Energy Consumption Estimation for Machining ... - Science Direct

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ScienceDirect Procedia CIRP 57 (2016) 637 – 642

49th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2016

Energy consumption estimation for machining processes based on real-time shop floor monitoring via wireless sensor networks. Dimitris Mourtzis*, Ekaterini Vlachou, Nikolaos Milas, George Dimitrakopoulos Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26500, Rio Patras, Greece *Corresponding author. Tel.: +30-261-0 99-7262; fax: +30-261-0 99-7744. E-mail address: [email protected]

Abstract The increasing concern about the depletion of the energy repositories places the energy efficiency issues in high priority. In the manufacturing sector, the improvement of energy efficiency is a challenging task due to the complexity of manufacturing systems and the requirements for flexible operation targeting highly customised products. Towards this end, the estimation of the energy consumption of a machining task, and therefore the machining cost, is necessary. This paper presents a machine tool monitoring methodology that integrates sensory systems, a scheduling module, and human operators to perform real-time monitoring on the shop-floor. A monitoring system is designed to capture realtime measurements from sensors attached on machine tools and perform the necessary pre-processing to transmit these measurements to a Cloud server via wireless sensor networks. Furthermore, the input from human operators is utilized to collect the machining parameters. The collected information is fused through an information fusion mechanism to extract meaningful results. The results are stored in a database for the reuse in future tasks by estimating the energy consumption of new cases, through a case-based reasoning approach, prior the job dispatching. Therefore, the machining parameters of the new case can be modified targeting energy consumption reduction. The proposed system is delivered as a Cloud software-as-a-service to realise the philosophy of Cloud manufacturing. © Published by Elsevier B.V. This ©2016 2015The TheAuthors. Authors. Published by Elsevier B.V.is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Scientific committee of the 49th CIRP Conference on Manufacturing Systems (CIRP-CMS 2016). Peer-review under responsibility of the scientific committee of the 49th CIRP Conference on Manufacturing Systems Keywords: Wireless Sensor Network, Monitoring; Cloud manufacturing; CBR

1. Introduction The depletion of the energy resources and the degradation of the environment place the issue of energy efficiency in high priority. Manufacturing is a sector with a large share in the energy consumption field with a percentage about 28% of the primary energy use, and about 38% of the CO2 emissions globally [1]. Except for the environmental footprint of manufacturing systems that needs to be controlled, the energy consumption corresponds to a portion of the cost of a manufacturing process and directly affects the cost of products. Therefore, techniques that provide awareness and reduction of the machine tool energy consumption are vital. The issue of the energy efficiency in manufacturing has been investigated since 1980 with the work of [2] to conclude that the energy capabilities of machine tools are scarcely exploited. The emergence of cost-efficient sensory systems enables the direct monitoring of the power requirements of machine tools

and therefore provides useful input to decision makers in order to improve their practices [3]. Furthermore, philosophies such as Cloud Manufacturing, along with ICT systems provide opportunities in terms of ubiquitous access to information and knowledge sharing. Therefore, smart monitoring systems can provide real-time awareness of the energy consumption and therefore contribute to the increase of the energy efficiency. The emerging of new technologies in computing has opened the path to the digitisation of manufacturing and facilitated the knowledge management. Modern IT tools can store the documentation of the manufactured products in digital form. Therefore, the paperwork is reduced and the knowledge can be easily reused in future cases. A major challenge in this digitisation is the synchronisation between the physical and the cyber world. To address this challenge, monitoring devices can be applied to raise information from the shop-floor to the higher levels of manufacturing enterprises and hence support the decision making.

2212-8271 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 49th CIRP Conference on Manufacturing Systems doi:10.1016/j.procir.2016.11.110

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Dimitris Mourtzis et al. / Procedia CIRP 57 (2016) 637 – 642

