Modeling the impact of cutting fluid strategies on environmentally conscious machining systems

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Procedia CIRP 00 (2017) 000–000

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Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

28th CIRP Design Conference, May 2018, Nantes, France

A new methodology to analyze the functional and physical architecture of

existing products for an assembly oriented product family identification

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France

* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: paul.stief@ensam.eu

Abstract

In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

Keywords: Assembly; Design method; Family identification

1. Introduction

Due to the fast development in the domain of communication and an ongoing trend of digitization and digitalization, manufacturing enterprises are facing important challenges in today’s market environments: a continuing tendency towards reduction of product development times and shortened product lifecycles. In addition, there is an increasing demand of customization, being at the same time in a global competition with competitors all over the world. This trend, which is inducing the development from macro to micro markets, results in diminished lot sizes due to augmenting product varieties (high-volume to low-volume production) [1]. To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing production system, it is important to have a precise knowledge

of the product range and characteristics manufactured and/or assembled in this system. In this context, the main challenge in modelling and analysis is now not only to cope with single products, a limited product range or existing product families, but also to be able to analyze and to compare products to define new product families. It can be observed that classical existing product families are regrouped in function of clients or features. However, assembly oriented product families are hardly to find.

On the product family level, products differ mainly in two main characteristics: (i) the number of components and (ii) the type of components (e.g. mechanical, electrical, electronical).

Classical methodologies considering mainly single products or solitary, already existing product families analyze the product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this

Procedia CIRP 80 (2019) 150–155

Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference. 10.1016/j.procir.2019.01.068

Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference.

ScienceDirect

Procedia CIRP 00 (2018) 000–000

(http://creativecommons.org/licenses/by-nc-nd/3.0/).

Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference. doi:10.1016/j.procir.2017.04.009

26th CIRP Life Cycle Engineering (LCE) Conference

Modeling the impact of cutting fluid strategies on environmentally

conscious machining systems

Nadine Madanchi

a

_{*, Sebastian Thiede}

a

_{, Timothy Gutowski}

b

_{, Christoph Herrmann}

a

a_{Institute of Machine Tools and Production Technology (IWF), Sustainable Manufacturing & Life Cycle Engineering Research Group, Technische Universität}

Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany

b_{Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge MA 02139, USA}

* Corresponding author. Tel.: +49-531-391-7639; fax: +49-531-391-5842. E-mail address: n.madanchi@tu-bs.de

Abstract

The application of cutting fluids for machining processes is a common practice in industry with the aim to improve productivity through increased cooling and lubricating performance. The application, however, also requires energy and resources for e.g. cutting fluid supply or chip treatment. Alternatively, the strategy of dry machining does not require cutting fluids and therefore claims to be more beneficial in terms of costs and environmental impact than wet machining. In order to assess the actual performance, it is important to comprehensively consider and analyze all possible impacts of alternative strategies on the elements of a machining system. This paper proposes a concept, which integrates the modeling of relevant influences depending on the strategy. The general applicability of the concept is shown within a case study, where the modeling results are compared with experimental results for a turning process and evaluated for different scenarios.

Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference. Keywords: Modeling; Cutting Fluid; Dry Machining; Machining System

1. Introduction

Machine tools are commonly used in the manufacturing industry to perform metalworking processes such as turning, milling or grinding. In 2012, 3.5 million metalworking machine tools were operated in Europe (27 countries) [1]. However, the use of metalworking machining tools is connected with a significant energy demand in the range of 410 PJ of primary energy per year [1]. According to previous studies, which analyzed the energy demand of machine tools during the use phase, especially supplementary and auxiliary components are the main consumers [2],3,4]. Bode [3] indicated that more than 50% of the total energy consumption is generated by the supply and preparation of coolants, while an alternative case study conducted by Triebs [4] presented an energy demand of around 26% related to cutting fluids.

Although cutting fluids ensure both cooling and lubrication of the machining process, there are also alternative strategies such as minimal quantity lubrication (MQL) or dry machining. In comparison, dry machining does not require any fluid preparation or supply pumps, but has a negative effect on e.g. tool wear and removal of chips [5]. Therefore, an integrated view is required to avoid problem shifting. Thus, this study focuses on the modeling of energy and resource flows to conduct an integrated evaluation of the environmental and economic impact.

