Modeling of Layer-wise Additive Manufacturing for Part Quality Prediction

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7th International Conference on Through-life Engineering Services 7th International Conference on Through-life Engineering Services

Modeling of Layer-wise Additive Manufacturing for Part Quality Modeling of Layer-wise Additive Manufacturing for Part Quality Prediction Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June Prediction 2017, Vigo (Pontevedra), Spain Jianjing Zhang, Peng Wang, Robert X. Gao* Jianjing Zhang, PengCase Wang, X. Gao* Department of Mechanical and Aerospace Engineering, Western Robert Reserve University, Cleveland, OH, 44106, USA

Costing models for capacity optimization in Industry 4.0: Trade-off Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA between used capacity and operational efficiency Abstract Abstract a a,* b due to part quality Widespread adoption of Additive Manufacturing has remained challenge A. Santana , P.(AM) Afonso , A.aZanin , R. Wernkeb inconsistency. Using Fused Deposition Modeling as a representative AM process, this paper presents a deep learning technique termed Long-Short Term Widespread adoption of Additive Manufacturing (AM) has remained a challenge due to part quality inconsistency. Using Fused a University of Minho, 4800-058 Portugal Memory for quantification of the nonlinear relationship between theGuimarães, printing process and part tensile strength. The presented b Deposition Modeling as a representative AM process, this paper Chapecó, presents SC, a deep learning technique termed Long-Short Term Unochapecó, 89809-000 Brazil modeling method takes into account the layer thermal history associated with the layer-wise printing process as well as the IR Memory for quantification of the nonlinear relationship between the printing process and part tensile strength. The presented sensing data acquired online. Evaluation using Polylactide as the printing material has shown a 46% reduction in prediction error modeling method takes into account the layer thermal history associated with the layer-wise printing process as well as the IR of part tensile strength achieved with the developed modeling method. sensing data acquired online. Evaluation using Polylactide as the printing material has shown a 46% reduction in prediction error of part tensile strength achieved with the developed modeling method. Abstract © 2018 The Authors. Published by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The by B.V. © 2018 The Authors. Authors. Published by Elsevier Elsevier B.V. Under the concept of "Industry 4.0", production processes willInternational be pushedConference to be increasingly interconnected, Peer-review under responsibility the scientific committee the 7th on Through-life Engineering This is an open access article underofthe CC BY-NC-ND licenseof(https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND licensemuch (https://creativecommons.org/licenses/by-nc-nd/4.0/) information based on a real time basis and, necessarily, more efficient. In this context, capacity optimization Peer-review Services. Services. under responsibility of the scientific committee of the 7th International Conference on Through-life Engineering Peer-review responsibility scientific committee ofcontributing the 7th International Conference on Through-life goes beyondunder the traditional aimofofthe capacity maximization, also for organization’s profitability Engineering and value. Services. Indeed, management continuous improvement approaches suggest capacity optimization instead of Keywords:lean Additive Manufacturing;and Process Modeling; Predictive Analytics, Deep Learning

maximization. The study of capacity optimization and costing models is an important research topic that deserves Keywords: Additive Manufacturing; Process Modeling; Predictive Analytics, Deep Learning contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical 1. Introduction model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s 1. Introduction Additive manufacturing (AM) is defined as process ofefficiency joining materials to make objects from 3D model data, value. The trade-off capacity maximization vs “a operational is highlighted and it is shown that capacity usually layermight uponhide layer, as opposed to subtractive manufacturing methodologies” by the American Society for optimization operational inefficiency. Additive manufacturing (AM) is defined as “a process of joining materials to make objects from 3D model data, Testing andAuthors. Materials (ASTM)Elsevier [1]. Seven main categories are defined for different AM processes: binder jetting, © 2017 The B.V. usually layer uponPublished layer, asbyopposed to subtractive manufacturing methodologies” by the American Society for Peer-review under responsibility the scientific committee of the Manufacturing Engineering Society International Conference directed energy deposition, of material extrusion, material jetting, powder bed fusion, sheet lamination and vat Testing and Materials (ASTM) [1]. Seven main categories are defined for different AM processes: binder jetting, 2017. photopolymerization, with various technologies under each category [1]. Since its inception, AM has been identified directed energy deposition, material extrusion, material jetting, powder bed fusion, sheet lamination and vat as having the potentialwith to provide number of advantages forcategory sustainable 1) theAM additive nature of AM photopolymerization, variousa technologies under each [1]. manufacturing: Since its inception, has been identified Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency

as having the potential to provide a number of advantages for sustainable manufacturing: 1) the additive nature of AM