Aiming to meet these challenges, this paper combines a monitoring framework with a knowledge retrieval tool to gain insights on the energy requirements of machine tools in the milling process. 2. State of the Art On the subject of energy consumption estimation, Dahmus [4] performs a high level recording of the energy consumption in machining processes to identify the contribution of the machine tool subsystems. The rest of the scientific work on this subject can be classified in the categories of theoretical modelling, the generalisation of experimental data, and the use of real-time monitoring. Avram and Xirouchakis propose in [5] an analytical model for the estimation of the variable mechanical energy requirements of a machine tool system with experimental verification. This approach takes into account both steady-state and transient conditions. The exploitation of experimental results to conclude on a model for the estimation of energy consumption based on the material removal rate (MRR) has been performed in [6]. In a higher level, a framework was proposed by [7], based on multi criteria decision making methods to incorporate energy consumption and environmental impact considerations. Therefore, the assignment scheme results to the reduction of the energy consumption of the manufacturing plant. The effect of the cutting parameters modification on the power consumption of machine tools is also investigated by [8]. Furthermore, the authors leveraged their capability to modify the control method of the spindle and the axes to reduce the energy consumption by synchronizing the spindle acceleration with the feed system. The necessity for the use of real-time monitoring in manufacturing has been stressed in the work of [9]. The authors identified that the main requirements for monitoring systems in production are the robustness, the capability for reconfiguration, the reliability, the intelligence, and the cost efficiency. Therefore, the fluctuating demands for flexibility in production require smart monitoring systems to raise information from the shop-floor. In the monitoring systems, various sensors can be employed (e.g. force, acceleration) but current sensors can provide useful information without interrupting the monitored subject [9]. Moreover, various topologies for the communication of monitoring devices can be employed. In manufacturing the wireless sensor network topologies are the most eligible candidates as they offer flexibility and scalability, especially in flexible environments such as the shop-floors [11]. The use of monitoring devices can transform the shop-floors into cyber-physical systems [12]. This transformation can provide awareness on the status of the resources and increase on their utilisation [13][14]. The extraction of meaningful information from multiple data sources requires the use of information fusion techniques [15]. The information fusion can be performed in three levels i.e. sensor level, feature level, and decision level [16]. In the decision level the Dempster-Shafer theory is mostly used, and can be paired with decision support algorithms to extract the weights for each source of information [17].

Manufacturing systems require intelligence in collaboration, and adaptability to dynamic changes. Towards this end, the philosophy of Cloud manufacturing can act as enabler for data exchange between IT tools and ubiquitous access by multiple users and IT tools to information [18]. Another recent study performed an extensive literature review in Cloud Manufacturing presenting the key benefits brought by the adoption of Cloud technology in manufacturing, such as scalability to business size and needs, and ubiquitous network access [19]. The Case-Based Reasoning (CBR) process is a problem solving technique that relies on the reuse of past experience. The main benefit of CBR is that it can be used as a similarity measurement among different cases [20]. The CBR method is utilised in this research work due to its suitability for complex and difficult to model systems, such as machine tools, and because case generalization is required. This technique has been successfully applied in various domains such as design, decision making, planning, diagnosis, medical applications, law, e-learning, knowledge management, image processing or recommender systems, etc. [21]. To enhance the information that refers to a machining part with the requirements of energy consumption, this scientific work proposes a methodology for the energy consumption estimation in machining operations, leveraging the information and knowledge that lies in historical data. This methodology is integrated in a Cloud-based machine monitoring framework which has as main inputs real-time measurements from sensors on machine-tools, and information from the human operator, the process plan, and the scheduling module of the production planning system. The knowledge retrieval is performed through the CBR methodology, along with a similarity mechanism. 3. Methodology The methodology that is followed in this scientific work combines a real-time monitoring framework with the CBR to estimate the energy consumption in a machining task (Fig. 1). The choice of using a cased-based approach to estimate the power consumption is mainly driven by the fact that the machine tool capabilities are scarcely exploited in real life machining operations [2] supported by the fact that the machining power consumption accounts for less than 60% of the overall under full load [4]. These two facts allow the estimation of the power consumption in a more abstract level without constructing a detailed mathematical model that is bounded with specific machine tools. 3.1. Monitoring framework The core of the monitoring system is a dedicated data acquisition (DAQ) device for the monitoring of machine tools. The DAQ employs inductive current sensors to monitor the currents of the main motor drives of a machine tool i.e. the spindle and the moving axes, along with the overall power

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Machine tools Spindle current Spindle RPM Axes X,Y,Z

New order

New order parameters CBR

Axes C,B Power consumption Human operators Process plan Engineers

CLOUD platform

Similarity results

Cloud gateway

Tool magazines

Data processing (Information fusion)

