- Email: [email protected]
Additive manufacturing with GMAW welding and CMT technology
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Procedia Manufacturing 00 (2017) 000–000 Available online atatwww.sciencedirect.com Available online www.sciencedirect.com Procedia Manufacturing 00 (2017) 000–000
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Procedia Manufacturing 13 (2017) 840–847 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia
Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June Manufacturing Engineering Society Conference 2017, International Vigo (Pontevedra), Spain2017, MESIC 2017, 28-30 June 2017, Vigo (Pontevedra), Spain
Additive manufacturing with GMAW welding and CMT technology Additive manufacturing with GMAWConference welding andMESIC CMT technology Manufacturing Engineering Society International 2017, 2017, 28-30 June b c 2017, Vigo (Pontevedra), Spain J. Gonzálezaa, I. Rodríguez , J-L. Prado-Cerqueira , J.L. Diéguezdd, A. Pereiradd b c J. González , I. Rodríguez , J-L. Prado-Cerqueira , J.L. Diéguez , A. Pereira
VIGOTEC, Cr. do Portal, Parcela 15, 1º Of. 11, Vigo (Pontevedra) 36314, Spain. Costing models for capacity optimization inIndustrial Industry 4.0: Trade-off Cr. Portal, Parcela 15, 1º(CTAG), Of. 11, Vigo (Pontevedra) 36314, Spain.Porriño CentroVIGOTEC, Tecnológico dedo Automoción de Galicia Polígono A Granxa. Centro Tecnológico de Automoción de Galicia (CTAG), Industrial Granxa. Porriño 36208, Spain, Department of Mechanical Manufacturing, Polytechnic Institute of Vigo,Polígono C/ Torrecedeira 88,A Vigo (Pontevedra) between used capacity andof Vigo, operational Department of Mechanical Manufacturing, Polytechnic Institute C/ Torrecedeira 88,efficiency Vigo (Pontevedra) 36208, Spain, [email protected] a
b
a
b
c c
d d
[email protected] Department of Design in Engineering, University of Vigo, C/ Torrecedeira 86, Vigo (Pontevedra) 36208, Spain, Department of Design in Engineering, University of a,* Vigo, C/ Torrecedeira a b 86, Vigo (Pontevedra) b 36208, Spain,
A. Santana , P. Afonso , A. Zanin , R. Wernke a
University of Minho, 4800-058 Guimarães, Portugal
Abstract b Unochapecó, 89809-000 Chapecó, SC, Brazil Abstract Additive manufacturing will be an option to develop prototypes or mechanical parts that will be made faster and cheaper than other Additive manufacturing be anoroption to develop prototypes or mechanical that was will to be study made the faster and cheaper than other techniques such as laser will cladding electron beam. The main objective of thisparts research optimal initial conditions techniques such additive as laser cladding or electron beam. The main objective this research was to study the optimal of the proposed manufacturing system in order to obtain metalofprototypes. This optimal conditions haveinitial beenconditions presented Abstract of the proposed additive manufacturing system in order to obtain prototypes. optimal conditions have been presented taking into account the measurements of geometrical conditions andmetal surface finishing.This The proposed additive manufacturing system taking account the of measurements of geometrical and surface finishing. The proposed additive manufacturing consistinto on integration of"Industry a Fronius TPS 4000 CMTconditions R welding machine with Vario CNC milling machine.system Once Under thean concept 4.0", production processes will bea BF30 pushed to Optimun be increasingly interconnected, consist on anwas integration a Fronius 4000 R welding machine with a been BF30obtained. Vario Optimun CNC milling machine. Once the material selected, the optimal conditions to make the much first layer have Also the geometrical shape of the information based on aofreal time TPS basis and,CMT necessarily, more efficient. In this context, capacity optimization the material was selected, the optimal conditions to make thesuch firstaslayer have and beencylindrical obtained. parts Also have the geometrical shape of the defined wall has been predicted. Previous simple geometries, prismatic been manufactured. goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. defined has been Published predicted. by Previous simple such as prismatic and cylindrical parts have been manufactured. © 2017 wall The Authors. B.V. geometries, Indeed, lean management and Elsevier continuous improvement approaches suggest capacity optimization instead of © 2017 The Authors. Published by B.V. committee of the Manufacturing Engineering Society International Conference Peer-review under responsibility of Elsevier the scientific © 2017 The Authors. Published by Elsevier B.V. maximization. The study of capacity optimization andofcosting models is Engineering an important research topic that deserves Peer-review under responsibility of the scientific committee the Manufacturing Society International Conference 2017. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical 2017.
