- Email: [email protected]
Process monitoring of meltpool and spatter for temporal-spatial modeling of laser powder bed fusion process
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Procedia CIRP 00 (2018) 000–000
www.elsevier.com/locate/procedia
Procedia CIRP 00 (2017) 000–000 Procedia CIRP 74 (2018) 102–106 www.elsevier.com/locate/procedia
10th CIRP 10th CIRPConference Conference on on Photonic Photonic Technologies Technologies [LANE [LANE 2018] 2018]
Process monitoring meltpool and spatter modeling of 28thofCIRP Design Conference, May for 2018,temporal-spatial Nantes, France laser powder bed fusion process A new methodology to analyze the functional and physical architecture of a, a a Tuğrulfor Özel Animek Shaurya , Ayçaproduct Altaya, Liang Yangidentification existing products an*,assembly oriented family a
Manufacturing & Automation Research Laboratory, Department of Industrial and Systems Engineering, Rutgers University, NJ 08854, USA
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. Tel.: +1-848-445-1099; fax: +1-848-445-5467. E-mail address: [email protected]
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France *Abstract Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
L-PBF is an additive manufacturing process which can produce nearly fully dense parts with complex geometry by using laser which follows layer-to-layer scanning on powder material. In-process statistical monitoring techniques are required to detect localize material spatter and Abstract control the meltpool. High speed video imaging provides process insights for identifying meltpool and spatter and can be integrated into process monitoring for L-PBF process. We demonstrate the use of high speed camera videos for in-situ monitoring of L-PBF of nickel alloy In625 today’s business the trend towards more product variety and capability. customization unbroken. Due development, the need of to detect spatterenvironment, and over melting regions to improve the process control Theisquantities that cantobethis measured via in-situ sensing agile andreferred reconfigurable production systems emerged to cope with various products to and product families. To design andimages optimize can be to as process signatures and can represent the source of information detect possible defects. The video areproduction processed systems as well as to analysis choose the optimal productcomponent matches, product needed. for Indeed, most ofthe thehistory knownofmethods aim to statisticalare descriptor identifying pixel intensity for temporal-spatial by using principal analysisanalysis and T2 methods analyze a product one product familyThese on theresults physical Different product families, however, may differ largely in terms of the number and levels through theorprocess monitoring. arelevel. correlated as over melting and spatter regions. nature ofThe components. This fact by impedes anLtd. efficient and article choice under of appropriate product family combinations for the production © 2018 2018 Authors. Published Published Elsevier This is iscomparison an open open access access the CC CC BY-NC-ND BY-NC-ND license © The Authors. by Elsevier Ltd. This an article under the license system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster (http://creativecommons.org/licenses/by-nc-nd/3.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) these productsunder in new assembly oriented product families for the optimization Peer-review under responsibility of the the Bayerisches Bayerisches Laserzentrum GmbH. of existing assembly lines and the creation of future reconfigurable Peer-review responsibility of Laserzentrum GmbH. assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a Keywords: functionalPowder analysis performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the bedisfusion; Meltpool; Spatter; Laser; Monitoring 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. 1. Introduction and measurement scales needed to broadly survey the surface © 2017 The Authors. Published by Elsevier B.V. area [6]. In-process monitoring techniques [6-9] have the Peer-review underdigital responsibility of the scientific committee of the 28th CIRP Design to Conference 2018. measurements of the shape and the The direct manufacturing of highly dense metal potential obtain in-situ
parts using powder bed fusion (PBF) processes also known as laser beam melting is rapidly expanding with applications ranging from aerospace, automotive to biomedical industries [1]. This technology has been employed by end users that are need for rapid part fabrication with many features that can 1.inIntroduction be easily altered or customized. However, there remain some challenges high development energy densities on the thin Due to due the to fast in applied the domain of layer of powder repeatedly [1,2] and resultant thermal communication and an ongoing trend of digitization and gradients [1,3], dynamic region of molten material [1,4], digitalization, manufacturing enterprises are facing important spatter and defect formation [1,2], stress-induced distortions in challenges in today’s market environments: a continuing relatively large parts with high aspect ratios [2], solidified tendency towards reduction of product development times and microstructure In order to obtain process shortened product[4]. lifecycles. In addition, therea isfuller an increasing understanding, microstructure investigation and modeling for demand of customization, being at the same time in a global solidification [4], thermal measurements for meltpool and competition with competitors all over the world. This trend, spatter,isand modelling thermal predictions [5] have been which inducing the for development from macro to micro reported. In addition, characterization of surface quality markets, results in diminished lot sizes due to augmenting presentsvarieties distinct(high-volume challenges caused by irregularities involved product to low-volume production) [1]. Keywords: Assembly; Design method; Family identification
size of the molten region that is dynamically changing due to the inherent nature of laser processing of powder material at high energy densities and continuously spattering particles flying away in the form of fully vaporized plume or molten liquid melt bubbles falling down and re-adhering to of the product range that and are characteristics manufactured and/or the solidified surfaces creating pores, inclusions, and voids assembled in this system. In this context, the main challenge in during re-solidification. Among the toin-situ monitoring modelling and analysis is now not only cope with single techniques, the images and videos obtained with high frame products, a limited product range or existing product families, rate cameras are good data sources that can be effectively used but also to be able to analyze and to compare products to define in controlling the LPBF processes [8,9]. This paper provides new product families. It can be observed that classical existing investigations utilize high frame rate camera videos that are product familiestoare regrouped in function of clients or features. processed to portray the process signatures of meltpool and However, assembly oriented product families are hardly to find. spatter with statistical analysis to gain deeper insight on On the product family level, products differ mainly in two processing in LPBF. 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 To cope with this augmenting variety as well as to be able to product structure on a physical level (components level) which 2212-8271 possible © 2018 Theoptimization Authors. Published by Elsevier is an opencauses access article under theregarding CC BY-NC-ND license identify potentials in Ltd. the This existing difficulties an efficient definition and (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 Bayerisches Laserzentrum GmbH.