Running task

Database Visualisation of results & KPIs

Cutting parameters - Raw material Machine schedule Task start time and duration Physical Shopfloor

Cyber-Physical Shop-floor

Fig. 1 Overview of the combined methodology consisted of the shop-floor monitoring and the CBR.

consumption. In the general occasion of a balanced three-phase load, the apparent power consumption is calculated via Eq. 1. ܵ ൌ ξ͵ ή ܸ௟ ή ‫ܫ‬௟

Eq. 1

Where, Vl is the grid line voltage and is considered constant and the Il is the average line current of the three phases as measured in real-time by the monitoring system. The active power can be calculated from the apparent power with multiplication with the power factor that was measured offline and is assigned the value 0.40. The value of the power factor is justified by the fact that the machine tools are composed of induction motors that operate under low loads that lead to power factor values far below 1. Furthermore, the dominant presence of three-phase induction motors makes the older machine tools almost balanced loads. This fact applies also in the cases of modern machine tools which use power electronic topologies to balance the load among the three phases. Each machine tool has a DAQ installed in the electrical cabinet which collects the measurements and performs the necessary pre-processing of the sensor outputs. The DAQs of a shop-floor are organised in a wireless sensor network (WSN) following the star topology. The selection of the WSN was driven by the requirements for flexibility and reduced infrastructure. The data transmission is coordinated by a central gateway which is responsible to collect the data from the DAQs and organise them into packets before transmitting them to a Cloud server for further processing and visualisation Fig. 2. Except for the DAQs, the proposed monitoring architecture integrates the human operators and the machine tool schedule. The machine tool schedule is provided by the scheduling module of a planning system and includes the task sequence, along with the cutting parameters of the process plan and the used cutting tools. The human operator informs the system about the task execution and the mean feedrate that is used during machining.

To synchronise the cyber with the physical world, an information fusion mechanism is employed. This information fusion extends the methodology that is presented in [15] by adding a second level of fusion that fuses the information from the DAQ, the human operator, and the schedule. In this level, the analytical hierarchy process generates the weights for the Dempster-Shafer theory of evidence, in order to identify the actual status of the machine tool.

Fig. 2 Sensor data capturing and transmission to Cloud.

3.2. CBR Methodology Aiming to correlate the energy consumption with the parts being produced, the monitoring system presented in the previous section, stores in the database the energy requirements of each machining task. Therefore, the engineers can gain useful insights on the energy requirements of new cases, along

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with the opportunity to reuse process parameters of previews cases. This procedure is facilitated by the CBR methodology and the similarity mechanism. Case Based Reasoning (CBR) focuses on solving problems by adapting acceptable solutions and comparing differences and similarities, between previous as well as current products (Fig. 3). Shop-floor Engineer

New order Received

New case (part) presented as a new task

Retrieval Past Cases

Similarity Engine Return matched cases

Revise similar case

Knowledge Repository

4. Development of the proposed framework Modern manufacturing systems under the umbrella of Cloud utilise a new business model capable of managing the growth in the amount of the collected data, incorporating Internet of Things, and mobile computing. Following these philosophies, the proposed framework is implemented as a Cloud-service that consists of hardware and software components.

CBR

Adapt case

strategy that was followed in the past. The output of this mechanism is the process plan information and the average power consumption of the most similar cases. Therefore, the engineer can reuse information from past process plans and also estimate the energy consumption of the new part using the past case average power consumption and the machining time that is calculated in the CAM tool. An important benefit of the CBR is that knowledge can be retrieved for different goals by selecting different features that are stored in a database and the appropriate weights. For example, the process plan engineer can use the CBR with other features to get results on similarity according to geometrical features.

