the machine tool in a standby situation. These results help to answer the two questions made at the beginning of this work. The outcomes are a first step towards the implementation of a condition monitoring system that enables SLM machine tools for predictive maintenance solutions, because the actual condition of the machine and process is monitored and an early fault detection of specific systems can be performed. As predictive maintenance for SLM machine tools is a relative new topic, future works will address the online observation of the combinations during a manufacturing process in order to identify more faulty situations. This could lead to a quicker reaction to those situations by predicting them, avoiding waste of resources and decreasing the maintenance costs. Moreover, more analysis of the data will be carried out to have more precise information with regard to the possibility to identify single failures in the dataset and the relationship between different failures. References [1] E. Uhlmann, C. Geisert, E. Hohwieler, Lifecycle Monitoring – Integration in Service-Prozesse und Produktdatenmanagement. In: Biedermann, H. (edt.): Total Productive and Safety Maintenance. TÜV Media GmbH, Köln 2012, pp. 125 – 132. [2] J. P. Kruth, Material Increase Manufacturing by Rapid Prototyping Techniques, CIRP Annals – Manufacturing Technology, 40/2, 1991, pp. 603-614. [3] J. P. Kruth, M. C. Leu, T. Nakawa, Progress in Additive Manufacturing and Rapid Prototyping, CIRP Annals – Manufacturing Technology 47/2, 1998, pp. 525-540. [4] M. F. Zaeh, Wirtschaftliche Fertigung mit Rapid-Technologien – AnwenderLeitfaden zur Auswahl geeigneter Verfahren. Munich: Hanser, 2006. [5] E. Uhlmann, A. Bergmann, Markt- und Trendstudie 2013 Laserstrahlschmelzen; ISBN: 978-3-945406-00-7; Fraunhofer IPK, 2014. [6] E. Uhlmann, R. P. Pontes, A. Laghmouchi, A. Bergmann, Intelligent pattern recognition of a SLM machine process and sensor data. Procedia CIRP, Volume 62, 2017, pp. 464-469, ISSN 2212-8271. [7] A. Gebhardt, J. Hötter, Additive Manufacturing: 3D Printing for Prototyping and Manufacturing. Munich: Hanser, 2016. [8] K. McAlea, P. Forderhase, U. Hejmadi, C. Nelson, Materials and Applications for the Selective Laser Sintering Process. Proceedings of the 7th International Conference on Rapid Prototyping, San Francisco, 1997, pp. 23-33. [9] G. N. Levy, R. Schindel, J.-P. Kruth, Rapid Manufacturing and Rapid Tooling with Layer Manufacturing (LM) Technologies, State Of the Art and Future Perspectives. CIRP Annals, Vol. 52/2, 2003. [10] E. Uhlmann, K. Urban, Markt- und Trendstudie 2010 Laserstrahlschmelzen; ISBN 978-3-9814405-1-5; 2011 Fraunhofer IPK. [11] The Spyder Project Contributors. 2018. Spyder documentation. Available at: http://pythonhosted.org/spyder/. Accessed 22 February 2018. [12] J. Zhou, D. Wen, An improved K-means Clustering Algorithm, Xi’an, 27th-29th May, 2011, pp. 44-46.