2. Research Background

2.1. Cutting fluids

Cutting fluids are used for machining processes to fulfil the primary tasks of cooling and lubrication [6]. As most of the

Available online at www.sciencedirect.com

ScienceDirect

Procedia CIRP 00 (2018) 000–000

(http://creativecommons.org/licenses/by-nc-nd/3.0/).

Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference. doi:10.1016/j.procir.2017.04.009

26th CIRP Life Cycle Engineering (LCE) Conference

Modeling the impact of cutting fluid strategies on environmentally

conscious machining systems

Nadine Madanchi

a

_{*, Sebastian Thiede}

a

_{, Timothy Gutowski}

b

_{, Christoph Herrmann}

a

a_{Institute of Machine Tools and Production Technology (IWF), Sustainable Manufacturing & Life Cycle Engineering Research Group, Technische Universität}

Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany

b_{Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge MA 02139, USA}

* Corresponding author. Tel.: +49-531-391-7639; fax: +49-531-391-5842. E-mail address: n.madanchi@tu-bs.de

Abstract

The application of cutting fluids for machining processes is a common practice in industry with the aim to improve productivity through increased cooling and lubricating performance. The application, however, also requires energy and resources for e.g. cutting fluid supply or chip treatment. Alternatively, the strategy of dry machining does not require cutting fluids and therefore claims to be more beneficial in terms of costs and environmental impact than wet machining. In order to assess the actual performance, it is important to comprehensively consider and analyze all possible impacts of alternative strategies on the elements of a machining system. This paper proposes a concept, which integrates the modeling of relevant influences depending on the strategy. The general applicability of the concept is shown within a case study, where the modeling results are compared with experimental results for a turning process and evaluated for different scenarios.

Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference. Keywords: Modeling; Cutting Fluid; Dry Machining; Machining System

1. Introduction

Machine tools are commonly used in the manufacturing industry to perform metalworking processes such as turning, milling or grinding. In 2012, 3.5 million metalworking machine tools were operated in Europe (27 countries) [1]. However, the use of metalworking machining tools is connected with a significant energy demand in the range of 410 PJ of primary energy per year [1]. According to previous studies, which analyzed the energy demand of machine tools during the use phase, especially supplementary and auxiliary components are the main consumers [2],3,4]. Bode [3] indicated that more than 50% of the total energy consumption is generated by the supply and preparation of coolants, while an alternative case study conducted by Triebs [4] presented an energy demand of around 26% related to cutting fluids.

Although cutting fluids ensure both cooling and lubrication of the machining process, there are also alternative strategies such as minimal quantity lubrication (MQL) or dry machining. In comparison, dry machining does not require any fluid preparation or supply pumps, but has a negative effect on e.g. tool wear and removal of chips [5]. Therefore, an integrated view is required to avoid problem shifting. Thus, this study focuses on the modeling of energy and resource flows to conduct an integrated evaluation of the environmental and economic impact.

2. Research Background

2.1. Cutting fluids

Cutting fluids are used for machining processes to fulfil the primary tasks of cooling and lubrication [6]. As most of the

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mechanical power is converted into heat, the cutting fluid has to remove the heat from the contact zone to prevent a change in the material properties of the workpiece or the tool. The lubricating effect reduces friction between workpiece and tool, which leads to a reduced tool wear, heat generation and energy demand [7]. Besides these primary tasks, cutting fluids need to fulfil secondary tasks as well. These include, for example, chip removal from the working area or corrosion protection of both the machine tool and the workpiece [6].

According to DIN 51385, cutting fluids can be classified into based and water-based cutting fluids [8]. The oil-based fluids are not water-miscible and usually consist of a mineral oil-based base fluid and an application-specific additive formulation (up to 25% by weight), which is designed to positively influence the performance of the cutting fluid [9]. In contrast, the base fluid of water-based cutting fluids is emulsified or dissolved in water (up to 99%), and therefore a further distinction can be made between emulsions and solutions [6]. The use of an oil- or water-based cutting fluid depends on the requirements of the respective machining process. While oil-based cutting fluids are recommended if the focus is on the lubrication performance, water-based cutting fluids are used if the cooling effect is more relevant [6],9]. The cutting fluid performance, however, strongly depends on its composition, effect on the workpiece surface and supply into the contact zone [9,[10].