Retain Case

4.1. Hardware development Fig. 3 The CBR methodology.

The CBR takes as inputs information from the process plan that is related to a new part and include the raw material, the cutting parameters, and the use of lubrication, along with the machine tool and cutting tool parameters. The weight assignment has been according to the reflection of each feature on the overall power consumption as it was indicated in experiments in various situations. A detailed presentation of the selected features and the corresponding weights can be found in Table 1. Table 1: The features and the corresponding weights that are used in the CBR

Feature Machine specifications Spindle power Overall power Process plan parameters Depth of cut (mm) Spindle rpm utilisation Feedrate (mm/min) Material (mm) Use of lubricant Tool parameters Teeth number Width of cut Tool material Sum of weights

Type Num Num Num Num Num Alpha Alpha Num Num Alpha

Weight 40% 20% 20% 55% 5% 20% 10% 10% 10% 5% 1% 1% 3% 100%

After performing pairwise comparisons with the historical data of past cases, a similarity mechanism returns the most similar cases. Hence, the process planning engineer is aware on the energy consumption of the new part and the machining

For the monitoring of machine tools, a DAQ has been designed and developed. The DAQ has at its core the microcontroller unit (MCU) ATmega 2560, that has embedded the necessary peripherals for sensor signal capturing. The selected current sensors are split-core inductive clamps and can be attached in the electrical cabinet without interrupting the wiring. The outputs of the sensors are connected to a sensor board that is a part of the DAQ and performs signal filtering and adjustment prior to the sampling by the microcontroller. The microcontroller pre-processes the current signals to calculate the root mean square (RMS) values. The WSN is facilitated with the use of DIGI XBee ZigBee RF module. The selection of ZigBee over other wireless standards is performed due to its support to various network topologies and encryption algorithms, and its robust operation with functionalities such as collision avoidance, retries, and acknowledgements performed in the hardware. A DIGI XBee ZigBee RF module is also installed on the microcomputer that is responsible for the coordination of the data transmission in the WSN. The microcomputer collects the measurements from the DAQs in the shop-floor, organises them into packets and transmits them to the Cloud server by using Hyper Text Transfer Protocol (HTTP) requests. 4.2. Software development The proposed framework is implemented as a Web Application developed on top of a Cloud Service. This application is confronted to Representational State Transfer (REST) architectural pattern which is based on simple HTTP. The cloud-based platform is deployed on an Infrastructure as a Service (IaaS) virtual machine running a Linux based operating system and includes an Apache HTTP server, a Ruby on Rails framework, and a MongoDB database.

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In addition, graphical user interfaces (GUIS) are developed for data entry and visualization of the results. The human operator of machine tools utilises a mobile device to provide information to the system and gain access to the results. Furthermore, the process plan engineer provides the information that are relevant to the process plan in a web application that exploits also information on the machine tool schedule. The schedule is gained in Extensible Markup Language (XML) format from the scheduling module of a legacy planning system. The issues of security are regarded in three main layers, the shop floor layer, the web application layer, and the Cloud service operating system layer. The selected counter measurements against threats are the encryption on data transfers, the identification of clients through Secure Sockets Layer/Transport Layer Security (SSL/TLS) protocol used in parallel with a secure database authentication system, and Virtual Private Network technology.

meaningful results, two different materials, two different cutting tools, as well as three feedrate values are used. This selection determines the values of the rest of the cutting parameters and the use of lubricant. The details of the experiments are presented in Table 3.

5. Case study and Results

Fig. 4 Power profile during machining

To validate the proposed methodology, four parts with similar geometry and features, but with different material and process plan parameters are manufactured by the same machine tool. The machine tool that is the subject of this study is a XYZ SMX SLV 3-axis machine tool and its specifications are shown in Table 2.

Table 3: The set of experiments and the results

Table 2 Case study machine tool specifications

XYZ SMX SLV Turret Mill Spindle drive motor Spindle max velocity Longitudinal travel - X axis Cross travel - Y axis Knee vertical travel - Z axis

3.75 kW 3600 rpm 1000 mm 410 mm 400 mm

Overall power consumption

10 kW

The monitoring system captures the power consumption of the machine tool throughout the machining process. In Fig. 4 measurements from a part of the process are presented. The first event that corresponds to a very high peak in the apparent power is the acceleration of the spindle. Following the spindle peak, the power consumption rises a little when the positioning of the Z-axis is performed. During the material removal process, a rise in the overall power consumption can be observed. Finally, a peak, but significantly lower than the spindle’s, can be observed when positioning of the axes is performed in rapid feed. From the graph, it can be concluded that even though in the selected machine tool the peripherals account for a little portion of the overall power consumption, the portion that is related to the actual machining process is very small. The largest portion of the energy consumption in this machine tool is related to the spindle movement. The efficiency of the approach in terms of selecting the features and the corresponding weights for the CBR is evaluated by manufacturing four parts in a milling process with a single cutting tool per process. Furthermore, to reduce the required set of experiments while managing to extract