2212-8271 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2212-8271 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of scientific the Bayerisches Laserzentrum GmbH. Peer-review under responsibility of the committee of the 28th CIRP Design Conference 2018. 10.1016/j.procir.2018.08.049
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Tuğrul Özel et al. / Procedia CIRP 74 (2018) 102–106 Author name / Procedia CIRP 00 (2018) 000–000
2. Experimental In the experiments, square layers with 256 mm2 surface area have been processed in powder bed (Inconel 625 material with 35 m average particle size) using EOS M270 type LPBF machine in Nitrogen filled glove with a 200 W power and 100 m beam spot size Yb-fiber laser at a laser energy density level of E = 113.75 J/mm3 (laser power of P = 182 W, scan velocity of vs = 800 mm/s, hatch distance of h = 0.10 mm) on a stainless steel build platform which was heated up to 80 °C. Each layer was 20 m thick and layer-to-layer rotation of the stripe pattern was orthogonal (90°). Laser scanning that provided melting of the powder layer was executed by following of hatching from the beginning to the end of 4 mm wide stripes and alternating direction in a serpentine manner. Each stripe consists of multiple fused tracks, separated by a hatch distance, and each track is processed with the laser beam moving with a constant scan velocity. There existed a “laser-off condition” after a track is completed by the movement of the laser beam in one direction for approximately 0.042 ms, during which time, scanning mirrors are aligned to scan the next unprocessed track, and turns on again to move the beam in the opposite direction of the previous track (see Fig. 1).
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(IFR 1,800 fps at 360 128 pixels) and thermography (filter with wavelength of 1350 nm to 1600nm) were used for performing an in-situ thermal monitoring to quantitatively analyse meltpool size and understand spattering behaviour and to plot them. The camera has an integration time of 0.040 ms and can record at 1800 frames per second which translates into 0.5555 ms per frame. In the instantaneous field of view (iFoV), each pixel represents 36 μm and the camera is angled at 43.7 with 150-mm distance. It is important to note that for emissivity value of ε=0.2 used, only temperatures from 600 °C to 1380 °C can be very reliably calculated by the camera [3]. Temperatures outside this range should be treated with caution. Three frames along a track (beginning, middle and end) from the thermal video recording are shown in Fig. 3.
Fig. 2. High speed camera orientation (a), camera angle (b) [3].
Fig. 1. Progression of parallel tracks along a stipe during LPBF.
Experimental investigations of the LPBF process with effects on melt pool geometry (size and shape) in multi-track processing are presented in [5]. Laser parameters and layerwise scan strategy have a significant influence on the underlying microstructure of the part [4], which affects the resultant mechanical properties and fatigue life [2]. Previous experimental investigations on microstructure formation in IN625 revealed LPBF process effects on growth directions of columnar grains and sizes of cellular grains [6]. 3. Process monitoring A high-speed camera was used for performing an in-situ process monitoring to quantitatively analyse meltpool size and understand spattering behaviour. This setup was organized at the National Institute of Standards and Technology (NIST) facility in Gaithersburg MD, to observe a portion of the build area of an LPBF machine, and the process has been recorded for a test coupon that was fabricated using P=195 W, vs=800 mm/s, and h=0.10 mm [3-5]. Video recordings of the process were obtained from a thermal and a digital camera which were placed in the process chamber (Fig. 2). A thermal camera
Fig. 3. High speed thermal camera images [3].