1. Introduction * Corresponding author. Tel.: +1-216-368-6045; fax: +1-216-368-6445.

E-mail address: [email protected] *The Corresponding author. Tel.: +1-216-368-6045; fax: information +1-216-368-6445. cost of idle capacity is a fundamental for companies and their management of extreme importance E-mail address: [email protected] in modern systems. In general, it isB.V. defined as unused capacity or production potential and can be measured 2351-9789 ©production 2018 The Authors. Published by Elsevier This is an open accesstons articleofunder the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) in several ways: production, available hours of manufacturing, etc. The management of the idle capacity 2351-9789 ©under 2018responsibility The Authors. Published by Elsevier B.V. the 7th International Conference on Through-life Engineering Services. Peer-review the761; scientific committee * Paulo Afonso. Tel.: +351 253 of 510 fax: +351 253 604of741 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) E-mail address: [email protected] Peer-review under responsibility of the scientific committee of the 7th International Conference on Through-life Engineering Services. 2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under of the scientificbycommittee the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018responsibility The Authors. Published Elsevier of B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Through-life Engineering Services. 10.1016/j.promfg.2018.10.165

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makes it resource efficient as less material waste (such as powder or resin) is generated as compared to the subtractive methods [2]; 2) AM features superior capability to recycle and reuse material. For example, an estimation of 95 to 98% recyclability for metal powders is reported [3]; 3) AM facilitates the remanufacture/repair processes and therefore, contributes to the product life extension [4]. Furthermore, AM enables the design of more complex components due to its freedom in designing part geometry, making it attractive for rapid prototyping [5-8]. From the perspective of through-life engineering service (TES), the combined reduction in material consumption, facility in remanufacture process, as well as the capability in material recycle and reuse, makes AM a solid contributor to the complete life-cycle of a product, from design to remanufacture [9]. Despite the potential of the AM technologies, widespread adoption of AM has remained a challenge as it is impeded by quality issues such as inconsistency in part mechanical property and geometrical accuracy [10-11]. This paper focuses on the quality issue in one specific AM technology, the Fused Deposition Modeling (FDM). FDM is part of the material extrusion category used to make thermoplastic parts through heated extrusion and selective deposition of material layer by layer [1]. A 2017 survey conducted by Sculpteo reported that FDM is the most widely used AM technology with 36% of the market, citing its affordability as the main contributor to its popularity [12]. Literature survey shows a large amount of research has been dedicated to construct predictive model that quantifies part quality as functions of materials, machine settings, and control polices [13]. In [14], the influence of FDM settings on part surface condition was investigated using design of experiment (DoE), with surface roughness as the quality indicator. In this study, layer thickness was identified as an influential parameter while the effect of temperature on surface roughness is not statistically significant. Similar methodology has also been investigated in [15-17]. In [15], the effect of slice height and raster width are found to be significant on surface roughness. A comprehensive experimental investigation was carried out in [16] to evaluate the effect of layer thickness, printing plane and orientation on the mechanical properties of the printed part. Major conclusions include: highest yield strength occurred at smallest layer thickness and printing plane orientation has little effect when the layer is thick but is influential at the smallest layer thickness. In [17], an optimal region of machine setting combination was found at high temperature, low layer thickness as well as high feed/flow rate ratio. In [18], an analytical expression for surface roughness prediction was derived, taking into account the geometrical information such as overlap interval between adjacent filament and cross-sectional shape of the filament. The authors found that as the overlap intervals increase, surface roughness decrease. However, these works only investigated and discussed the effects of the static machine setting parameters on part quality, without taking into consideration the in-process variation during the printing process. Also, the predictive model should consider the AM process nature that a part is printed layer-by-layer, the thermal interactions that exist among layers as well as the nonlinear process-structure-property relationship [19]. The objective of this paper is to design an analytical model to account for 1) the layer-by-layer process and thermal interactions among layers and 2) in-process variation, by integrating sensor measurement with machine setting parameters for improved part quality prediction. The model is established based on Long-short Term Memory (LSTM). As one of the major branches in deep learning [20], LSTM features a special network structure that captures the temporal relationship within a sequence of data points [21], and therefore considered well suited for time series modeling. Major applications of LSTM in manufacturing include machine tool-wear prediction and system remaining life estimation [22, 23]. For the purpose of AM layer-wise modeling, the layer thermal history (revealed by infrared measurement) are taken as the inputs to the network, with the layer thermal interactions modeled by the information flow among LSTM cells. The remainder of the paper is organized as follows: Section 2 details the FDM process and the process layer thermal history, while Section 3 describes the LSTM structure and the proposed LSTM-based predictive model for part quality prediction. Section 4 presents the procedure for experimental evaluation of the model, using part tensile strength as a representative quality variable. The results of the evaluation are discussed in Section 5, in view of the performance of the predictive model. Conclusions and future work are presented in Section 6. 2. Fused Deposition Modeling 2.1. FDM Process The FDM begins by processing the STereoLithography (STL) file of the part, then orienting and slicing the model along the build direction for printing. During the printing process, the extruder is heated to melt the material past its



