Automated statistical evaluation of energy data in the automotive production

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52nd CIRP Conference on Manufacturing Systems 52nd CIRP Conference on Manufacturing Systems

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Automated statistical evaluation of energy data in the automotive Automated statistical evaluation ofMay energy data inFrance the automotive 28th CIRP Design Conference, 2018, Nantes, production productiond ,a b,c A new methodology to analyze the functional and physical Ingo Labbus* , Hanno Teiwes , Marc-André Filz , Christoph Herrmanndarchitecture , Mark Gonterc, of ,a b,c Rössingerb, Sebastian Markus ThiededHerrmann Ingo Labbus* , Hanno , Marc-André Filzd, Christoph , Mark Gonterc, existing products forTeiwes an assembly oriented product family didentification b d Markus Rössinger , Sebastian Thiede Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

Volkswagen Group Components, Information Management & Digitization, Postbox 011/8153, 38436 Wolfsburg, Germany b Volkswagen AG, construction body shop, Postbox 011/1326, 38436 Wolfsburg, Germany a Volkswagen Group Components, Information Management & Digitization, 011/8153, 38436 Wolfsburg, Germany c Volkswagen AG AutoUni, Postbox 011/1231, 38440Postbox Wolfsburg, Germany b Volkswagen AG, construction body shop, Postbox 011/1326, 38436 Wolfsburg, Germany Technology (IWF), d of Sustainable Manufacturing and Life Engineering, Institute Machine and Production ÉcoleChair Nationale Supérieure d’Arts et Métiers, ArtsCycle et Métiers ParisTech, LCFCofEA 4495, 4Tools Rue Augustin Fresnel, Metz 57078, France c Volkswagen AGBraunschweig, AutoUni, Postbox 011/1231, 38440 Wolfsburg, GermanyGermany Technische Universität Langer Kamp 19b, 38106 Braunschweig, d Chair of Sustainable Manufacturing and Life Cycle Engineering, Institute of Machine Tools and Production Technology (IWF), Correspondingauthor. author.Tel.: Tel.:+33 +49-5361-9E-mail address: [email protected] Technische Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany **Corresponding 3 87 37 54970983; 30;Universität E-mail address: [email protected] a

* Corresponding author. Tel.: +49-5361-9- 970983; E-mail address: [email protected]

Abstract Abstract Abstract the manufacturing industry, there a strong demand forproduct methods evaluating energy relatedisdata like energy profiles. The obtained energy InIntoday’s business environment, theistrend towards more variety and customization unbroken. Dueload to this development, the need of data can be used in commercial production system planning and simulation software solutions. However, due to missing automated evaluation agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production In the manufacturing industry, there is a strong demand for methods evaluating energy related data like energy load profiles. The obtained energy solutions, there is a lack of data for e.g. machine tools or utilities. Therefore, the industry tries to bridge the gap between software and measured systems asbewell as tocommercial choose theproduction optimal product matches, product analysis software methods solutions. are needed. Indeed, mosttoof the known methods aim to data candata. used systeman planning andenergy simulation missing evaluation energy The in aim of this article is to develop automated load profile analysis for However, productiondue equipment to automated provide consumption analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number solutions, there is a lack of data for e.g. machine tools or utilities. Therefore, the industry tries to bridge the gap between software and measured data for further uses, e.g. in the early factory planning phase. The main advantage of the presented method is the reduction of input data onlyand on energy data. aim of this article is tostate develop an automated energy load profile analysis for production equipment provide nature components. This fact impedes an depended efficient comparison and To choice of appropriate product family combinations for theconsumption production energyofdata toThe identify e.g. the machine energy demand. achieve this, statistical methods and clusteringtoalgorithms are applied. data approach forAfurther uses, e.g. in the early planning phase.products The main of the presented method is the reduction The of input on system. new is methodology isbyproposed to from analyze in advantage view of their functional and physical architecture. aim data is toonly cluster The exemplified a use factory case the existing automotive industry. energy data toin identify e.g. the machine depended energy To achieve this, statistical are applied. these products new assembly oriented state product families for thedemand. optimization of existing assemblymethods lines andand theclustering creation ofalgorithms future reconfigurable The approach is exemplified by a use caseChain, from the automotive industry. assembly systems. Based on Datum Flow structure the products is CC analyzed. Functional subassemblies are identified, and © 2019 The Authors. Published by Elsevier Ltd. the Thisphysical is an open accessofarticle under the BY-NC-ND license a(http://creativecommons.org/licenses/by-nc-nd/3.0/) performed. a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the ©functional 2019 Theanalysis Authors.isPublished by Moreover, Elsevier Ltd. © 2019 The Authors. Published by Elsevier Ltd. This is license ansupport open(http://creativecommons.org/licenses/by-nc-nd/3.0/) access article under thesystem CC BY-NC-ND license similarity between product families by scientific providing design both, production planners and product This is an open access article under the CC BY-NC-ND Peer-review under responsibility of the committee of the to 52nd CIRP Conference on Manufacturing Systems.designers. An illustrative (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility the scientific committee of the 52ndAn CIRP Conference on Manufacturing Systems. example of a nail-clipper is used toofexplain the proposed methodology. industrial case study on two product families of steering columns of Peer-review under responsibility of the scientific committee of the 52nd CIRP Conference on Manufacturing Systems. thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. Keywords: energy efficiency; automotive industry; statistical evaluation; data analysis © 2017 The Authors. Published by Elsevier B.V. Keywords: energy automotive statistical evaluation; Peer-review underefficiency; responsibility of theindustry; scientific committee of the data 28thanalysis CIRP Design Conference 2018. Keywords: Assembly; Design method; Family identification 1. Introduction