Video recordings of the process were obtained from a camera. An HFR camera (Photron) has been utilized (Fig. 4). The camera characteristics included; integration times of 0.1 ms - 0.5 ms, frame rates of 2,000 frames/s, 10,000 frames/s, 24,000 frames/s, and imaging windows of 512 pixels x 128 pixels, and 1024 pixel x 256 pixel.
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number of acquired frames from j=1 to J and (M N) is the size in pixel of each frame. In other words, (U1, U2 …. Uj) represents stream of images where Uj ∈ RMXN is the jth image of size (M N) and j= 1 to J. The vectorised principal component analysis (PCA) is the most common way of applying PCA techniques to image data, which involves transformation of bi-dimensional samples (i.e. frames) into one dimensional vectors. This conversion process is also known as ‘unfolding’ operation. There are two types of PCA techniques which are used in this paper; (i) Temporal PCA, and (ii) Spatial PCA. 4.1. Temporal analysis of high frame rate camera videos
Fig. 4. High frame rate camera images.
Two different types of meltpool observed during the LPBF process [3]. Type-I meltpool, meltpool area being processed is still within the heat-affected zone of the previous hatch scanning (or track processing) and Type-II meltpool, meltpool area currently being processed is no longer affected by the heat from laser scanning of the previous track or hatch [3]. 4. Statistical process analysis The quality of parts fabricated with LPBF is influenced by powder quality and process parameters. Statistical quality control methods combined with inspection solutions and multi-stream sensors can be employed for process monitoring and control for zero-defect additive manufacturing [6-9]. The quantities that can be measured via in-process monitoring can be referred to as “process signatures”, and can represent the source of information to detect possible defects [8,9]. For instance, use of meltpool and spatter monitoring for achieving desired quality using feedback control system in LPBF processing of metal parts was proposed by Grasso et al. [7]. This section involves the statistical analysis of LPBF system monitored with a high speed camera. The goal is to be able to define statistically spatter and overmelting, to predict the time and the location of overmelting and spatters. In-situ monitoring of overmelting and spatter involves exploration of the relationship to them with process parameters. High energy causes more frequent and larger spatters and “normal melting” can be related to a target energy density [9]; any value lower than that is accepted as “under melting” and any value greater than that is accepted as “overmelting” at a discrete level. However, with the same parameters, different melting structures can also be obtained. Additionally, small deviations still may count as “normal” and the melting process is continuous. Using data collected from the thermal and HFR camera videos, correlation analysis and statistical process control (SPC) charts needs to be established. For this purpose, each video file is used to obtain stream of image frames for carrying out statistical methods to study spatter analysis and over melting phenomenon. According to Grasso et al. [7] the videos acquired from the experimental setup consists of image streams which is represented as a three-dimensional array 𝓤𝓤 𝓤 𝓤����� , where J is the total
Temporal PCA will be performed on the stream of images 𝓤𝓤 � �𝑼𝑼𝟏𝟏 , 𝑼𝑼𝟐𝟐 , … , 𝑼𝑼𝟑𝟑 � . Three-dimensional array i.e. 𝓤𝓤 𝓤 ℝ����� is transformed into a matrix ∈ℝ��� , where p = M N. Each row of matrix in here consists of a pixel intensity profile. Principal components generated through vectorised PCA associates a weight to each frame. Spectral decomposition of the variance covariance matrix ∈ℝ��� , of the p J data matrix is then done which is given by VTSV=L. Extracted PCs are linear combination of frames from j =1 to J. The relative importance of each PC i.e. the amount of explained variance is represented by the value of the corresponding eigenvalue. Reduced number of PCs is used to represent the relevant information without any loss. Then a graph for mean gray level values vs the frames from j= 1 to J in the video file is plotted (see Fig. 5).
Fig. 5. Temporal PCA with meltpool area as monitored.
Temporal PCA were conducted after “laser-off condition” image frames were removed from the videos and the Type-I and Type-II tracks were separated. Figs. 6 and Fig. 7 are results obtained after performing a statistical analysis of TypeI and Type-II tracks which includes calculation of mean and standard deviation of the mean gray level values as the laser processes each track. It can be inferred from these statistical graphs that the process goes out of control for the range between µ+σ and µ-σ whereas it remains within the limit for a range between µ+2σ and µ-2σ. Particularly, meltpool variations intensify at the beginning of Type I meltpools (Fig.6) and at the end of Type II meltpools (Fig.7).
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Tuğrul Özel et al. / Procedia CIRP 74 (2018) 102–106 Author name / Procedia CIRP 00 (2018) 000–000
Fig. 6. Temporal PCA monitored during Type-I meltpool processing.