2.2. Cutting fluid strategies

In practice, conventional cutting fluids are mostly used as a flood strategy with 10 to 100 l/min. This intensive supply of cutting fluid leads to a high cooling and lubricating effect as well as improved chip removal, but also leads to the need for high volume flows of cutting fluid and additional peripheral equipment to filtrate and circulate them. In 1997, it was estimated that cutting fluids were responsible for 7-17 % of the workpiece-related manufacturing costs [11]. Further, skin diseases caused by contact with cutting fluid are among the most frequently recognized occupational diseases in the metalworking industry [12]. For this reason, alternative strategies were developed to reduce the use of cutting fluid or even to eliminate it (Table 1).

Table 1: Classification of cutting fluid strategies (based on [14]) Supply strategy Quantity of

applied fluid System design

flood 10 to 100 l/min circulating system

reduced quantity lub. 50 ml/h to 2 l/h circulating system minimal quantity lub. < 50 ml/h loss lubrication

dry no fluid no system

A possible alternative is MQL with a significantly reduced cutting fluid quantity of less than 50 ml/h or reduced quantity lubrication with less than 2 l/h [13]. By the use of compressed air a very fine air-oil mixture is produced, which is supplied to the contact zone. As most of the cutting fluid evaporates, it can be considered as a loss lubrication without filtration or reuse [13],14]. By contrast, dry machining does not require any cutting fluid system, as no tribologically effective fluid is

used in this process. The lack of cutting fluid leads to high temperatures in the contact zone, which negatively affect the workpiece quality and tool life as well as chip removal and production rate [5],6,13]. To meet these challenges the choice of tool material and coating is critical, but dry machining has so far only been successfully implemented for a few workpiece materials and machining operations (e.g. turning and milling of cast iron) in practice [5]. Besides dry and MQL, there are also other strategies such as the use of supercritical CO2, solid lubrication or cryogenic cooling. 2.3. Relevant elements of a machining system

As described, the type of cutting fluid strategy has a direct impact on the need for peripheral equipment to prepare the cutting fluid as well as on the tool preparation. However, the choice of strategy influences the need for further system elements such as an exhaust air system or chip treatment. The need for cleaning the workpieces in preparation for e.g. painting or for intermediate cleaning between different machining processes depends on the needs of a specific process chain [15]. Thus, cleaning processes are not within the scope of this research.

Fig. 1. Cutting fluid influenced elements of a machining system Machining process

Regarding the machine tool, the choice of strategy especially influences the material removal process and the cutting power, which varies depending on the strategy and the type of cutting fluid [16]. Further, the strategy also has an impact on possible process parameters. Due to the lubrication and cooling effect, higher material removal rates (MRR) can usually be realized by flood. Thus, the process time varies as well and not only the cutting energy, but also the energy of the entire machine tool has to be considered.

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Exhaust air system

The use of cutting fluids leads to the formation of oil mist and vapours. Therefore, the machine tool should be enclosed and an exhaust air systems needs to be installed to filter the air [17]. In the case of a decentralized system, the cutting fluid can be returned to the system after filtration from the exhaust air, as long as its composition has not changed during filtration. Otherwise, it is lost and must be refilled. The formation of cutting fluid emissions and thus the drag-out depends strongly on the selected process parameters [18]. However, exhaust air systems are not only recommended when cutting fluids are used, but also for dry machining. Ultrafine particles (nano-size or micro-size particles) are generated during dry machining processes, which pose a risk in terms of occupational health and safety [19].

Cutting fluid preparation

In case of flood but also reduced lubrication the supply of cutting fluid via pumps is required. Through pipes and nozzles, the cutting fluid passes from a tank into the contact zone between tool and workpiece. In contrast to MQL, the cutting fluid is returned into the filtration system together with the chips during flooding [20]. At this stage, the cutting fluid is cleaned of chips and impurities as well as tempered in order to ensure its quality and performance. According to the circulating system, the cleaned cutting fluid is then supplied back into the machine tool and the contact zone.

De-oiling

Regarding the scrap processing the de-oiling of the chips and swarf is required when cutting fluids are used, but does not apply for dry machining. A de-oiling of the chips can be done e.g. by pressing, centrifuging or briquetting the chips. The residual oil content of the chips after pressing is less than 1 % [21], after centrifuging or briquetting it is approximately 1 % [22]. Depending on the de-oiling process, the recovered cutting fluid could be returned to the circulation system.