model forAdditive capacity management based on different costing models (ABC and TDABC). A generic model has been Keywords: Manufacturing; Rapid prototypin; GMAW; CMT. developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s Keywords: Additive Manufacturing; Rapid prototypin; GMAW; CMT. value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency. 1. Introduction 1.2017 Introduction © The Authors. Published by Elsevier B.V. Peer-review responsibility of the scientific of the challenging Manufacturing Engineering Society International Conference Product under manufacturing industry is facingcommittee two important tasks, reduction of product development time 2017. manufacturing industry facing two important challenging tasks, of product development time andProduct improvement on flexibility forismanufacturing products [1]; besides, to reduction reduce machining times and materials and improvement flexibility for items manufacturing productsin[1]; besides, in to continuous reduce machining times and standards materials wastes is one of theonmost important to be competitive an industry evolution. ASTM Keywords: Capacity Management; Idle Capacity; Operational Efficiency wastes isCost oneModels; ofmanufacturing the ABC; mostTDABC; important be competitive in materials an industry in continuous evolution. ASTM standards define additive (AM) items as theto“process of joining to make objects from 3D model data, usually define additive manufacturing as the “process of joining materials to [2]. make objects from 3D model data, usually layer upon layer, as opposed to(AM) subtractive manufacturing methodologies” layer upon layer, opposed to subtractive manufacturing methodologies” [2].technologies. These technologies have this field theaslaser and the electron beam have been the most developed 1.InIntroduction In this field the laserasand electron beenheat theinput, most developed technologies. These technologies have gained wide popularity thethe heat sourcebeam due tohave its low high precision and less susceptibility to distortion gained wide popularity as the heat source due to its low heat input, high precision and less susceptibility to distortion The cost of idle capacity is a fundamental information for companies and their management of extreme importance in modern systems. In general, it isB.V. defined as unused capacity or production potential and can be measured 2351-9789 ©production 2017 The Authors. Published by Elsevier 2351-9789 2017responsibility The Authors. Published by Elsevier B.V.hours Peer-review of the scientific committee of the Manufacturing Engineering Conference in several©under ways: tons of production, available of manufacturing, etc.Society The International management of the 2017. idle capacity Peer-review underTel.: responsibility the761; scientific committee the Manufacturing Engineering Society International Conference 2017. * Paulo Afonso. +351 253 of 510 fax: +351 253 604of741 E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 10.1016/j.promfg.2017.09.189
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by selective laser melting of metallic powders [3]. The recently improvements in welding have made it possible to replace the power source from laser or electron beam with a multi-choice welding source, and normally these sources allow to download all welding information to control the process. In the Wire Arc Additive Manufacturing (WAAM) process, 3D metallic components are built by depositing beads of weld metal layer by layer, using welding processes such as Gas Metal Arc Welding (GMAW) combined with a positioning system as a CNC milling machine or a Robot [4]. On the one hand, the main advantage of WAAM processes are the high deposition rate that allows higher speed of manufacturing, being more suitable to produce large size components. On the other hand, the accuracy of laser or electron beam is higher as have been proposed by Santos, Wanjara et al, but in most cases will be necessary to reprocess the components because of the design surface would need a functional surface or accuracy geometry [5][6]. However, this method of fabrication allows to produce metal parts directly from computer CAD data or CAM systems without using moulds [7]. With the actual and forecast aircraft aerospace market expansion rate, the demand for aluminium and titanium parts is increasing. Traditional methods produce these parts by machining processes, which produce a lot of debris or waste resulting from machining. Keeping in mind that the initial volume of the workpiece (stock) is around 10, or even 20, times more than the finished component, and that these materials are so expensive, in the aircraft industry there is the pressing need to develop a process to replace the machining process [8]. Another important aspect to consider is that rapid prototyping will be a low cost solution in the manufacturing industry to test the new designs without need of specific tools. When creating a new design, it may be necessary to produce several prototypes until the design is working properly. [9]. With traditional manufacturing processes the parts often contain more material and weight than actually needed to safely support the design loads. For this reason, additive manufacturing processes are a new step to develop more efficient components [10]. 2. Experimental methodology Previous works using GMAW welding as additive manufacturing have shown that results are directly related with the materials and equipment used. In order to achieve a good system performance, the shape and the dimensions of the weld bead are the main parameters to be considered [11]. 2.1 Materials and equipment Two different materials have been considered in this experimental work. A solid steel welding wire with 0.8 mm of diameter has been the material selected as the feedstock material in the welding system (commercial name: AWS ER70S-6). S235JR steel plates have been used as substrate to perform the experiments. The samples dimensions were designed according the fixture device employed, as can be shown in Fig. 1. 2.1. Materials and equipment Two different materials have been considered in this experimental work. A solid steel welding wire with 0.8 mm of diameter has been the material selected as the feedstock material in the welding system (commercial name: AWS ER70S-6). S235JR steel plates have been used as substrate to perform the experiments. The samples dimensions were designed according the fixture device employed, as can be shown in Fig.1.
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Fig. 1. (a) first picture; (b) second picture.