organizational improvements can be realized during nonproduction times. Critical elements within the companies are 1. Introduction organizational improvements can be realized during nonRising energy and resource prices have led to a paradigm the often not ideal organizational structures, behavioural production times. Critical elements within the companies are shift in the manufacturing industry since energy costs cannot barriers as not well ideal as missing energy related databases for their Rising energy and resource prices have led to a paradigm thetheoften organizational structures, behavioural 1. Introduction of product range and characteristics manufactured and/or be treated as overhead costs anymore [1]. Growing awareness production equipment, which are some of the reasons for so shift in the manufacturing industry since energy costs cannot barriers asinwell missing energy related forthe their assembled thisas system. In this context, thedatabases main challenge in of resource consumption and green products are a strong driver called energy efficiency gap [3]. Even though commercial beDue treatedtoas the overhead anymore [1]. awareness productionand equipment, arenot some of the for single the so fast costs development in Growing the domain of modelling analysiswhich is now only to reasons cope with for the manufacturing industry. As a consequence, planning tools are widely used in theEven industry, energy resource of resource consumption green trend products a strong driver called energy efficiency [3]. though commercial communication and an and ongoing of are digitization and products, a limited productgap range or existing product families, manufacturing companies try to reduce the energy demand planning is hardly possible without a corresponding energetic for the manufacturing consequence, planning are to widely used into thecompare industry, energy to resource digitalization, manufacturingindustry. enterprisesAs are afacing important but also totools be able analyze and products define already in the production process by implementing either state utility database. Therefore, methods athat autonomously obtain manufacturing companies try toenvironments: reduce the energy demand planning is hardly possible corresponding challenges in today’s market a continuing new product families. It can without be observed that classicalenergetic existing of the art technology or using energy efficient production information on the overall energetic performance from various already intowards the production process by implementing utility database. Therefore, methods that autonomously obtain tendency reduction of product developmenteither timesstate and product families are regrouped in function of clients or features. planning. However, the most effective way to foster energy sources without expert knowledge are necessary. Thisvarious paper of the artproduct technology or using energy there efficient information on the overall from shortened lifecycles. In addition, is an production increasing However, assembly orientedenergetic product performance families are hardly to find. efficiency is in the early planning phase in which constructional presents a new approach that focuses on automated energy data planning. the most way time to foster sources are necessary. Thisinpaper demand of However, customization, beingeffective at the same in a energy global On thewithout productexpert familyknowledge level, products differ mainly two and organizational changes are possible at low cost [2]. In the analysis and evaluation in the production process, which can be efficiency iswith in thecompetitors early planning constructional presents a new approach thatnumber focusesofoncomponents automated energy competition all phase over in thewhich world. This trend, main characteristics: (i) the and (ii)data the actual production process itself, holistic solutions are not used afterwards, e.g. as a valuable input in the planning phase and organizational are possiblefrom at low cost [2]. In the analysis and evaluation the production process,electronical). which can be which is inducing changes the development macro to micro type of components (e.g.inmechanical, electrical, possible any longer, and at that point only local technical or when it comes to e.g. the selection of machine actual production itself, solutions are not used afterwards, as a valuable inputmainly intools. the single planning phase markets, results in process diminished lot holistic sizes due to augmenting Classical methodologies considering products possiblevarieties any longer, and at that only local technical[1]. or when it comes to the selection machine tools. analyze the product (high-volume to point low-volume production) or solitary, already existing of product families 2212-8271 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license To cope with this augmenting variety as well as to be able to product structure on a physical level (components level) which (http://creativecommons.org/licenses/by-nc-nd/3.0/) 2212-8271 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license an efficient definition and identify possible optimization potentials in the existing causes regarding Peer-review under responsibility of the scientific committee of the 52nd CIRP Conference on difficulties Manufacturing Systems. (http://creativecommons.org/licenses/by-nc-nd/3.0/) production system, it is important to have a precise knowledge comparison of different product families. Addressing this Peer-review under responsibility of the scientific committee of the 52nd CIRP Conference on Manufacturing Systems.