Tool preparation

The used cutting fluid strategy can have an impact on the tool life. Compared to wet machining, the temperature at the cutting tool edge is significantly higher during dry machining, which causes softening of the tool edge and a deformation of its geometry [5]. For an efficient dry machining process the tools have to be adapted to the specific requirements of dry machining. This includes in particular wear and heat resistant tool materials and coatings. Especially in dry machining processes the tool materials need to combine the properties of high hardness, good toughness and chemical stability. Therefore, cemented carbides, ceramics, cubic boron nitride or polycrystalline compact diamond are used as tool materials [23]. Coatings are used to compensate the lubricating effect of cutting fluids and thus to further reduce tool wear. They are made from hard materials such as TiCN, CBN, etc. [5],13].

2.4. Research demand

The impact of different cutting fluid strategies on technical performance has been investigated in various studies. However, less attention has been paid to the environmental and economic effects. Fratila [24] conducted an

environmental comparison of near dry and flood machining including the use of coated and uncoated tools as well as metal scrap processing. Ginting et al. [25] also conducted an investigation of different cutting fluid strategies regarding the environmental performance. Both studies are based on experimentally collected data, which only apply to the individual case and do not consider all elements of the machining system. Benedicto et al. [26] compared the technical, economic and environmental performance of dry, flood, MQL and other strategies. This comparison is qualitative and is neither based on experimentally collected nor on modeled data. So far, a generic and comprehensive analysis that takes into account all influenced elements of the machining system is missing. Thus, the focus of this approach is to model the energy and resource flows in a generic and transferable form within the machining system shown in Fig. 1 and to estimate the impact of dry or wet machining with regard to energy and costs.

3. Methods

3.1. Modeling of the energy and resource flows

Although the energy and resource flows in Fig. 1 are generally valid for machining processes, the following models focus on turning processes and the strategies dry and flood.

Machining process

Regarding the machine tool the cutting power (Pc) can be

modeled based on the cutting force (Fc) and the cutting speed

(vc) [6]:

𝑃𝑃� � 𝐹𝐹� ∗ 𝑣𝑣� (1)

The calculation of the cutting force is usually based on the cutting force model of Kienzle. This is an empirical approach, which uses the unit specific force 𝑘𝑘� �� to remove a chip cross

section of 1 mm² (ℎ = 𝑏𝑏 = 1 mm) [27]. For common materials, the values for the specific cutting force 𝑘𝑘� �� and material

exponents � � �� can be taken from tables of e.g. Victor and Kienzle [28] or König und Essel [29]. However, as there are also other influencing parameters in practice, the equation has been extended by various correction factors in the past. Besides the correction factors for the rake angle 𝐾𝐾�, material

𝐾𝐾�, cutting speed 𝐾𝐾�, tool wear 𝐾𝐾��, there is also a factor for

the cutting fluid strategy 𝐾𝐾��.

𝐹𝐹�� 𝑘𝑘� ��∗ 𝑏𝑏 ∗ ℎ��∗ 𝐾𝐾�∗ 𝐾𝐾�∗ 𝐾𝐾�∗ 𝐾𝐾��∗ 𝐾𝐾�� (2)

For the correction factor 𝐾𝐾��, a value of 0.8 is used for

oil-based cutting fluids, 0.94 for water-oil-based emulsions and 1 for dry machining [29].

Exhaust air system

The exhaust air system usually operates at a fixed state and exhausts a constant volume flow [31]. The required exhaust air volume flow (𝑄𝑄��) can be determined depending on the

machining process, volume of the workstation and the cutting fluid strategy. In case of dry MQL, the required exhaust air volume flow is higher compared to the use of oil-based cutting fluids or water-based emulsions, which require the lowest exhaust air volume flow [17]. The exhaust air system can be described by the power demand of the ventilator 𝑃𝑃 ��.

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It depends on this volume flow as well as the total pressure increase 𝛥𝛥𝛥𝛥� and efficiency 𝜂𝜂 [31]:

𝑃𝑃 ��𝑄𝑄_{1000 ∙ 𝜂𝜂}��∙ 𝛥𝛥𝛥𝛥� (3) Cutting fluid preparation

The cutting fluid preparation comprises different elements such as the filtration unit, supply unit and optional cooling units [31]. However, for simplification only the pumps of the cutting fluid supply are taken into account in terms of power demand, as they account for the largest demand [4]. In addition, pressure losses due to the piping system are neglected as well. The power consumption of pumps (𝑃𝑃�) can

be calculated by equation (4) based on Ott [33]:

𝑃𝑃�� _{600 ⋅ 𝜂𝜂}𝑉𝑉��⋅ 𝛥𝛥��⋅ 𝜌𝜌��

��⋅ 𝜂𝜂�� (4)

It is calculated from the cutting fluid volume flow 𝑉𝑉��, the

pump pressure 𝛥𝛥��, density of the cutting fluid 𝜌𝜌��, hydraulic

efficiency 𝜂𝜂�� and motor efficiency 𝜂𝜂��.