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Fig. 9. Spatial mapping for primary and secondary spikes for spatter monitored during multi-track Type-I meltpool processing.
Fig. 7. Temporal PCA monitored during Type-I meltpool processing.
4.2. Spatial analysis of high frame rate camera videos In Spatial PCA the initial steps remain the same as temporal PCA. Transformation of a three-dimensional array i.e. 𝓤𝓤 𝓤 𝓤������� is transformed into a matrix 𝑿𝑿 𝑿𝑿��� , where p = M N. Here each row of the matrix consists of a vectorised frame. Principal components generated through VPCA associates a weight to each pixel. Spatial mapping will be applied to the data. A statistical descriptor based upon Hotelling’s T2 distance which is a spatial index i.e. a function T2 (X, Y) of pixel location within the image which maps a T2 value to each pixel is added. Then a three-dimensional map is plotted using this T2 values against the pixel location. T2 values are based upon all principal components but it can be restricted to a few ones which contribute most to the video without loss of any data. The resultant spatial mapping is given as shown in Fig.8. According to Grasso et al. [7,8] higher T2 statistic value represents overmelting occurring during the process. The magnitude of T2 statistic for Type-I tracks (Fig. 9) ranges from 0 to 2.5 whereas for Type-II tracks (Fig. 10) it ranges from 0 to 2, which states the fact that overmelting occurs more frequently while processing of Type-I tracks compared to Type-II tracks.
Fig. 8. Spatial mapping (T2 statistical descriptor) as spatter monitored (without laser-off conditions).
Fig. 10. Spatial mapping for primary and secondary spikes for spatter monitored during multi-track Type-II meltpool processing.
Such high values are usually occurring since these areas are characterized by an intensity profile that is mainly different from the underlying pattern that describes the image stream. There are more than one spikes getting formed as the laser moves on from one track to another. These different types of spikes formed during the spatial distribution can be categorized into primary spikes and secondary spikes. Occurrence of more number of secondary spikes along with the primary spikes represents spatter occurring during the processing. Type-I tracks show higher number of secondary spikes formed in the spatial distribution of compared to TypeII tracks. This states the fact that spattering defects are more consistent during processing of Type-I tracks rather than Type-II tracks. 5. Conclusions Temporal principal component analysis provides monitoring of meltpool area and spatial vectorised principal component analysis provides monitoring of spattered location during LPBF process. A statistical and definition of overmelting have been provided. A temporal analysis model has been given to predict the location and the time of the next overmelting. By utilizing such statistical analysis, a stochastic model can be formed to analyse and infer the occurrence and size of spatters. Using data collected, correlation analysis and SPC charts can be established which present a useful tool that can provide LPBF users with the mean of identifying possible changes in the process. Therefore, it can be used for process monitoring to ensure consistency in part quality for long term
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production. Acknowledgements The prior support by the US-DOC NIST and the collaboration with A. Donmez, B. Lane, S. Moylan is greatly appreciated. References [1] Khairallah SA, Anderson AT, Rubenchik A, King WE (2016) Laser powder-bed fusion additive manufacturing. Acta Mater 108:36–45. [2] Dunbar AJ, Denlinger ER, Gouge MF, Simpson TW, Michaleris P (2017) Comparisons of laser powder bed fusion additive manufacturing builds through experimental in situ distortion and temperature measurements. Add Manu 15:57–65. [3] Criales LE, Arisoy YM, Lane B, Moylan S, Donmez A, Özel T (2017) Laser powder bed fusion of nickel alloy 625: experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis. Int J Mach Tool Manu 121:22–36.
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[4] Arisoy YM, Criales LE, Özel T, Lane B, Moylan S, Donmez A (2017) Influence of scan strategy and process parameters on microstructure and its optimization in additively manufactured nickel alloy 625 via laser powder bed fusion. Int J Adv Manuf Tech 90(5–8):1393–1417. [5] Criales LE, Arisoy YM, Lane B, Moylan S, Donmez A, Özel T (2017) Predictive modeling and optimization of multi-track processing for laser powder bed fusion of nickel alloy 625. Add Manu 13:14-36. [6] Everton SK, Hirsch M, Stravroulakis P, Leach RK, Clare AT (2016) Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Mater Design 95:431-445. [7] Grasso M, Colosimo BM (2017) Process defects and in situ monitoring methods in metal powder bed fusion: a review. Meas Sci Technol 28 (4), 044005. [8] Grasso M, Laguzza V, Semeraro Q, Colosimo BM (2016) In-process monitoring of selective laser melting: spatial detection of defects via image data analysis. J Manuf Sci E-T ASME, 139 (5), 051001: -1-16. [9] Repossini G, Laguzza V, Grasso M, Colosimo BM (2017) On the use of spatter signature for in-situ monitoring of laser powder bed fusion, Add Manu 16: 35–48.