2212-8271 © 2019 The Authors. Published by Elsevier Ltd. This is an©open article Published under theby CC BY-NC-ND 2212-8271 2017access The Authors. Elsevier B.V. license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of scientific the scientific committee theCIRP 52ndDesign CIRPConference Conference2018. on Manufacturing Systems. Peer-review under responsibility of the committee of the of 28th 10.1016/j.procir.2019.03.284

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2. State of research regarding energy efficient resource planning Fostering energy efficiency in order to optimize the production in the planning phase requires a thorough knowledge within the field of machine tools. Therefore, in order to consider the energy efficient use of resources during the planning process, energetic demands of all utilities, e.g. machine tools have to be implemented within the existing planning processes. The level of detail that needs to be addressed using commercial software varies from process to plant level in the corresponding planning stage. Material and resource flow simulation are commonly performed on plant level. On process level, the focus is on the specific manufacturing processes and how the spatial prerequisites meet the proposed layout. Furthermore, on the production equipment level, software is used especially for the simulation of industrial robot movements in a single workgroup or whether there are spatial conflicts when performing the production tasks. Bornschlegl et al. address traditional aspects like cycle times, availability and investment costs that are dominant during the planning process [4]. However, during the described planning process, different types of commercial planning software solutions are applied that depend on both traditional and energy related input [5–7]. Therefore, multiple approaches exist for the acquisition of energy related information. Zein et al. evaluate the energy efficiency of machine tools by introducing a minimum energy reference level, based on the process material removal rate. According to the measured level of energy efficiency, improvement measures are presented supporting the machine tool user towards optimization [8]. Gutowski et al. calculate an energetic benchmark by determining the exergy energy demand for a specific manufacturing operation [9]. In order to identify the most suitable machine tool for a given production task, Schudeleit et al. present four different energy efficiency test procedures using an analytic hierarchy process [10]. Devoldere et al. especially focus on the idle state since the idle energy consumption of machine tools has a substantial energy share of the yearly energy demand [11]. Schmitt et al. use functional machine states that relate to different subsystems. Using a supply controller, the machine states are selected automatically in relation to the machine tool user profile [12]. The obtained results can be stored in a database like that developed by Labbus et al. who present a modular framework that uses specialized planning tools in order to develop a database which helps the user to select the most appropriate machine tool [13]. Li et al. connect information on machine behavior as well as energy data [7]. However, the implementation of a full machine data acquisition is rather cost-intensive, especially for complex production equipment like machine tools. Therefore, other approaches use cluster analyses to interpret machine tools behavior [5]. Moreover, it is pointed out that multiple different information inputs are needed for a specific energy related information of the production as an output. However, the amount of information input should be as small as possible. Therefore, their methodology uses a k-means algorithm in order to identify the product embodied energy only with a machine schedule [5]. All described approaches present