Continuous cutting fluid demand

Throughout the entire machining system cutting fluid is dragged out and has to be refilled. The continuous cutting fluid demand can therefore be defined by that loss. It is caused by wetting, evaporation and aerosols as well as others e.g. leakages or spills [31],34,35]. However, for simplification only drag-out by wetting is further considered, because according to Petuelli it has the greatest impact during turning processes [34]. In this case, wetting actually describes the adhesion of cutting fluid on workpieces or chips, which depends on the viscosity and proportionally on the surface tension of the fluid [35]. The loss due to wetting can be calculated by an empirical regression model from Petuelli [34], which was also used and modified by Winter [31].

𝑚𝑚�� _�� 𝑐𝑐��∗ �𝑡𝑡�� for �� 𝑡𝑡�� 600� (5)

The specific cutting fluid mass (𝑚𝑚��) demands on dripping

time (𝑡𝑡��) and regression coefficients a, b and c, which are

determined experimentally [30]. This model can be applied for the drag-out via workpiece and chips.

De-oiling

As described before, the chips can be de-oiled in order to reduce the cutting fluid loss. For this purpose, a centrifuge is modeled to separate the adhering cutting fluid from the chips. Centrifuges are widely used for chip treatment after turning processes [22]. For this process, an electric motor accelerates a basket, which can also be described as a rotating cylinder with chips and cutting fluid inside. The power demand can be calculated by the kinetic energy of the rotating cylinder ( 𝐸𝐸�� ) and the kinetic energy to accelerate the chips

( 𝐸𝐸�� ) as well as the acceleration time ( 𝑡𝑡��) and the

efficiency (𝜂𝜂��).

𝑃𝑃��𝐸𝐸�� 𝐸𝐸_𝑡𝑡�� ∗ 𝜂𝜂��

�� (6)

Usually an acceleration time of five seconds is assumed for centrifuges [36]. After centrifugation, residual fluid (𝑓𝑓��)

remains on the chips, which is less for chips after turning or milling than for swarf after grinding. The residual moisture

levels indicated by Mayfran [22] for different materials and cutting fluids are listed in Table 2 for chips from processes with geometrically defined cutting edges. However, the degree of wetting before centrifugation has no influence on the residual fluid after centrifugation [36].

Table 2: Residual fluid of chips residual

fluid 𝑓𝑓�� [%] steel cast iron aluminum

emulsion 1,0 2,0 2,5

oil 1,5 3,0 3,5

Tool preparation

Tools can be modeled using the embodied energy of the material and production process energy. The respective data can be taken from the literature or a database e.g. CES material selector [37]. Therefore, it is necessary to model the tool wear in order to determine the number of tools that are needed to machine a certain number of workpieces. The first tool wear models are based on Taylor. Through experimental investigations, an exponential relationship between the cutting speed and the tool life was determined. In an extended Taylor equation, the impact of the feed rate and the cutting depth was also included. However, the models do not include the impact of different cutting fluid strategies. Similar to the cutting force model, the Taylor equation is therefore extended by a correction factor (𝐾𝐾��) as well.

𝑡𝑡�� 𝑣𝑣� �

𝑐𝑐� ∗ 𝐾𝐾�� (7)

According to Taylor the tool life (𝑡𝑡�) is calculated based on

the cutting speed (𝑣𝑣�) and the coefficients (𝑘𝑘) and (𝑐𝑐), which

depend on the respective materials and tools. The values can be taken from different tables in the literature, similar to the values for the correction factor (𝐾𝐾��). For example, Degner et

al. present recommended values for various materials and tools, where a value of 1 is used for wet machining and 0.4 to 0.95 for dry machining [38].