According to ISO 14175, a mixture composed of CO2 (15%) and Argon (85%) has been used as protecting gas. The results of using the mixture gas are: stability in the process, improvement in the surface finishing quality and reduction of the splatters. The less amount of CO2, the smaller the welding drops. The experimental equipment used to carry out the welding deposition is composed by the integration of two different systems: Welding system. The welding machine employed was a Fronius TPS 4000 CMT R machine that allows to weld using Cold Metal Transfer technology, patented by Fronius®. The basic principle of this technology is the intensity and voltage control during the deposition; the results are the reduction of welding temperature and the wire movement optimization (Fig. 2). Consequently, a better quality of weld beads were obtained than those made using conventional GMAW welding. Positioning system. According to the main objective, the movement of the whole system should be easily controlled. A BF 30 Vario Optimum CNC milling machine has been adapted to place the torch welding in the Z Axis. The movement of th X-Y table of the CNC system enables a layer of welding deposition in a fixed Z level, being the welding torch fixed to the milling head. When the next layer has to be deposited, the Z axis elevates the torch and the deposition can start again with the X-Y table movement. An auxiliary working table has been necessary to isolate electrically both systems and obtain a little source of cooling. The experimental equipment described in this section is shown in the Fig. 2.
Fig. 2. (a) Wire movement during the welding process using CMT technology; (b) Experimental equipment.
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2.2. Experimental Design Several experiments have been designed in order to study the best process conditions. The welding machine selected allows a very configurable set up, thus a first elemental experiment had been developed to know the relation between the input parameters. To start with, certain tests have been performed and the main variable parameters to take into account have been determined: Intensity (A). It is the necessary current that the machine has to supply in order to achieve the feeding speed programed. The intensity range of this welding machine is 34 A-300 A. Welding speed (mm/min). It is the CNC machine speed. It is directly programed in the CNC code and each one of the axis can have different speeds. Arc length correction (%). It is set between -30% and 30%. Negative values mean smaller arc length while positive values mean bigger arc length. Dynamic correction. The operating range is between -5 and 5. Negative values are those relating to the lower force of weld drop detachment and positive values are those ones corresponding to the greater forces of detachment. This parameter is also called weld drop detachment correction. Considering these parameters and their corresponding ranges, it has been programed the first essay to obtain the best input parameters. Eight series of weld beads have been designed. The output parameters to measure, defined to stablish the optimal conditions, are the weld bead height (H) and the weld bead width (w). A Nikon MSZ800N stereoscopic microscope and a DSIF2 photomicrography camera have been used to measure the weld bead geometry. Three sections of each experiment have been analysed and the arithmetic average has been the representative datum used (Fig. 3).
Fig. 3. Sample with the weld beads deposited and weld bead geometrical analysis.
The next step to be able of fabricating complex pieces is to investigate how the system behaviour is when a weld bead is deposited over the first one. In this case, taken into account the first experiment results, the input parameters used are the followings:
Arc length correction: 0%. Dynamic correction: 0. Welding speed: variable between 200 mm/min and 800 mm/min. Intensity: variable between 27 A and 100 A. Number of layers (n): variable between 10 layers and 40 layers.
The trajectories programming has been made according to the geometrical measurements studied in the first essay. As can be shown in the next section, it has been necessary to adapt the height of each layer depending on the number of layers deposited, so the weld bead output parameters are not precise in order to perform the n-layers wall essay.
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The measurements stablished to study which configuration of input parameters has the better results are divided into two groups: Geometrical measurements, shown in Fig. 4. Taken with a Mitutoyo Coordinating Measuring Machine and with the microscope used in the first essay, considering different sections of the wall. o Wall width, X (mm). o Wall height, Z (mm). o Growth per layer, Zp (mm). It is the wall height (Z) divided by the layers deposited (n). o Angle between substrate and wall, α (º). o Height deviation (mm). In order to analyse the height variation, ten wall heights measurements have been made with the CMM. The mean and the maximum deviation have been calculated as the representative parameters.
Fig. 4. Geometrical measurements.
Topographical measurements. It has been used a 3D perfilometry microscope (Alicona InfiniteFocus SL) with variable focus for the lateral surfaces characterization (Fig. 5). The microscope provides a stereolithographic file (STL) which will be processed to obtain the most outstanding height parameters collected in ISO 25178. o Sa arithmetical mean height of the surface. o Ssk (skewness of height distribution) represents the surface symmetry. o Sku (kurtosis of height distribution) shows the existence of valleys or peaks if Sku>3. When Sku<3 it means there are neither remarkable valleys nor peaks. o Vvc Volume of void in the core or kernel, between two material ratios p and q (in %), calculated in the zone between c1 and c2.
Fig. 5. Surface wall characterization.
Furthermore, a histogram that indicates the density distribution of the data is combining with the roughness profile, as can be shown in Fig. 5. A global vision of the surface topography is obtained attending the shape of this curve.
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3. Results and discussion. With the material selected, the optimal conditions to make the first layer have been obtained. The geometrical shape of the defined wall has also been predicted, and with these dates have been allowed to do walls. The measurements of the walls had been made with Mitutoyo and the obtained measurements have been the height and the width (Fig. 6). With all this information, the layer height has been stablished to do simples geometries, such as prismatic and cylindrical parts.