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concepts for energy data processing and the use of derived information. The greatest challenge for the application of these approaches is the availability of data, since they need either detailed energy consumption data or detail machine behaviour information. Therefore, chapter 3 presents a methodology for an automated statistical energy data analysis and the related knowledge discovery in data. 3. Methodology for statistical energy data analysis 3.1. Selection of suitable energy data and analytical requirements As described in the beginning, the overall goal is to develop a methodology that is easy to use and needs as little information as possible. Since the automated acquisition of energy data is widely established in the automotive industry, it is not discussed here in detail. The objective of the developed method is an automatable and universally applicable generation of suitable energy reference information based on available energy data, to enable simplified machine benchmarks and integration into existing software systems for production system planning. Therefore, the first step is the assessment of suitable energy data. Today’s measurement technology enables the acquirement of energy related data in frequency ranges from 15 minutes up to 50 Hz. Consequently, a tradeoff has to be made since high frequency data is mostly redundant for the resulting costs. Empowering the developed methodology to be used in combination with different energy data acquisition systems, it is designed to cover both high and low frequency energy data. Fig. 1 introduces different types of load profiles for specific machine tools since the methodology has to be suitable for various machine types. On the one hand, Fig. 1 a depicts the power consumption of a grinding machine, acquired as 15 minutes average over a time period of three days. A typical behaviour with repetitive load levels representing typical machine states is displayed. In this case, four machine states are recognizable: first, a working state at the highest load level, second an operational state at a middle load level, third a standby state below the operational state and the off-state at the lowest load level. Since machine behaviour is not as clearly divisible, a standardization of typical machine states is needed to make energy consumption of different machines comparable. The presented method follows the VDMA 34179 standard separating machine behaviour into the four states in working, operational, standby and off. Fluctuations between these states are caused by state changes within the 15 minutes acquisition interval. On the other hand, b shows a rather simple and Fig. 1 c a more complex power consumption profile of two machine tools with a one second resolution. The cycle time of the machine is about 60 seconds in which the mechanical processing is visible as a peak load followed by a period of equal power consumption until the next part is processed. The processing is interrupted by minor operational phases. Working and operational phase are easily separable by different load levels. Still, the production cycle is visually recognizable compared to the operational phase, but the separation of the production cycle is more difficult than in Fig. 1 b.

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For simple structured load profiles as shown in Fig. 1 a. and b. with clear load levels, cluster analyses as discussed before they might be used for the determination of the machine states. The usage of a cluster analysis is not fully suitable, if the minimum working power is lower than the operational power due to recuperation effects. Sometimes they are hardly usable for complex load profiles (Fig. 1 c). A third possible solution is to use statistical analysis. For a large-scale production like in automotive component production, the occurrence of stable load levels can be used to determine machine states for energy data analyses, as well as a simplified machine monitoring. Pattern recognition concepts are also applicable, since most machines show repetitive load profiles. A drawback of this approach is an individual learning process for the machine tool to ensure a good quality of results.

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assignment of the machine state. Further machine data acquisition to record the operating states of a machine is not necessary. The second step is a data preprocessing, depending on the input data. Next, the statistical data analysis and the identification of machine states are carried out. The generated reference data are ultimately provisioned for further usage. 1. Energy data acquisition

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The necessary preprocessing is dependent on the acquisition frequency of the analyzed data. For low frequency data with an acquisition interval higher than the machine cycle time, only outliners are filtered. For high frequency energy data, a smoothing procedure is applied. To reduce short-term fluctuations within one production cycle, a simple moving average with a window length of nominal cycle time is calculated. Fig. 3 exemplifies the result. The short-term fluctuations within the load profile are smoothed. The drawback of this method lies in a resulting transition area for every change in the machine state, which needs to be filtered out for further proceeding of the data.