3.2. Evaluation

As described, this study uses energy and costs to represent the environmental and economic impacts. The calculation of the environmental impact due to energy consumption ( 𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬 ) is shown in equation (8). This equation

considers the energy demand due to exhaust air (𝑷𝑷𝑬𝑬𝒆𝒆𝒆𝒆𝒆�, pump

(𝑷𝑷𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒆) and cutting power (𝑷𝑷𝒄𝒄𝒆) during the cutting process

time (𝒕𝒕𝒄𝒄), standby power of the machine tool (𝑷𝑷𝒊𝒊𝒊𝒊𝒊𝒊𝑬𝑬) during

machine downtime (𝒕𝒕𝒊𝒊) due to used tool life (𝒕𝒕𝒊𝒊), de-oiling

power (𝑷𝑷𝒊𝒊𝑬𝑬) during the de-oiling time (𝒕𝒕𝒊𝒊𝑬𝑬) as well as the

embodied energy for the tool (𝑬𝑬𝒕𝒕) based on used tool life and

for the cutting fluid (𝑬𝑬𝒄𝒄𝒄𝒄) based on the loss (𝒑𝒑𝒄𝒄𝒄𝒄).

𝐸𝐸𝐸𝐸𝑣𝑣�� 𝑃𝑃��𝒆� 𝑃𝑃�� 𝑃𝑃�� ∗ 𝑡𝑡�� 𝑃𝑃�� ∗ 𝑡𝑡�∗𝑡𝑡_𝑡𝑡� �

�𝑃𝑃��∗ 𝑡𝑡��𝑡𝑡_𝑡𝑡�

�∗ 𝐸𝐸� � 𝑚𝑚��∗ 𝐸𝐸��

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The economic impact due to costs (𝐸𝐸𝑐𝑐𝐸𝐸𝐸𝐸) is calculated by

equation (9). This equation considers the specific costs pi

(with i for energy, tools, cutting fluid, machine tool and labor) for the demand of energy and resources as described before.

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𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 � �� ∗ 𝑡𝑡�� ∗ 𝑡𝑡� ∗�_��_�� ∗ 𝑡𝑡�� (9) ∗ 𝑝𝑝� �𝑡𝑡_𝑡𝑡� �∗ 𝑝𝑝� � ��∗ 𝑝𝑝�� 𝑝𝑝�� 𝑝𝑝�� ∗ �𝑡𝑡�� 𝑡𝑡�∗ 𝑡𝑡� 𝑡𝑡�� 4. Case study 4.1. Experimental setup

In order to apply the developed method for exemplary scenarios, a case study was carried out. This case study was performed on a Spinner CNC turning machine with an external turning process and a material removal of Vw = 6.7 cm³/workpiece. An exhaust air filter with two

settings of 600 m3_{/h and 1000 m}3_{/h as well as a cutting fluid}

filter were powered and controlled by the turning machine. The experiments were carried out as dry and wet machining with a mineral-oil based emulsion of 7 % and a non-water miscible oil with a volume flow of 11 l/min. During the experiments the power consumption was measured with a three-phase power meter type PPC-3 from Load Controls and recorded via a LabView based processing system and the surface roughness was measured using a surface measurement device from Hommel-Etamic T1000.

4.2. Model validation

The results of the experiments and models are presented and compared in Table 3. It shows that the modeled cutting power is close to reality and the deviation is less than 10%. The model used these values kc1.1= 1563N/mm², mc= 0.26, h = 0.1mm, b = 0.22mm, 𝐾𝐾�= 0.99, 𝐾𝐾�= 1, 𝐾𝐾�= 0.97, 𝐾𝐾��= 1.

Table 3: Comparison of experimental and modeled results

Regarding the pump power, a deviation of less than 10% can also be observed for the emulsion, but a significantly larger deviation for the oil. As the pressure losses caused by the piping system were neglected, it could already be assumed that the modeled power is lower. According to [17] the volume flow of the exhaust air system was set to 600 m3_{/h for}

the emulsion and to 1000 m3_{/h for the oil and dry machining.}

In comparison, the calculated results are too high for the emulsion and too low for the oil and dry setting. This could be explained by the efficiency, which was assumed to be the same for both settings. The models of the de-oiling process, cutting fluid loss and tool life could not be verified experimentally for this study and were calculated according to

section 3.1 with 𝑘𝑘= -6.25, C = 234 m/min and 𝐾𝐾��= 1 for wet

and 0.7 for dry machining to calculate the tool life.