Fig. 6. Image weld sean.
The material contraction and the regularity of the surface during the welding process were difficult to predict, so that weld seam pilling couldn’t be completely controlled. Due to the above, an oversized part was programed to compensate this contractions and deviations. With the Nikon SMZ800 microscope it was checked that the perpendicularity of the wall is near to 90 degrees, but in the most of the cases with a deviation between 3 to 4 degrees in the base (Fig. 7).
Fig. 7 Measurements in Nikon SMZ800.
On the one hand, the total height of the weld seam layers can be predicted from the obtained model base on the first experiments. But the obtained results show that the height of the created wall could be slightly different from the theoretical height. Predict the real growing can be representative to develop complex geometries and functional parts. As it can be observed in Fig. 8, both height and width increase with the weld intensity. The performance of the system to control the parameters and ideal deposition rate can be predicted with these experiments.
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Fig. 8. Comparison between height and width front Intensity of welding.
On the other hand, when the wall is obtained by more than 20 layers, the growing up of the height is smaller than the predicted one with the 10 layers model, probably because of the warm up. As it can be note in Fig. 9, for the 10 layers experiment (I=40A), the growth per layer, Zp, is about 1,0 mm (height=10mm) and for the 40 layers experiment (I=40A), the growth per layer is about 0,9 mm (height=36mm). This growing of the wall has to be considered to create walls over 20 layers, and not only for height but also for width.
Fig. 9. Analysis width and height versus number of layers.
The Zp is a parameter to take into account when a part is created with an additive manufacturing process. In welding process the gap of the torch should be between 7 to 10mm over the last layer, and if this gap is not the good one, several erros could be occur during the welding process. In relation to topographic measurements, after applying the adecuate filter, the roughness surface has been obtained. The behavior of the parameter Sz reflects that there are no significant variability versus the intensity of the welding process. The assimetry parameter Ssk decrease slightly with the intensity (A), as it can be shown in Fig. 10. The negative signal of Ssk presents more valleys than peaks.
Fig. 10. a) Sz versus Intensity; b) Ssk versus intensity.
The Fig. 11 shows the wall topography of sample 3 (7mm x 20 mm), correponding to an intensity of 65A. It could be noticed the several layers (20) of the construction of the wall.
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µm 200
ISO 25178 150
100
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Height Parameters Ssk
-0.862
Sku
5.44
Sz
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Sa
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Functional Parameters (V… Vvc
0.0261
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Fig. 11. a) 3D topography of sample 3: b) Parameters sample 3.
4. Conclusions In conclusion, additive manufacturing of metals has been made with a non-expensive system based on the integration of CMT welding technology and CNC milling machine. It’s a smart cost solution for developing and testing new components in comparison with only machining process, laser or electron beam technologies. Furthermore, in relation to the topographic measurements, Sz could be consider an interesting parameter in this process in order to know the oversized amount of machining. Finally, in this work an oversized piece has been produced in order to do a post-machining required to obtain most accuracy in our designed part. A machining process after deposition will be included in future experiments in order to improve the geometrical accuracy. Acknowledgements This work has been developed within the framework of the doctorate program in Industrial Technologies of the UNED and it has been financially supported by the funds provided through the program “Innova” of the Galician Department of Education (Spain), in collaboration with the company Ucalsa S.A. The authors would appreciate the support and advices of all the staff of the department of manufacturing of the polytechnic institute of Vigo. References [1] X. Yan, P. Gu, CAD Comput. Aided Des. 28 (1996) (4) 307–318. [2] ASTM International, F2792-12a - Standard Terminology for Additive Manufacturing Technologies, Rapid Manuf. Assoc., pp. 10–12, 2013. [3] J. Xiong, G. Zhang, H. Gao, L. Wu, Robot. Comput. Integr. Manuf. 29 (2013) (2) 417–423. [4] J. Ding et al, Comput. Mater. Sci. 50 (2011) (12) 3315–3322. [5] E. C. Santos, M. Shiomi, K. Osakada, T. Laoui, Int. J. Mach. Tools Manuf. 46 (2006) (12–13) 1459–1468. [6] P. Wanjara, M. Brochu, M. Jahazi, Mater. Des. 28 (2007) (8) 2278–2286. [7] Y. Cao, S. Zhu, X. Liang, W. Wang, Robot. Comput. Integr. Manuf. 27 (2011) (3) 641–645. [8] S. W. Williams, F. Martina, A. C. Addison, J. Ding, G. Pardal, P. Colegrove, Mater. Sci. Technol. 836 (2015) 1743284715Y.000. [9] K. G. Cooper, Rapid Prototyping Technology Selection and Application, 2001. [10] P. Edwards, A. O’Conner, M. Ramulu, J. Manuf. Sci. Eng. 135 (2013) (6) 61016. [11] S. Akula, K. P. Karunakaran, Robot. Comput. Integr. Manuf. 22 (2006) (2) 113–123.