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Fig. 2. Procedure of the developed methodology.

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Fig. 3. Moving average on measured energy consumption data.

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Fig. 4 a exemplifies the concept of the developed method. Instead of load profiles as shown before, it plots the power consumption in a histogram. This means the x-axis depicts the power consumption, categorized into equidistant bins, while the y-axis represents the counts for each bin. For the pictured example, the consumption of a grinding machine acquired over a three-month period is analyzed. Within the plot, four significant peaks are visible. The first peak at about 1 kW represents the off-state of the machine. Since the power consumption is measured independent from the machine control system, the off-state is measured while the machine is switched off. The next peak at about 16 kW represents the standby state, the following by the operational state at 33 kW. The last peak between 50 kW and 55 kW is the energy consumption during the working state. Moreover, the histogram plot shows the basic idea to analyze the repetitive load levels of a machine tool. Although the histogram is a comprehensible visualization, the classification into discrete bins shows some drawbacks for further usage. Instead, the developed method uses a kernel density estimation (KDE), to determine a continuous density function, calculated according to [14]:

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Fig. 1. Comparison of load profiles for different machine tools.

As the state-of-the-art review showed, there is a lack of utilities that help to analyse energy demands in order to obtain state related energy demands for machine tools while being able to interpret different load profile and input data types. To realize an automated data analysis suitable for these demands, a statistical energy data analysis is presented in the following. 3.2. Methodology The basic idea is to use statistical evaluations to determine relevant reference data in order to obtain further information. As described above, machines used in large series production of identical or similar products, such as in automobile production, show recurring load profiles. These patterns can be used to determine the energy consumption of the individual machine states. Fig. 2 summarizes the procedure of the developed method. The first step is the acquisition of energy data. As stated before, the acquisition itself is not considered here. Prerequisite for the application of the methodology is an individual energy data acquisition to ensure the right

𝑓𝑓(𝑥𝑥) =

1

𝑛𝑛ℎ

∑𝑛𝑛𝑖𝑖=1 𝐾𝐾 (

𝑥𝑥−𝑥𝑥𝑖𝑖 ℎ

) , 𝑥𝑥 ∈ 𝑅𝑅

(1)

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Ingo Labbus et al. / Procedia CIRP 81 (2019) 1154–1159 Ingo Labbus et al. / Procedia CIRP 00 (2019) 000–000

4. Validation and application

using a Gausian-Kernel 1

√2𝜋𝜋

𝑒𝑒

(2)

, 𝑧𝑧 ∈ 𝑅𝑅.

Fig. 4 b presents the result of the KDE for the same data depicted in Fig. 4 a. The density function shows continuous progression with a clear visibility of the four machine states.

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Fig. 4. Result of histogram plot and kernel density estimation.

As mentioned in the beginning, a main motivation for the development of the statistical energy data analysis is the automation of the interpretation of acquired energy data. Due to that, also the assignment of machine states needs to be automated. Stating with the density function, the machine states can be assigned in three steps. The first step is the location of the relevant working state. As shown in Fig. 4, there is a clear maximum with a high power consumption. Nevertheless, the statistical analysis identifies the inflexion point with the highest power consumption as working state. Usually, production plants are not ideally utilized. Instead, micro disruptions interrupt the production processes. The maxima in the density function correspond to the working power with the highest share of time, including the influence of disruptions. The developed data analysis is supposed to assign the power consumption in the highest possible utilization, to reduce the influence of micro disruptions. At the same time, the influence of outliners and unusual high power consumption situations is supposed to be reduced. Due to this, the maximum power consumption is not suitable as well, since it is easily falsified by outliners. The inflexion point is the empirically best suitable characteristic to estimate the working power consumption (see section 4.1), since it represents a high utilization and is less influenced by disruptions or outliners. The off-state is characterized by the first maximum. The next two maxima characterize the standby and operational state whereas the operational state is expected at the higher power consumption. If only one peak is assignable, the analysis labels the state as operational since a standby state is not available for all machines. Anyhow, as the trustworthy detection of the right operational and standby state is the biggest challenge, chapter 4 will go deeper into typical patterns.