ScienceDirect
Available online atonline www.sciencedirect.com Available at www.sciencedirect.com
ScienceDirect ScienceDirect
Procedia CIRP 00 (2018) 000–000
www.elsevier.com/locate/procedia
Procedia CIRP 00 (2017) 000–000 Procedia CIRP 74 (2018) 102–106 www.elsevier.com/locate/procedia
10th CIRP 10th CIRPConference Conference on on Photonic Photonic Technologies Technologies [LANE [LANE 2018] 2018]
Process monitoring meltpool and spatter modeling of 28thofCIRP Design Conference, May for 2018,temporal-spatial Nantes, France laser powder bed fusion process A new methodology to analyze the functional and physical architecture of a, a a Tuğrulfor Özel Animek Shaurya , Ayçaproduct Altaya, Liang Yangidentification existing products an*,assembly oriented family a
Manufacturing & Automation Research Laboratory, Department of Industrial and Systems Engineering, Rutgers University, NJ 08854, USA
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. Tel.: +1-848-445-1099; fax: +1-848-445-5467. E-mail address: [email protected]
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France *Abstract Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
L-PBF is an additive manufacturing process which can produce nearly fully dense parts with complex geometry by using laser which follows layer-to-layer scanning on powder material. In-process statistical monitoring techniques are required to detect localize material spatter and Abstract control the meltpool. High speed video imaging provides process insights for identifying meltpool and spatter and can be integrated into process monitoring for L-PBF process. We demonstrate the use of high speed camera videos for in-situ monitoring of L-PBF of nickel alloy In625 today’s business the trend towards more product variety and capability. customization unbroken. Due development, the need of to detect spatterenvironment, and over melting regions to improve the process control Theisquantities that cantobethis measured via in-situ sensing agile andreferred reconfigurable production systems emerged to cope with various products to and product families. To design andimages optimize can be to as process signatures and can represent the source of information detect possible defects. The video areproduction processed systems as well as to analysis choose the optimal productcomponent matches, product needed. for Indeed, most ofthe thehistory knownofmethods aim to statisticalare descriptor identifying pixel intensity for temporal-spatial by using principal analysisanalysis and T2 methods analyze a product one product familyThese on theresults physical Different product families, however, may differ largely in terms of the number and levels through theorprocess monitoring. arelevel. correlated as over melting and spatter regions. nature ofThe components. This fact by impedes anLtd. efficient and article choice under of appropriate product family combinations for the production © 2018 2018 Authors. Published Published Elsevier This is iscomparison an open open access access the CC CC BY-NC-ND BY-NC-ND license © The Authors. by Elsevier Ltd. This an article under the license system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster (http://creativecommons.org/licenses/by-nc-nd/3.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) these productsunder in new assembly oriented product families for the optimization Peer-review under responsibility of the the Bayerisches Bayerisches Laserzentrum GmbH. of existing assembly lines and the creation of future reconfigurable Peer-review responsibility of Laserzentrum GmbH. assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a Keywords: functionalPowder analysis performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the bedisfusion; Meltpool; Spatter; Laser; Monitoring 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. 1. Introduction and measurement scales needed to broadly survey the surface © 2017 The Authors. Published by Elsevier B.V. area [6]. In-process monitoring techniques [6-9] have the Peer-review underdigital responsibility of the scientific committee of the 28th CIRP Design to Conference 2018. measurements of the shape and the The direct manufacturing of highly dense metal potential obtain in-situ
parts using powder bed fusion (PBF) processes also known as laser beam melting is rapidly expanding with applications ranging from aerospace, automotive to biomedical industries [1]. This technology has been employed by end users that are need for rapid part fabrication with many features that can 1.inIntroduction be easily altered or customized. However, there remain some challenges high development energy densities on the thin Due to due the to fast in applied the domain of layer of powder repeatedly [1,2] and resultant thermal communication and an ongoing trend of digitization and gradients [1,3], dynamic region of molten material [1,4], digitalization, manufacturing enterprises are facing important spatter and defect formation [1,2], stress-induced distortions in challenges in today’s market environments: a continuing relatively large parts with high aspect ratios [2], solidified tendency towards reduction of product development times and microstructure In order to obtain process shortened product[4]. lifecycles. In addition, therea isfuller an increasing understanding, microstructure investigation and modeling for demand of customization, being at the same time in a global solidification [4], thermal measurements for meltpool and competition with competitors all over the world. This trend, spatter,isand modelling thermal predictions [5] have been which inducing the for development from macro to micro reported. In addition, characterization of surface quality markets, results in diminished lot sizes due to augmenting presentsvarieties distinct(high-volume challenges caused by irregularities involved product to low-volume production) [1]. Keywords: Assembly; Design method; Family identification
size of the molten region that is dynamically changing due to the inherent nature of laser processing of powder material at high energy densities and continuously spattering particles flying away in the form of fully vaporized plume or molten liquid melt bubbles falling down and re-adhering to of the product range that and are characteristics manufactured and/or the solidified surfaces creating pores, inclusions, and voids assembled in this system. In this context, the main challenge in during re-solidification. Among the toin-situ monitoring modelling and analysis is now not only cope with single techniques, the images and videos obtained with high frame products, a limited product range or existing product families, rate cameras are good data sources that can be effectively used but also to be able to analyze and to compare products to define in controlling the LPBF processes [8,9]. This paper provides new product families. It can be observed that classical existing investigations utilize high frame rate camera videos that are product familiestoare regrouped in function of clients or features. processed to portray the process signatures of meltpool and However, assembly oriented product families are hardly to find. spatter with statistical analysis to gain deeper insight on On the product family level, products differ mainly in two processing in LPBF. 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 To cope with this augmenting variety as well as to be able to product structure on a physical level (components level) which 2212-8271 possible © 2018 Theoptimization Authors. Published by Elsevier is an opencauses access article under theregarding CC BY-NC-ND license identify potentials in Ltd. the This existing difficulties an efficient definition and (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 Bayerisches Laserzentrum GmbH.