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Procedia Manufacturing 13 (2017) 840–847 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia
Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June Manufacturing Engineering Society Conference 2017, International Vigo (Pontevedra), Spain2017, MESIC 2017, 28-30 June 2017, Vigo (Pontevedra), Spain
Additive manufacturing with GMAW welding and CMT technology Additive manufacturing with GMAWConference welding andMESIC CMT technology Manufacturing Engineering Society International 2017, 2017, 28-30 June b c 2017, Vigo (Pontevedra), Spain J. Gonzálezaa, I. Rodríguez , J-L. Prado-Cerqueira , J.L. Diéguezdd, A. Pereiradd b c J. González , I. Rodríguez , J-L. Prado-Cerqueira , J.L. Diéguez , A. Pereira
VIGOTEC, Cr. do Portal, Parcela 15, 1º Of. 11, Vigo (Pontevedra) 36314, Spain. Costing models for capacity optimization inIndustrial Industry 4.0: Trade-off Cr. Portal, Parcela 15, 1º(CTAG), Of. 11, Vigo (Pontevedra) 36314, Spain.Porriño CentroVIGOTEC, Tecnológico dedo Automoción de Galicia Polígono A Granxa. Centro Tecnológico de Automoción de Galicia (CTAG), Industrial Granxa. Porriño 36208, Spain, Department of Mechanical Manufacturing, Polytechnic Institute of Vigo,Polígono C/ Torrecedeira 88,A Vigo (Pontevedra) between used capacity andof Vigo, operational Department of Mechanical Manufacturing, Polytechnic Institute C/ Torrecedeira 88,efficiency Vigo (Pontevedra) 36208, Spain, [email protected] a
b
a
b
c c
d d
[email protected] Department of Design in Engineering, University of Vigo, C/ Torrecedeira 86, Vigo (Pontevedra) 36208, Spain, Department of Design in Engineering, University of a,* Vigo, C/ Torrecedeira a b 86, Vigo (Pontevedra) b 36208, Spain,
A. Santana , P. Afonso , A. Zanin , R. Wernke a
University of Minho, 4800-058 Guimarães, Portugal
Abstract b Unochapecó, 89809-000 Chapecó, SC, Brazil Abstract Additive manufacturing will be an option to develop prototypes or mechanical parts that will be made faster and cheaper than other Additive manufacturing be anoroption to develop prototypes or mechanical that was will to be study made the faster and cheaper than other techniques such as laser will cladding electron beam. The main objective of thisparts research optimal initial conditions techniques such additive as laser cladding or electron beam. The main objective this research was to study the optimal of the proposed manufacturing system in order to obtain metalofprototypes. This optimal conditions haveinitial beenconditions presented Abstract of the proposed additive manufacturing system in order to obtain prototypes. optimal conditions have been presented taking into account the measurements of geometrical conditions andmetal surface finishing.This The proposed additive manufacturing system taking account the of measurements of geometrical and surface finishing. The proposed additive manufacturing consistinto on integration of"Industry a Fronius TPS 4000 CMTconditions R welding machine with Vario CNC milling machine.system Once Under thean concept 4.0", production processes will bea BF30 pushed to Optimun be increasingly interconnected, consist on anwas integration a Fronius 4000 R welding machine with a been BF30obtained. Vario Optimun CNC milling machine. Once the material selected, the optimal conditions to make the much first layer have Also the geometrical shape of the information based on aofreal time TPS basis and,CMT necessarily, more efficient. In this context, capacity optimization the material was selected, the optimal conditions to make thesuch firstaslayer have and beencylindrical obtained. parts Also have the geometrical shape of the defined wall has been predicted. Previous simple geometries, prismatic been manufactured. goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. defined has been Published predicted. by Previous simple such as prismatic and cylindrical parts have been manufactured. © 2017 wall The Authors. B.V. geometries, Indeed, lean management and Elsevier continuous improvement approaches suggest capacity optimization instead of © 2017 The Authors. Published by B.V. committee of the Manufacturing Engineering Society International Conference Peer-review under responsibility of Elsevier the scientific © 2017 The Authors. Published by Elsevier B.V. maximization. The study of capacity optimization andofcosting models is Engineering an important research topic that deserves Peer-review under responsibility of the scientific committee the Manufacturing Society International Conference 2017. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical 2017.