Fig. 5 presents typical density functions to discuss the plausibility of the results. On the upper left side, case a. shows a typical distribution with four machine states. However, this is not the only possible result. Case b. on the upper right side shows a plot with only three obvious machine states. Since not every machine has an unambiguous standby state, the middle peak is assumed as an operational state in these cases. Moreover, case c. depicts a machine with a bad utilization. The working peak is widely distributed due to frequent transitions between working and operational state during waiting time for the next part to process. Since the inflexion point is interpreted instead of the maximum, the analysis returns a working consumption representing an ideal utilization. a. Normal load pattern:

b. No standby state:

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For application, the presented methodology is prototypically realized in a Python© framework. To ensure the quality of the results, chapter 4.1. validates the results of the method by comparison with the real consumption of the machines. Chapter 4.2 presents an application example for the uses of the generated reference energy data, while chapter 4.3 gives an outlook about further potentials of the presented method. The results shown refer to the mechanical production of car engine components.

Density

𝐾𝐾(𝑧𝑧) =

−𝑧𝑧2 2

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Fig. 5. Comparison of typical density functions for machine tools.

In case d, a possible standby state at 2.5 kW is not recognized since its density is less than the chosen detection threshold. In these cases, the analysis tool is configured to miss a machine state, rather than assigning a false state. Since operational and standby state are ambiguous in many cases, e.g. caused by an unknown fault behaviour, the classification of these states is less reliable then off and working classifications. To evaluate the quality of the results, Table 1 compares the results of the statistical analysis for low frequency 15 minutes average values as well as in a one second interval acquired high frequent input data, compared to the real consumption of one grinding machine. In case of low frequent input data, the continuous measured energy consumption over a time period of six months was analyzed with the described statistical energy data analysis, in case of high frequent data over 14 days.

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The results shown in Table 1 underline that the method is applicable for the examined machine. To ensure the general applicability of the developed method, it is applied to 18 machines from one production line and compared to real consumption, as discussed before. Table 2 shows a summary of the results. In general, the statistical analysis delivers reliable results for the working consumption. In any case, the working state is assigned correctly, for 17 machines with a good quality, for one machine with an acceptable quality. The classification of the operational and standby state is more challenging in contrast. As discussed before, a non-classification is preferred to a false classification. In case of operational states, three machines are classified incorrectly, three are not classified. For standby, two were incorrectly classified, five not classified. The three respectively two incorrect classifications are mainly

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Good quality: Error less than 5% or 0.5 kW Acceptable quality: Error less than 10% or 1 kW Incorrectly classified: Error more than 10% and 1 kW or classification of a non-existent state Not classified: Missing classification of an existing state

Considering that the analysis was carried out without further consideration of the machine characteristics, the results show the potential of the statistical energy data analysis for the evaluation of energy consumption in large scale production. Many machines can be evaluated with minimum effort. 4.2. Application example for production planning As stated, production data can be useful for the planning of new production systems if it is sufficiently preprocessed. Fig. 6 depicts a comparison of the working power consumption for 211 machine tools in total, categorized by manufacturing process and machine type. Each machine type is visualized by a box plot. For most of the machine types, similar power consumption within a machine type can be seen. A major advantage of the use of working consumption instead of an average consumption is the independency of the machine utilization, which ensures comparability of the reference data. Legend: Outliner

60

Maximum

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0

Milling with machining center (6)

Comparison to actual consumption: Working: 54.2 kW, Operational 33.1 kW, Standby 15.7 kW, Off 1.2 kW

Incorrectly Not classified classified

Drilling with drilling machine (20)