2212-8271 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2212-8271 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of scientific the Bayerisches Laserzentrum GmbH. Peer-review under responsibility of the committee of the 28th CIRP Design Conference 2018. 10.1016/j.procir.2018.08.049
2
Tuğrul Özel et al. / Procedia CIRP 74 (2018) 102–106 Author name / Procedia CIRP 00 (2018) 000–000
2. Experimental In the experiments, square layers with 256 mm2 surface area have been processed in powder bed (Inconel 625 material with 35 m average particle size) using EOS M270 type LPBF machine in Nitrogen filled glove with a 200 W power and 100 m beam spot size Yb-fiber laser at a laser energy density level of E = 113.75 J/mm3 (laser power of P = 182 W, scan velocity of vs = 800 mm/s, hatch distance of h = 0.10 mm) on a stainless steel build platform which was heated up to 80 °C. Each layer was 20 m thick and layer-to-layer rotation of the stripe pattern was orthogonal (90°). Laser scanning that provided melting of the powder layer was executed by following of hatching from the beginning to the end of 4 mm wide stripes and alternating direction in a serpentine manner. Each stripe consists of multiple fused tracks, separated by a hatch distance, and each track is processed with the laser beam moving with a constant scan velocity. There existed a “laser-off condition” after a track is completed by the movement of the laser beam in one direction for approximately 0.042 ms, during which time, scanning mirrors are aligned to scan the next unprocessed track, and turns on again to move the beam in the opposite direction of the previous track (see Fig. 1).
103
(IFR 1,800 fps at 360 128 pixels) and thermography (filter with wavelength of 1350 nm to 1600nm) were used for performing an in-situ thermal monitoring to quantitatively analyse meltpool size and understand spattering behaviour and to plot them. The camera has an integration time of 0.040 ms and can record at 1800 frames per second which translates into 0.5555 ms per frame. In the instantaneous field of view (iFoV), each pixel represents 36 μm and the camera is angled at 43.7 with 150-mm distance. It is important to note that for emissivity value of ε=0.2 used, only temperatures from 600 °C to 1380 °C can be very reliably calculated by the camera [3]. Temperatures outside this range should be treated with caution. Three frames along a track (beginning, middle and end) from the thermal video recording are shown in Fig. 3.
Fig. 2. High speed camera orientation (a), camera angle (b) [3].
Fig. 1. Progression of parallel tracks along a stipe during LPBF.
Experimental investigations of the LPBF process with effects on melt pool geometry (size and shape) in multi-track processing are presented in [5]. Laser parameters and layerwise scan strategy have a significant influence on the underlying microstructure of the part [4], which affects the resultant mechanical properties and fatigue life [2]. Previous experimental investigations on microstructure formation in IN625 revealed LPBF process effects on growth directions of columnar grains and sizes of cellular grains [6]. 3. Process monitoring A high-speed camera was used for performing an in-situ process monitoring to quantitatively analyse meltpool size and understand spattering behaviour. This setup was organized at the National Institute of Standards and Technology (NIST) facility in Gaithersburg MD, to observe a portion of the build area of an LPBF machine, and the process has been recorded for a test coupon that was fabricated using P=195 W, vs=800 mm/s, and h=0.10 mm [3-5]. Video recordings of the process were obtained from a thermal and a digital camera which were placed in the process chamber (Fig. 2). A thermal camera
Fig. 3. High speed thermal camera images [3].