model forAdditive capacity management based on different costing models (ABC and TDABC). A generic model has been Keywords: Manufacturing; Rapid prototypin; GMAW; CMT. developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s Keywords: Additive Manufacturing; Rapid prototypin; GMAW; CMT. value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency. 1. Introduction 1.2017 Introduction © The Authors. Published by Elsevier B.V. Peer-review responsibility of the scientific of the challenging Manufacturing Engineering Society International Conference Product under manufacturing industry is facingcommittee two important tasks, reduction of product development time 2017. manufacturing industry facing two important challenging tasks, of product development time andProduct improvement on flexibility forismanufacturing products [1]; besides, to reduction reduce machining times and materials and improvement flexibility for items manufacturing productsin[1]; besides, in to continuous reduce machining times and standards materials wastes is one of theonmost important to be competitive an industry evolution. ASTM Keywords: Capacity Management; Idle Capacity; Operational Efficiency wastes isCost oneModels; ofmanufacturing the ABC; mostTDABC; important be competitive in materials an industry in continuous evolution. ASTM standards define additive (AM) items as theto“process of joining to make objects from 3D model data, usually define additive manufacturing as the “process of joining materials to [2]. make objects from 3D model data, usually layer upon layer, as opposed to(AM) subtractive manufacturing methodologies” layer upon layer, opposed to subtractive manufacturing methodologies” [2].technologies. These technologies have this field theaslaser and the electron beam have been the most developed 1.InIntroduction In this field the laserasand electron beenheat theinput, most developed technologies. These technologies have gained wide popularity thethe heat sourcebeam due tohave its low high precision and less susceptibility to distortion gained wide popularity as the heat source due to its low heat input, high precision and less susceptibility to distortion The cost of idle capacity is a fundamental information for companies and their management of extreme importance in modern systems. In general, it isB.V. defined as unused capacity or production potential and can be measured 2351-9789 ©production 2017 The Authors. Published by Elsevier 2351-9789 2017responsibility The Authors. Published by Elsevier B.V.hours Peer-review of the scientific committee of the Manufacturing Engineering Conference in several©under ways: tons of production, available of manufacturing, etc.Society The International management of the 2017. idle capacity Peer-review underTel.: responsibility the761; scientific committee the Manufacturing Engineering Society International Conference 2017. * Paulo Afonso. +351 253 of 510 fax: +351 253 604of741 E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. 10.1016/j.promfg.2017.09.189
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by selective laser melting of metallic powders [3]. The recently improvements in welding have made it possible to replace the power source from laser or electron beam with a multi-choice welding source, and normally these sources allow to download all welding information to control the process. In the Wire Arc Additive Manufacturing (WAAM) process, 3D metallic components are built by depositing beads of weld metal layer by layer, using welding processes such as Gas Metal Arc Welding (GMAW) combined with a positioning system as a CNC milling machine or a Robot [4]. On the one hand, the main advantage of WAAM processes are the high deposition rate that allows higher speed of manufacturing, being more suitable to produce large size components. On the other hand, the accuracy of laser or electron beam is higher as have been proposed by Santos, Wanjara et al, but in most cases will be necessary to reprocess the components because of the design surface would need a functional surface or accuracy geometry [5][6]. However, this method of fabrication allows to produce metal parts directly from computer CAD data or CAM systems without using moulds [7]. With the actual and forecast aircraft aerospace market expansion rate, the demand for aluminium and titanium parts is increasing. Traditional methods produce these parts by machining processes, which produce a lot of debris or waste resulting from machining. Keeping in mind that the initial volume of the workpiece (stock) is around 10, or even 20, times more than the finished component, and that these materials are so expensive, in the aircraft industry there is the pressing need to develop a process to replace the machining process [8]. Another important aspect to consider is that rapid prototyping will be a low cost solution in the manufacturing industry to test the new designs without need of specific tools. When creating a new design, it may be necessary to produce several prototypes until the design is working properly. [9]. With traditional manufacturing processes the parts often contain more material and weight than actually needed to safely support the design loads. For this reason, additive manufacturing processes are a new step to develop more efficient components [10]. 2. Experimental methodology Previous works using GMAW welding as additive manufacturing have shown that results are directly related with the materials and equipment used. In order to achieve a good system performance, the shape and the dimensions of the weld bead are the main parameters to be considered [11]. 2.1 Materials and equipment Two different materials have been considered in this experimental work. A solid steel welding wire with 0.8 mm of diameter has been the material selected as the feedstock material in the welding system (commercial name: AWS ER70S-6). S235JR steel plates have been used as substrate to perform the experiments. The samples dimensions were designed according the fixture device employed, as can be shown in Fig. 1. 2.1. Materials and equipment Two different materials have been considered in this experimental work. A solid steel welding wire with 0.8 mm of diameter has been the material selected as the feedstock material in the welding system (commercial name: AWS ER70S-6). S235JR steel plates have been used as substrate to perform the experiments. The samples dimensions were designed according the fixture device employed, as can be shown in Fig.1.
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Fig. 1. (a) first picture; (b) second picture.