0.1 1.4 -0.1 0 0

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Operational

Grinding with grinding machine (23)

54.3 52.8 33.0 15.7 1.2

Workin

Turning with turning machine (15)

Absolute error [kW]

1.7 2.1 -0.1 0.5 0

Good quality

Milling with milling machine (19)

Power [kW]

55.9 52.1 33.0 16.2 1.2

Quality of results: Acceptable quality

Broaching with turnbroaching mach. (16)

Absolute error [kW]

Inflexion Point Maximum Operational Standby Off Working

Low frequency input data

Power [kW]

High frequency input data

Table 2: Overview of analysis results for 18 machines

Milling with end working machine (9)

Table 1. Results of the statistical energy data analysis for a single machine, comparison of high frequent (1s interval) and low frequent (15 min interval) acquired energy data.

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explained by high time shares of machine failure and heavily fluctuating machine utilization. Especially the influence of machine failure is critical. Since the failure energy consumption is dependent on the individual machine behaviour and failure type, the statistical energy data analysis it not able to completely exclude its influence. In contrast, the off-state is more definite and classified with a good quality in 16 cases.

Cutting with machining center (103)

For the determination of actual consumption, the energy consumption of the machine is analyzed manually in detail under consideration of detailed machine data, in order to ensure the correct allocation of machine state and energy consumption. For every state, at least five time intervals of at least ten minutes without a change in machine state are taken into calculation. The working consumption is determined during regular production. To reduce the influence of micro disturbances, time intervals with a maximum utilization are evaluated. The working power consumption of the evaluated machine in Table 1 is 54.2 kW. In comparison, the results of the statistical analysis for high frequency as well as low frequency data acquisition are given. The working consumption results are separated into the results for the evaluation of the inflexion point, as well as the maximum. Both results for the evaluation of the inflexion point are above while the results of the maximum are below real consumption. This tendency is generally observed. The maximum can be regarded as an average utilization of a machine. Since it is usually not fully utilized during normal production, the average consumption is reduced due to minor stops. In contrast, the inflexion point represents the maximal utilization as it occurs in normal production. Since the statistical analysis aims at evaluating the maximum consumption, the inflexion point is chosen to determine the working consumption. In Table 1, the results for the analysis of low frequency energy data shows an absolute error of 0.1 kW, the high frequency data of 1.7 kW, compared to real consumption. The other machine states are correctly classified with a maximum error of 0.5 kW.

Working Power [kW]

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Minimum

Machine Type

Fig. 6. Working power consumption of different machine types (number of machines per categories in brackets).

As Schmidt et al. describe, the provision of these reference data enables the consideration of energy consumption in early phases of production system planning with little effort [15]. Through the provision of data for the relevant machine states,

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aspects like the idle energy consumption might be simulated within the planning process. The aggregation of acquired time series data to reference data also simplifies the integration of production data into existing planning data models, as Labbus has shown [13]. Beside usage of the generated reference data for planning new productions, it also supports efficiency increase in running production. For instance for identification of machines with low utilization, by comparison of working and average consumptions, the derivation of shutdown strategies or the identification of maintenance demand based on changes of the working power. 4.3. Further application potentials Beside the considered aspect of energy efficiency, the statistical energy data analysis also has potential for further analyses of the production process. Fig. 7 compares two histograms for two different production shifts of one machine. Fig. 7 a. depicts a highly utilized production shift. The machine only shows few counts in non-productive states with a power consumption up to 30 kW. Most of the time, the machine is in productive working mode. This is illustrated by the significant accumulation between 30 kW and 35 kW. In comparison, Fig. 7 b. shows the histogram for an underutilized shift. It shows significant higher counts in the non-productive area. Since the counts of the histogram correspond to the time share of the machine states, this behaviour can be used to determine the utilization of the machine. For visualization purposes, both Figures contain a cumulative share. In Fig. 7 a., the cumulative share indicates a share of about 0.15 below 30 kW, Fig. 7 b. about 0.8. This distribution indicates the utilization of the machine and describes the produced quantity. These evaluations are inevitably less exact than a comprehensive machine data acquisition, but it shows the unused potential of energy data. Since energy data acquisition is significantly less expensive than a full machine data acquisition it provides a cost-effective alternative for a simplified production data acquisition.