Video recordings of the process were obtained from a camera. An HFR camera (Photron) has been utilized (Fig. 4). The camera characteristics included; integration times of 0.1 ms - 0.5 ms, frame rates of 2,000 frames/s, 10,000 frames/s, 24,000 frames/s, and imaging windows of 512 pixels x 128 pixels, and 1024 pixel x 256 pixel.
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number of acquired frames from j=1 to J and (M N) is the size in pixel of each frame. In other words, (U1, U2 …. Uj) represents stream of images where Uj ∈ RMXN is the jth image of size (M N) and j= 1 to J. The vectorised principal component analysis (PCA) is the most common way of applying PCA techniques to image data, which involves transformation of bi-dimensional samples (i.e. frames) into one dimensional vectors. This conversion process is also known as ‘unfolding’ operation. There are two types of PCA techniques which are used in this paper; (i) Temporal PCA, and (ii) Spatial PCA. 4.1. Temporal analysis of high frame rate camera videos
Fig. 4. High frame rate camera images.
Two different types of meltpool observed during the LPBF process [3]. Type-I meltpool, meltpool area being processed is still within the heat-affected zone of the previous hatch scanning (or track processing) and Type-II meltpool, meltpool area currently being processed is no longer affected by the heat from laser scanning of the previous track or hatch [3]. 4. Statistical process analysis The quality of parts fabricated with LPBF is influenced by powder quality and process parameters. Statistical quality control methods combined with inspection solutions and multi-stream sensors can be employed for process monitoring and control for zero-defect additive manufacturing [6-9]. The quantities that can be measured via in-process monitoring can be referred to as “process signatures”, and can represent the source of information to detect possible defects [8,9]. For instance, use of meltpool and spatter monitoring for achieving desired quality using feedback control system in LPBF processing of metal parts was proposed by Grasso et al. [7]. This section involves the statistical analysis of LPBF system monitored with a high speed camera. The goal is to be able to define statistically spatter and overmelting, to predict the time and the location of overmelting and spatters. In-situ monitoring of overmelting and spatter involves exploration of the relationship to them with process parameters. High energy causes more frequent and larger spatters and “normal melting” can be related to a target energy density [9]; any value lower than that is accepted as “under melting” and any value greater than that is accepted as “overmelting” at a discrete level. However, with the same parameters, different melting structures can also be obtained. Additionally, small deviations still may count as “normal” and the melting process is continuous. Using data collected from the thermal and HFR camera videos, correlation analysis and statistical process control (SPC) charts needs to be established. For this purpose, each video file is used to obtain stream of image frames for carrying out statistical methods to study spatter analysis and over melting phenomenon. According to Grasso et al. [7] the videos acquired from the experimental setup consists of image streams which is represented as a three-dimensional array 𝓤𝓤 𝓤 𝓤����� , where J is the total
Temporal PCA will be performed on the stream of images 𝓤𝓤 � �𝑼𝑼𝟏𝟏 , 𝑼𝑼𝟐𝟐 , … , 𝑼𝑼𝟑𝟑 � . Three-dimensional array i.e. 𝓤𝓤 𝓤 ℝ����� is transformed into a matrix ∈ℝ��� , where p = M N. Each row of matrix in here consists of a pixel intensity profile. Principal components generated through vectorised PCA associates a weight to each frame. Spectral decomposition of the variance covariance matrix ∈ℝ��� , of the p J data matrix is then done which is given by VTSV=L. Extracted PCs are linear combination of frames from j =1 to J. The relative importance of each PC i.e. the amount of explained variance is represented by the value of the corresponding eigenvalue. Reduced number of PCs is used to represent the relevant information without any loss. Then a graph for mean gray level values vs the frames from j= 1 to J in the video file is plotted (see Fig. 5).
Fig. 5. Temporal PCA with meltpool area as monitored.
Temporal PCA were conducted after “laser-off condition” image frames were removed from the videos and the Type-I and Type-II tracks were separated. Figs. 6 and Fig. 7 are results obtained after performing a statistical analysis of TypeI and Type-II tracks which includes calculation of mean and standard deviation of the mean gray level values as the laser processes each track. It can be inferred from these statistical graphs that the process goes out of control for the range between µ+σ and µ-σ whereas it remains within the limit for a range between µ+2σ and µ-2σ. Particularly, meltpool variations intensify at the beginning of Type I meltpools (Fig.6) and at the end of Type II meltpools (Fig.7).
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Fig. 6. Temporal PCA monitored during Type-I meltpool processing.
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Fig. 9. Spatial mapping for primary and secondary spikes for spatter monitored during multi-track Type-I meltpool processing.