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glass transition temperature, which is then deposited by an extrusion nozzle. The part is formed layer-by-layer as the material cools and hardens after being extruded. The extruder moves in horizontal directions. Once a layer of the part is printed, the build plate lowers by a pre-defined distance in the build direction to allow the deposition of the material to form the higher layer. This process continues until the complete part is printed [16]. The FDM setup is illustrated in Fig. 1-a). The most commonly used material in FDM includes Acrylonitrile Butadiene Styrene (ABS) and Polylactide (PLA), both of which are thermoplastics. 2.2. Process-Structure-Property Relationship (PSP) The sequential interrelation of processing-structure-properties (PSP) relationship is of central importance towards the understanding of the AM process, where the thermal history, directly determined by the layer-by-layer printing process of the AM, involves cycles of heating and cooling [19]. Fig. 1-b) shows representative temperature curves measured by a thermocouple located at the center of a 130 mm x 130 mm x 3.3 mm test sample during 2 experimental runs of FDM. The thermal behavior of the sensor location, as revealed by the temperature variation figure, is shown to be cyclic as layers being printed on top of it. This further implies that lower printed layers can potentially evolve under the cyclical thermal behaviors when printing topper layers until the entire part is printed and mechanical property is finalized. Thus, the integration of the PSP relationship and layer thermal history into the predictive model can potentially improve the part property prediction. However, comprehensive physics-based model is still not available, which motivates the establishment of a data-driven predictive model in this paper that takes into consideration the PSP relationship and layer thermal history, to form basis for process optimization

Fig. 1. Illustration of the FDM process. a) Equipment setup [24]; b) Representative waveform of thermal history (measured by a thermocouple located at the geometric center of test specimen) [25]