4.3. Evaluation of different scenarios

To compare the environmental and economic impact of the strategies energy and costs are calculated according to equation (8) and (9) using energy prices from Germany for three different scenarios. The basic scenario is shown in Table 3, a scenario with an increased MRR and a quality scenario, which assumes that a surface roughness of Rz smaller 3 µm

and Ra smaller 0.7 µm must be achieved. For each scenario

the modeled energy and resources are used to allow a fair comparison and a tool life criterion of VB = 0.2 mm was assumed. The results are shown in Fig. 2 to Fig. 4.

Fig. 2. Results for the basic scenario

Fig. 3. Results for the scenario with increased MRR

Fig. 4. Results for the quality scenario with a given surface roughness

Based on the results in Fig. 2 it can be determined that the use of oil has the highest environmental and economic impact. This is mainly related to the consumption of the oil. However, by increasing the cutting speed and thus the MRR, the influence of the tool life increases as well. This is especially significant for dry machining and therefore the use of

exp. modelled Δ [% ] Pc [W] -cutting power Dry 314 348 9.7 Emulsion 296 313 5.4 Oil 297 296 0.3 Pp [W] - pump power Dry - - -Emulsion 850 792 6.8 Oil 829 648 21.8 Pea [W] -exhaust air Dry 282 222 21.3 Emulsion 82 134 38.8 Oil 282 222 21.3 Process: Turning Tool: CCMT 09T304 Workpiece: 42 CrMoS4 V

Cutting velocity: 110 [m/min] Cutting depth : 0.5 [mm] Feed rate: 0.15 [mm/rev.]

0% 20% 40% 60% 80% 100%

Process Tool Change De-Oiling Tool CF Labour/Machine

O il Em uls io n D ry Costs [€] Energy [MJ-eq.] Costs [€] Costs [€] Energy [MJ-eq.] Energy [MJ-eq.] Process: Turning Tool: CCMT 09T304 Workpiece: 42 CrMoS4 V

Cutting velocity: 110 [m/min] Cutting depth : 0,5 [mm] Feed rate: 0,15 [mm/rev.]

O il Em uls io n Process: Turning Tool: CCMT 09T304 Workpiece: 42 CrMoS4 V

Cutting velocity : 160 [m/min] Cutting depth : 0,5 [mm] Feed rate: 0,15 [mm/rev.]

0% 20% 40% 60% 80% 100% l l n n y y

Costs [€] Energy [MJ-eq.] Costs [€] Costs [€] Energy [MJ-eq.] Energy [MJ-eq.] D ry Process: Turning Tool: CCMT 09T304 Workpiece: 42 CrMoS4 V

Cutting speed: 130 [m/min] Cutting depth : 0,78 [mm] Feed rate: 0,1 [mm/U]

Cutting velocity: 90 [m/min] Cutting depth : 0,22 [mm] Feed rate: 0,1 [mm/rev.]

Oil/emulsion: Dry:

0% 20% 40% 60% 80% 100%

y y

O il Em uls io n D ry Costs [€] Energy [MJ-eq.] Costs [€] Costs [€] Energy [MJ-eq.] Energy [MJ-eq.]

(6)

emulsion has the lowest impact in this scenario (see Fig. 3). Regarding the third scenario different process parameters had to be selected for wet and dry machining. These are based on the conducted experiments and even if the surface roughness is slightly higher for the emulsion than for the oil, all strategies meet the given requirements with the selected parameters. For this scenario the results in Fig. 4 show that the use of emulsion has the lowest impact for each impact category. The selected parameters result in a significantly lower MRR for dry machining compared to wet machining and thus also in a higher process time. The energy consumption of the machine tool and peripheral equipment is therefore higher as well.

5. Conclusion

This paper develop a general concept to evaluate the environmental and economic impact of different cutting fluid strategies with a focus on flood and dry machining. The elements of a machining system that are influenced by the specific cutting fluid strategy are presented and modeled according to the energy and resource flows. On the basis of these models the overall environmental and economic impact expressed by energy and costs can be evaluated for a specific machining scenario. The developed method and models were also tested within a case study. As a result, it can be concluded that not only the supply strategy, but also the type of cutting fluid has a significant impact. Regarding the energy use, the embodied energy of the oil has the highest impact. Further, the paper investigates a scenario with increased MRR and with different process parameters to achieve a given surface quality, which reveals a significant influence of process time and tool life. However, the actual results of such a comparison are always case-specific and strongly depend on the selected process and parameters. The developed method can be used as a basic decision support in order to theoretically evaluate alternatives before practical implementation. In future studies this approach will be extended by considering further cutting fluid strategies and a sensitivity analysis of the influencing parameters.

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