Fig. 7. Temporal PCA monitored during Type-I meltpool processing.
4.2. Spatial analysis of high frame rate camera videos In Spatial PCA the initial steps remain the same as temporal PCA. Transformation of a three-dimensional array i.e. 𝓤𝓤 𝓤 𝓤������� is transformed into a matrix 𝑿𝑿 𝑿𝑿��� , where p = M N. Here each row of the matrix consists of a vectorised frame. Principal components generated through VPCA associates a weight to each pixel. Spatial mapping will be applied to the data. A statistical descriptor based upon Hotelling’s T2 distance which is a spatial index i.e. a function T2 (X, Y) of pixel location within the image which maps a T2 value to each pixel is added. Then a three-dimensional map is plotted using this T2 values against the pixel location. T2 values are based upon all principal components but it can be restricted to a few ones which contribute most to the video without loss of any data. The resultant spatial mapping is given as shown in Fig.8. According to Grasso et al. [7,8] higher T2 statistic value represents overmelting occurring during the process. The magnitude of T2 statistic for Type-I tracks (Fig. 9) ranges from 0 to 2.5 whereas for Type-II tracks (Fig. 10) it ranges from 0 to 2, which states the fact that overmelting occurs more frequently while processing of Type-I tracks compared to Type-II tracks.
Fig. 8. Spatial mapping (T2 statistical descriptor) as spatter monitored (without laser-off conditions).
Fig. 10. Spatial mapping for primary and secondary spikes for spatter monitored during multi-track Type-II meltpool processing.
Such high values are usually occurring since these areas are characterized by an intensity profile that is mainly different from the underlying pattern that describes the image stream. There are more than one spikes getting formed as the laser moves on from one track to another. These different types of spikes formed during the spatial distribution can be categorized into primary spikes and secondary spikes. Occurrence of more number of secondary spikes along with the primary spikes represents spatter occurring during the processing. Type-I tracks show higher number of secondary spikes formed in the spatial distribution of compared to TypeII tracks. This states the fact that spattering defects are more consistent during processing of Type-I tracks rather than Type-II tracks. 5. Conclusions Temporal principal component analysis provides monitoring of meltpool area and spatial vectorised principal component analysis provides monitoring of spattered location during LPBF process. A statistical and definition of overmelting have been provided. A temporal analysis model has been given to predict the location and the time of the next overmelting. By utilizing such statistical analysis, a stochastic model can be formed to analyse and infer the occurrence and size of spatters. Using data collected, correlation analysis and SPC charts can be established which present a useful tool that can provide LPBF users with the mean of identifying possible changes in the process. Therefore, it can be used for process monitoring to ensure consistency in part quality for long term
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production. Acknowledgements The prior support by the US-DOC NIST and the collaboration with A. Donmez, B. Lane, S. Moylan is greatly appreciated. References [1] Khairallah SA, Anderson AT, Rubenchik A, King WE (2016) Laser powder-bed fusion additive manufacturing. Acta Mater 108:36–45. [2] Dunbar AJ, Denlinger ER, Gouge MF, Simpson TW, Michaleris P (2017) Comparisons of laser powder bed fusion additive manufacturing builds through experimental in situ distortion and temperature measurements. Add Manu 15:57–65. [3] Criales LE, Arisoy YM, Lane B, Moylan S, Donmez A, Özel T (2017) Laser powder bed fusion of nickel alloy 625: experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis. Int J Mach Tool Manu 121:22–36.
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[4] Arisoy YM, Criales LE, Özel T, Lane B, Moylan S, Donmez A (2017) Influence of scan strategy and process parameters on microstructure and its optimization in additively manufactured nickel alloy 625 via laser powder bed fusion. Int J Adv Manuf Tech 90(5–8):1393–1417. [5] Criales LE, Arisoy YM, Lane B, Moylan S, Donmez A, Özel T (2017) Predictive modeling and optimization of multi-track processing for laser powder bed fusion of nickel alloy 625. Add Manu 13:14-36. [6] Everton SK, Hirsch M, Stravroulakis P, Leach RK, Clare AT (2016) Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Mater Design 95:431-445. [7] Grasso M, Colosimo BM (2017) Process defects and in situ monitoring methods in metal powder bed fusion: a review. Meas Sci Technol 28 (4), 044005. [8] Grasso M, Laguzza V, Semeraro Q, Colosimo BM (2016) In-process monitoring of selective laser melting: spatial detection of defects via image data analysis. J Manuf Sci E-T ASME, 139 (5), 051001: -1-16. [9] Repossini G, Laguzza V, Grasso M, Colosimo BM (2017) On the use of spatter signature for in-situ monitoring of laser powder bed fusion, Add Manu 16: 35–48.