According to ISO 14175, a mixture composed of CO2 (15%) and Argon (85%) has been used as protecting gas. The results of using the mixture gas are: stability in the process, improvement in the surface finishing quality and reduction of the splatters. The less amount of CO2, the smaller the welding drops. The experimental equipment used to carry out the welding deposition is composed by the integration of two different systems: Welding system. The welding machine employed was a Fronius TPS 4000 CMT R machine that allows to weld using Cold Metal Transfer technology, patented by Fronius®. The basic principle of this technology is the intensity and voltage control during the deposition; the results are the reduction of welding temperature and the wire movement optimization (Fig. 2). Consequently, a better quality of weld beads were obtained than those made using conventional GMAW welding. Positioning system. According to the main objective, the movement of the whole system should be easily controlled. A BF 30 Vario Optimum CNC milling machine has been adapted to place the torch welding in the Z Axis. The movement of th X-Y table of the CNC system enables a layer of welding deposition in a fixed Z level, being the welding torch fixed to the milling head. When the next layer has to be deposited, the Z axis elevates the torch and the deposition can start again with the X-Y table movement. An auxiliary working table has been necessary to isolate electrically both systems and obtain a little source of cooling. The experimental equipment described in this section is shown in the Fig. 2.
Fig. 2. (a) Wire movement during the welding process using CMT technology; (b) Experimental equipment.
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2.2. Experimental Design Several experiments have been designed in order to study the best process conditions. The welding machine selected allows a very configurable set up, thus a first elemental experiment had been developed to know the relation between the input parameters. To start with, certain tests have been performed and the main variable parameters to take into account have been determined: Intensity (A). It is the necessary current that the machine has to supply in order to achieve the feeding speed programed. The intensity range of this welding machine is 34 A-300 A. Welding speed (mm/min). It is the CNC machine speed. It is directly programed in the CNC code and each one of the axis can have different speeds. Arc length correction (%). It is set between -30% and 30%. Negative values mean smaller arc length while positive values mean bigger arc length. Dynamic correction. The operating range is between -5 and 5. Negative values are those relating to the lower force of weld drop detachment and positive values are those ones corresponding to the greater forces of detachment. This parameter is also called weld drop detachment correction. Considering these parameters and their corresponding ranges, it has been programed the first essay to obtain the best input parameters. Eight series of weld beads have been designed. The output parameters to measure, defined to stablish the optimal conditions, are the weld bead height (H) and the weld bead width (w). A Nikon MSZ800N stereoscopic microscope and a DSIF2 photomicrography camera have been used to measure the weld bead geometry. Three sections of each experiment have been analysed and the arithmetic average has been the representative datum used (Fig. 3).
Fig. 3. Sample with the weld beads deposited and weld bead geometrical analysis.
The next step to be able of fabricating complex pieces is to investigate how the system behaviour is when a weld bead is deposited over the first one. In this case, taken into account the first experiment results, the input parameters used are the followings:
Arc length correction: 0%. Dynamic correction: 0. Welding speed: variable between 200 mm/min and 800 mm/min. Intensity: variable between 27 A and 100 A. Number of layers (n): variable between 10 layers and 40 layers.
The trajectories programming has been made according to the geometrical measurements studied in the first essay. As can be shown in the next section, it has been necessary to adapt the height of each layer depending on the number of layers deposited, so the weld bead output parameters are not precise in order to perform the n-layers wall essay.
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The measurements stablished to study which configuration of input parameters has the better results are divided into two groups: Geometrical measurements, shown in Fig. 4. Taken with a Mitutoyo Coordinating Measuring Machine and with the microscope used in the first essay, considering different sections of the wall. o Wall width, X (mm). o Wall height, Z (mm). o Growth per layer, Zp (mm). It is the wall height (Z) divided by the layers deposited (n). o Angle between substrate and wall, α (º). o Height deviation (mm). In order to analyse the height variation, ten wall heights measurements have been made with the CMM. The mean and the maximum deviation have been calculated as the representative parameters.
Fig. 4. Geometrical measurements.
Topographical measurements. It has been used a 3D perfilometry microscope (Alicona InfiniteFocus SL) with variable focus for the lateral surfaces characterization (Fig. 5). The microscope provides a stereolithographic file (STL) which will be processed to obtain the most outstanding height parameters collected in ISO 25178. o Sa arithmetical mean height of the surface. o Ssk (skewness of height distribution) represents the surface symmetry. o Sku (kurtosis of height distribution) shows the existence of valleys or peaks if Sku>3. When Sku<3 it means there are neither remarkable valleys nor peaks. o Vvc Volume of void in the core or kernel, between two material ratios p and q (in %), calculated in the zone between c1 and c2.
Fig. 5. Surface wall characterization.
Furthermore, a histogram that indicates the density distribution of the data is combining with the roughness profile, as can be shown in Fig. 5. A global vision of the surface topography is obtained attending the shape of this curve.
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3. Results and discussion. With the material selected, the optimal conditions to make the first layer have been obtained. The geometrical shape of the defined wall has also been predicted, and with these dates have been allowed to do walls. The measurements of the walls had been made with Mitutoyo and the obtained measurements have been the height and the width (Fig. 6). With all this information, the layer height has been stablished to do simples geometries, such as prismatic and cylindrical parts.