3. Method for Modeling To effectively model the layer-by-layer printing process of FDM as well as the interrelation among layers (i.e. the thermal history comprised of heating-cooling cycles), a Long short-term memory (LSTM) based approach has been investigated. The background and the structure of LSTM is first introduced, followed by the details of the proposed FDM predictive model. 3.1. LSTM Structure The basic element of LSTM is the LSTM cell as shown in Fig. 2 [21]. The subscript indicates the order in the sequence, for example, 𝒙𝒙(𝑛𝑛) is the nth input. Different from other deep learning structures that only involve the input 𝒙𝒙 and output 𝒚𝒚, the LSTM cell features two forward paths 𝒄𝒄 and 𝒉𝒉 that transfer the information down the sequence. As an example, the output at the nth LSTM cell, 𝒚𝒚(𝑛𝑛) will be jointly determined by the corresponding input, 𝒙𝒙(𝑛𝑛) , as well as the information from early data points in the sequence. Inside the LSTM cell, 𝒈𝒈(𝑛𝑛) is the output from the standard recurrent neuron layer following the mathematical expression: 𝒈𝒈(𝑛𝑛) = 𝜙𝜙(𝑊𝑊𝑥𝑥 𝒙𝒙(𝑛𝑛) + 𝑊𝑊ℎ 𝒉𝒉(𝑛𝑛−1) + 𝒃𝒃)

(1)

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where 𝑊𝑊𝑥𝑥 and 𝑊𝑊ℎ are two sets of weights, b is the bias term and 𝜙𝜙(∙) is the activation function. 𝒇𝒇(𝑛𝑛) , 𝒊𝒊(𝑛𝑛) and 𝒐𝒐(𝑛𝑛) are three logic gates that control the information flow inside the cell. All of them follow an expression similar to Eq. (1), with each having its own set of weights and bias. In practice, all weights and bias will be iteratively optimized through the network training process.

Fig. 2. Diagram of an LSTM cell

3.2. Proposed FDM layer-wise modeling based on LSTM Leveraging the capability of LSTM for sequential data, the LSTM structure is adapted for modeling the layer-bylayer printing process of FDM. Specifically, each LSTM cell in the sequence will be corresponding to an individual layer in FDM, as illustrated in Fig. 3. Different from modeling the time series data in which the direction of the forward path is same as the direction of time progression, in FDM modeling, the first LSTM cell in the sequence corresponds to the final printing layer. This is because during the FDM process, the printing of later layers affects the early layers through cycles of heating and cooling. The influence on the early layers from the late layers is therefore modeled by the forward paths of the LSTM network.

Fig. 3. LSTM-based FDM process modeling and part quality prediction.

To account for the PSP relationship, the input to each LSTM cell includes layer-wise, in-process sensing data features, such as those obtained from temperature and vibration signals, as well as layer-printing features, such as printing direction of each layer. All LSTM cell outputs are aggregated in a fully-connected layer to predict the final part quality. Also, machine setting parameters are used as partial inputs to the fully-connected layer. Finally, different batches of material produced from the manufacturer can potentially have variation in property which may affect the printed part. To account for the potential variation among different batches of material used, batch information is also included in modeling as part of the categorical input to the fully-connected layer. To train the predictive model, backpropagation algorithm will be used to optimize the network weights and bias [21]. The cost function for the backpropagation is the part quality prediction mean squared error (MSE), between the predicted part quality from the network and the actual part quality obtained from the experimental tests. Once the



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network is trained, it can be used to predict the part quality based on the in-process sensing data as well as the machine settings. 4. Experimental Evaluation To train and evaluate the proposed predictive model, experiment is carried out using full factorial design [26]. Test specimen (type-II) from the ASTM D638-14 standard is used [27]. The model of the FDM printer is Makerbot Replicator +. Printing infill is set to 100%. Printing method is set to linear, meaning each layer is printed in one of the two orthogonal directions along the long and short axes of the specimen. Three sensors have been chosen for inprocess sensing: an IR sensor for temperature sensing at the location of filament deposition; a thermocouple for build plate temperature sensing, and an accelerometer for vibration sensing at the printer base. Sampling rate is 100 Hz. In this paper, tensile test is presented as representative test for part quality evaluation. The tester is an Instron 4411 series. Test procedure follows the ASTM D638-14 standard. As PLA material is characterized by brittle failure during tensile test, the maximum force reading (in kN) during the test is used as output variable for prediction, corresponding to the ultimate tensile strength. The complete experimental procedure is illustrated in Fig. 4.