Apparent Power (VA)

Power requirements during machining 10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0

Spindle acceleration

Z positioning Machining Rapid feed

No of Samples

Feature Machine specifications Spindle power (kW) Overall power (kW) Process plan parameters Depth of cut (mm) Spindle rpm utilisation Feedrate (mm/min) Material Use of lubricant Tool parameters Teeth number Width of cut Tool material Similarity with new order Estimated average power consumption Actual average power consumption

Case1

Case2

Case3

Case4

3.75

3.75

3.75

New case 3.75

10

10

10

10

2 0.439

1 0.447

1 0.414

1 0.447

100

200

100

300

Alum. No

Alum. Yes

Steel Yes

Alum. Yes

2 10 S.Carb.

4 10 HSS

2 10 S.Carb.

4 10 HSS

85.73%

96.67%

86.56%

-

-

-

-

1555

1395 VA

1502 VA

1465 VA

1562 VA

The outcome of the similarity mechanism is that the new case is most similar with case two by a percentage of 96.67%. The final phase of the CBR is the adoption of the similarity degree to further estimate the power consumption of the new case. The 96.67% indicates a power consumption of 1555VA for the new case. This value can be verified by the results of the

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experiments and also by the Figure 4 which demonstrates the power consumption profile of the new case. 6. Conclusions This paper proposes an approach for machine tools energy consumption estimation based on real-time monitoring measurements and on a case-based reasoning approach. Aiming to correlate the energy consumption with the parts being produced, the monitoring system stores in the database the energy requirements of each machining task and the engineers are capable of gaining useful insights on the energy requirements of new cases, along with the opportunity to reuse process parameters of previews cases. The process planning engineer is aware of the energy consumption of the new part and on the machining strategy that was followed in the past. Among the main advantages of this approach is the reuse of the past data, and the gaining of useful insights on power consumption of the new parts through the real-time measurements of the monitoring system. In a future study, a further validation of the proposed system, focusing on the extension of functionalities related to more complex products as well as various cutting tools, will be performed. Acknowledgements The work presented in this paper is partially supported by the EU funded research project “Collaborative and Adaptive Process Planning for Sustainable Manufacturing Environments − CAPP4SMEs” (314024). References [1] Fysikopoulos A, Pastras G, Vlachou A, Chryssolouris G, An Approach to Increase Energy Efficiency Using Shutdown and Standby Machine Modes, IFIP Advances in Information and Communication Technology, 2014;439: 205-212. [2] De Filippi A, Ippolito R, Micheletti GF, NC Machine Tools as Electric Energy Users, CIRP Annals - Manufacturing Technology, 1981;30:323-326. [3] Chryssolouris G, Manufacturing Systems: Theory and Practice. 2nd New York: Springer-Verlag, 2006. [4] Dahmus J, Gutowski T, An Environmental Analysis of Machining, Int. Mech. Eng. Congr. RD&D Expo, 2004: 1–10. [5] Avram OI, Xirouchakis P, Evaluating the use phase energy requirements of a machine tool system, J. Clean. Prod., 2011;19: 699–711. [6] Fasoli T, Terzi S, Jantunen E, Kortelainen J, Sääski J, Salonen T, Glocalized Solutions for Sustainability in Manufacturing, Cycle, 2011; 25: 454–458. [7] Yan H, Fei H, Methods for Integrating Energy Consumption and Environmental Impact Considerations into the Production Operation of Machining Processes, Chinese J. Mech. Eng., 2010;23/06: 742. [8] Mori M, Fujishima M, Inamasu Y, Oda Y, A study on energy efficiency improvement for machine tools, CIRP Ann. - Manuf. Technol, 2011; 60: 145–148. [9] Teti R, Jemielniak K, O’Donnell G, Dornfeld D, Advanced monitoring of machining operations, CIRP Annals – Manufacturing Technology, 2010;59/2:717-739. [10] Mourtzis D, Doukas M, Vlachou A, Xanthopoulos N, Machine availability monitoring for adaptive holistic scheduling:

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