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Fig. 7. Comparison of power consumption histograms for one machine, a.: high utilized shift, b.: low utilized shift.

5. Conclusion & Outlook The presented methodology for a statistical energy data analysis presents a solution for an automated evaluation of

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energy data in the automotive production. The proposed procedure uses statistical methods to determine typical machine states, dependent only on the availability of energy consumption data. The biggest advantage of this procedure is the low input data requirement and the simple automation capability. As shown in this paper, it can be used to analyze the energy consumption for a large scale production with little effort. The consideration of the further application potentials demonstrates that energy data acquisition can even be used for simple production control use cases. This underlines the potential of energy data from running production for further analysis and usage to identify savings potential. References [1] Kara S, Bogdanski G, Li W. Electricity Metering and Monitoring in Manufacturing Systems. In: Hesselbach J and Herrmann C, editors. Glocalized Solutions for Sustainability in Manufacturing:Berlin, Heidelberg: Springer Berlin Heidelberg; 2011. p. 1-10. [2] Azevedo A. Advances in Sustainable and Competitive Manufacturing Systems: Springer International Publishing. Heidelberg 2013. [3] Hrovatin N, Dolšak N, Zorić J. Factors impacting investments in energy efficiency and clean technologies. Journal of Cleaner Production 2016;127:475-486. [4] Bornschlegl M, Bregulla M, Mantwill F, Franke J, Müller A. Lebenszyklusbetrachtungen im Planungsprozess. wt Werkstatttechnik online 2016;106:89-93. [5] Teiwes H, Blume S, Herrmann C, Rössinger M, Thiede S. Energy Load Profile Analysis on Machine Level. Procedia CIRP 2018;69:271-276. [6] Kellens K, Dewulf W, Overcash M, Hauschild M, Duflou J. Methodology for systematic analysis and improvement of manufacturing unit process life-cycle inventory (UPLCI)—CO2PE! The International Journal of Life Cycle Assessment 2012;17:69-78. [7] Li W, Alvandi S, Kara S, Thiede S, Herrmann C. Sustainability Cockpit. CIRP Annals 2016;65:5-8. [8] Zein A. Transition Towards Energy Efficient Machine Tools: Springer Berlin Heidelberg. Berlin, Heidelberg 2012. [9] Gutowski T, Dahmus J, Thiriez A. Electrical Energy Requirements for Manufacturing Processes. Energy 2006;2. [10] Schudeleit T, Züst S, Wegener K. Methods for evaluation of energy efficiency of machine tools. Energy 2015;93:1964-1970. [11] Devoldere T, Dewulf W, Deprez W, Willems B, Duflou J. Improvement Potential for Energy Consumption in Discrete Part Production Machines. In: Takata S and Umeda Y, editors. Advances in Life Cycle Engineering for Sustainable Manufacturing Businesses:London: Springer London; 2007. p. 311-316. [12] Schmitt R, Bittencourt J, Bonefeld R. Modelling Machine Tools for SelfOptimisation of Energy Consumption. In: Hesselbach J and Herrmann C, editors. Glocalized Solutions for Sustainability in Manufacturing:Berlin, Heidelberg: Springer Berlin Heidelberg; 2011. p. 253-257. [13] Labbus I, Schmidt C, Dér A, Herrmann C, Thiede S. Automated production data integration for energy-oriented process chain design. Procedia CIRP 2018;72:551-556. [14] Steland A. Basiswissen Statistik. 4., überarb. Aufl. 2016. Berlin, Heidelberg: Springer Berlin Heidelberg; 2016. [15] Schmidt C, Labbus I, Herrmann C, Thiede S. Framework of a Modular Tool Box for the Design of Process Chains in Automotive Component Manufacturing. Procedia CIRP 2017;63:739-744.