Fig. 6. Image weld sean.
The material contraction and the regularity of the surface during the welding process were difficult to predict, so that weld seam pilling couldn’t be completely controlled. Due to the above, an oversized part was programed to compensate this contractions and deviations. With the Nikon SMZ800 microscope it was checked that the perpendicularity of the wall is near to 90 degrees, but in the most of the cases with a deviation between 3 to 4 degrees in the base (Fig. 7).
Fig. 7 Measurements in Nikon SMZ800.
On the one hand, the total height of the weld seam layers can be predicted from the obtained model base on the first experiments. But the obtained results show that the height of the created wall could be slightly different from the theoretical height. Predict the real growing can be representative to develop complex geometries and functional parts. As it can be observed in Fig. 8, both height and width increase with the weld intensity. The performance of the system to control the parameters and ideal deposition rate can be predicted with these experiments.
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Fig. 8. Comparison between height and width front Intensity of welding.
On the other hand, when the wall is obtained by more than 20 layers, the growing up of the height is smaller than the predicted one with the 10 layers model, probably because of the warm up. As it can be note in Fig. 9, for the 10 layers experiment (I=40A), the growth per layer, Zp, is about 1,0 mm (height=10mm) and for the 40 layers experiment (I=40A), the growth per layer is about 0,9 mm (height=36mm). This growing of the wall has to be considered to create walls over 20 layers, and not only for height but also for width.
Fig. 9. Analysis width and height versus number of layers.
The Zp is a parameter to take into account when a part is created with an additive manufacturing process. In welding process the gap of the torch should be between 7 to 10mm over the last layer, and if this gap is not the good one, several erros could be occur during the welding process. In relation to topographic measurements, after applying the adecuate filter, the roughness surface has been obtained. The behavior of the parameter Sz reflects that there are no significant variability versus the intensity of the welding process. The assimetry parameter Ssk decrease slightly with the intensity (A), as it can be shown in Fig. 10. The negative signal of Ssk presents more valleys than peaks.
Fig. 10. a) Sz versus Intensity; b) Ssk versus intensity.
The Fig. 11 shows the wall topography of sample 3 (7mm x 20 mm), correponding to an intensity of 65A. It could be noticed the several layers (20) of the construction of the wall.
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µm 200
ISO 25178 150
100
50
Height Parameters Ssk
-0.862
Sku
5.44
Sz
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Sa
18.7
µm
Functional Parameters (V… Vvc
0.0261
mm³/mm²
0
Fig. 11. a) 3D topography of sample 3: b) Parameters sample 3.
4. Conclusions In conclusion, additive manufacturing of metals has been made with a non-expensive system based on the integration of CMT welding technology and CNC milling machine. It’s a smart cost solution for developing and testing new components in comparison with only machining process, laser or electron beam technologies. Furthermore, in relation to the topographic measurements, Sz could be consider an interesting parameter in this process in order to know the oversized amount of machining. Finally, in this work an oversized piece has been produced in order to do a post-machining required to obtain most accuracy in our designed part. A machining process after deposition will be included in future experiments in order to improve the geometrical accuracy. Acknowledgements This work has been developed within the framework of the doctorate program in Industrial Technologies of the UNED and it has been financially supported by the funds provided through the program “Innova” of the Galician Department of Education (Spain), in collaboration with the company Ucalsa S.A. The authors would appreciate the support and advices of all the staff of the department of manufacturing of the polytechnic institute of Vigo. References [1] X. Yan, P. Gu, CAD Comput. Aided Des. 28 (1996) (4) 307–318. [2] ASTM International, F2792-12a - Standard Terminology for Additive Manufacturing Technologies, Rapid Manuf. Assoc., pp. 10–12, 2013. [3] J. Xiong, G. Zhang, H. Gao, L. Wu, Robot. Comput. Integr. Manuf. 29 (2013) (2) 417–423. [4] J. Ding et al, Comput. Mater. Sci. 50 (2011) (12) 3315–3322. [5] E. C. Santos, M. Shiomi, K. Osakada, T. Laoui, Int. J. Mach. Tools Manuf. 46 (2006) (12–13) 1459–1468. [6] P. Wanjara, M. Brochu, M. Jahazi, Mater. Des. 28 (2007) (8) 2278–2286. [7] Y. Cao, S. Zhu, X. Liang, W. Wang, Robot. Comput. Integr. Manuf. 27 (2011) (3) 641–645. [8] S. W. Williams, F. Martina, A. C. Addison, J. Ding, G. Pardal, P. Colegrove, Mater. Sci. Technol. 836 (2015) 1743284715Y.000. [9] K. G. Cooper, Rapid Prototyping Technology Selection and Application, 2001. [10] P. Edwards, A. O’Conner, M. Ramulu, J. Manuf. Sci. Eng. 135 (2013) (6) 61016. [11] S. Akula, K. P. Karunakaran, Robot. Comput. Integr. Manuf. 22 (2006) (2) 113–123.