Fig. 4. Complete experimental procedure. a) Part STL file processing; b) FDM part layer-by-layer printing; c) Printed ASTM D638-14 standard specimen; d) Tensile test setup on Instron 4411 series.

Extruder temperature (in °C), printing speed (in mm/s) and layer height (in mm) are selected as machine settings of interest following the analysis in [17] and [28]. A preliminary test of 24 experimental runs is first conducted to expose any potential experimental issue. The final 2x2x3 full factorial design is shown in Table 1. There’re total of 12 setting combinations and each combination has been repeated for 10 times. The mean and standard deviation of each combination are also shown in Table 1. Three box-plots, corresponding to the relationship between three machine settings and the pooled part tensile strength, are shown in Fig. 5. The box shows the quartiles of the data while the whiskers extend to show the rest of the data. The horizontal bar inside the box shows the median of the data. Several observations can be made from Table 1 and Fig. 5. First, the standard deviations observed in Table 1 are relatively large as compared to the range of the mean values, suggesting that large variation existed in the process. Second, in Fig. 5, when one single machine setting is analyzed at a time, there is a trend of improved tensile strength with reduced layer height, increased extruder temperature, and decreased printing speed. However, due to the relatively large variation shown in Table 1 within each combination, such a trend may not exist for a comparison between two combinations. For example, the combination (220, 80, 0.2) and combination (220, 110, 0.2) have the mean value of 0.931 and 0.916, respectively. Out of 12 combinations, the combination (220, 80, 0.2) gives the highest mean tensile strength value. However, Student-t test shows that the impact of printing speed in this comparison is not statistically significant, with the p-value of only 0.34. Therefore, by looking at the trend of a single machine setting alone, it is difficult to determine whether the setting is truly significant in affecting part tensile strength. This motivates the presented study, which aimed at developing a data-driven model based on LSTM to better extract the relationship between part tensile strength and the change in machine settings. To build numerical models, the collected sensing data is first segmented corresponding to individual printing layer. Then features are extracted from each sensor data segment to represent the layers’ thermal behaviors. Mean and standard deviation are extracted for IR and thermocouple sensor data; mean, standard deviation, skewness and kurtosis are extracted for vibration sensor data. Skewness characterizes the degree of signal asymmetry around its mean, and

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kurtosis indicates the spikiness of the signal. Both are commonly used vibration signal time domain features in manufacturing community for machine learning tasks [29]. One-hot encoding [30] is employed to encode the printing direction for each layer, by describing the two printing directions using vector of [0,1] and [1,0], respectively. The main purpose of using one-hot encoding is to be able to treat categorical parameter/factor in learning algorithm. As different layer height settings lead to different numbers of layers printed for the same part, the number of LSTM cells in the model is set to the maximum number of layers in the experiment: 15. For the part printed with thicker layer height and less layers, as the number of layer-wise inputs is less than the number of LSTM cells, zero padding is used to produce zero vectors as inputs in to the LSTM cells that do not correspond to printing layers such that the output of these cells will be effectively zero. Finally, three batches of material are used in this experiment. To account for the potential material property variation among different batches, one-hot encoding is again used, and these three batches are encoded as [1,0,0], [0,1,0] and [0,0,1], respectively. The total of the 144 experimental runs are randomly split into 100 and 44 for model training and validation, respectively. Evaluation is conducted for 10 random splits and root mean squared error (RMSE) of the predicted maximum force in the validation set is of interest. The parameters for LSTM network are set to: 20 neurons per LSTM cell and 0.005 learning rate. All features other than the one hot-encoded values are normalized to zero mean and unit variance within each training and validation set. Table 1. Design of Experiment (2x2x3). Extruder Temp (C)

Printing Speed (mm/s)

Layer Height (mm)

Mean tensile strength (kN)

Standard deviation (kN)

200 200 200 200 200 200 220 220 220 220 220 220

80 80 80 110 110 110 80 80 80 110 110 110

0.2 0.24 0.28 0.2 0.24 0.28 0.2 0.24 0.28 0.2 0.24 0.28

0.899 0.854 0.829 0.880 0.831 0.802 0.931 0.887 0.854 0.916 0.863 0.837

0.048 0.030 0.034 0.043 0.025 0.031 0.039 0.033 0.035 0.036 0.026 0.032

Fig. 5. Box plots of part tensile strength vs. each of the 3 machine settings.

5. Result and Discussion For the LSTM-based model, sensing data from three sensors, IR sensor, thermocouple (CT) and accelerometer (V), and their combinations are evaluated separately as inputs to the LSTM network. In total, 7 prediction results were obtained as shown in Fig. 6 a). It can be seen that when only the IR sensor, which directly monitors the temperature at filament deposition point, is included, the best predictive model is generated with the smallest prediction error of 0.018. The case using data from thermocouple is in distant second with a mean prediction RMSE of 0.045. On the other hand, it is noticed that the inclusion of vibration data greatly decreases the prediction accuracy. The data fusion has not been shown to help the part quality prediction in this application. Possible explanations for these observations include: 1) FDM is dominated by the thermal interaction during the layer-by-layer printing process, which in the



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current experiment is directly monitored by the IR sensor. Therefore, compared with the thermocouple and accelerometer in individual sensor comparison, IR sensor is expected to provide the most relevant information for predictive modeling and consequently, the most accurate prediction performance; 2) The relatively small sample size for training can potentially overfit the network, leading to reduced performance for the validation data. Fusing features from two or more sensors can also lead to a more complex data pattern that requires larger training data size as well as a more refined network structure. As an example, at the conclusion of training, for one of the examples using IR+CT+V, the training RMSE is 0.004 while the validation RMSE remains at 0.201, suggesting network overfitting as the improvement on validation accuracy stagnates well before the training accuracy. This is in contrast to the examples using IR, in which the training RMSE improves to 0.007 and the validation RMSE also improves to 0.013, much closer to the training accuracy. The LSTM-based model is then compared to three other techniques: non-linear regression as commonly used with DoE, Support Vector Machine (SVM) regression, and Random Forest (RF) regression. Specifically, for non-linear regression, three machine settings and their interactions have been used as regressors along with material batch information (known as “block” in DoE). For SVM and RF, as neither method can model the thermal history as the forward path in LSTM, all in-process features are concatenated together with machine settings and batch information as one single input for the evaluation. Specifically for SVM, the radial basis function (RBF) kernel was evaluated considering data non-linearity. For RF, the number of regression trees is set to 500, which is a commonly used value [31]. Performance comparison using IR sensing data only is shown in Fig. 6 b). It is seen that LSTM has produced the lowest RMSE value as compared to RF, SVM and non-linear regression, confirming its good performance.

Fig. 6. a) Prediction RMSE of proposed method with different combination of sensing data b) Part property prediction performance comparison among different techniques.

6. Conclusion This paper presents a data-driven approach to FDM part property prediction, based on a deep-learning technique termed LSTM. The LSTM network structure is implemented for modeling the FDM sequential layer printing process. The layer thermal history, a key component in the PSP relationship, is modeled by the forward path of LSTM. The experimental evaluation has shown that the proposed model is capable of predicting the tensile strength of printed part with a prediction RMSE of 1.8 ∙ 10−2 kN, by utilizing the in-process layer-wise features obtained from IR sensing data. The developed model outperforms commonly used models such as non-linear regression, Support Vector Machine regression, and Random Forest regression. In particular, a 46% reduction in the prediction error is achieved as compared to non-linear regression, which is the most commonly used technique in the literature. This suggests that LSTM is a good technique for improved AM predictive modeling and can serve as the basis for process optimization and subsequently, part quality control. Future work will collect additional experimental data to refine the model, and perform relevance analysis of the LSTM input such that the influence of the specific process parameters on the part quality can be quantitatively analyzed. This will serve the basis for more robust evaluation of the optimal set of parameters that leads to the best part quality. Acknowledgements This work is supported by the Digital Manufacturing and Design Innovation Institute under DMDII-15-14-01.

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