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Research Papers

J. Manuf. Sci. Eng. 2018;141(1):011001-011001-10. doi:10.1115/1.4041423.

Understanding the capture efficiency of powder during direct laser deposition (DLD) is critical when determining the overall manufacturing costs of additive manufacturing (AM) for comparison to traditional manufacturing methods. By developing a tool to predict the capture efficiency of a particular deposition process, parameter optimization can be achieved without the need to perform a costly and extensive experimental study. The focus of this work is to model the deposition process and acquire the final track geometry and temperature field of a single track deposition of Ti–6Al–4V powder on a Ti–6Al–4V substrate for a four-nozzle powder delivery system during direct laser deposition with a LENS™ system without the need for capture efficiency assumptions by using physical powder flow and laser irradiation profiles to predict capture efficiency. The model was able to predict the track height and width within 2 μm and 31 μm, respectively, or 3.3% error from experimentation. A maximum of 36 μm profile error was observed in the molten pool, and corresponds to errors of 11% and 4% in molten pool depth and width, respectively. Based on experimentation, the capture efficiency of a single track deposition of Ti–6Al–4V was found to be 12.0%, while that from simulation was calculated to be 11.7%, a 2.5% deviation.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):011002-011002-11. doi:10.1115/1.4041425.

High-definition metrology (HDM) has gained significant attention for surface quality inspection since it can reveal spatial surface variations in detail. Due to its cost and durability, such HDM measurements are occasionally implemented. The limitation creates a new research opportunity to improve surface variation characterization by fusing the insights gained from limited HDM data with widely available low-resolution surface data during quality inspections. A useful insight from state-of-the-art research using HDM is the revealed relationship and positive correlation between surface height and certain measurable covariates, such as material removal rate (MRR). Such a relationship was assumed spatially constant and integrated with surface measurements to improve surface quality modeling. However, this method encounters challenges when the covariates have nonstationary relationships with the surface height over different surface areas, i.e., the covariate-surface height relationship is spatially varying. Additionally, the nonstationary relationship can only be captured by HDM, adding to the challenge of surface modeling when most training data are measured at low resolution. This paper proposes a transfer learning (TL) framework to deal with these challenges by which the common information from a spatial model of an HDM-measured surface is transferred to a new surface where only low-resolution data are available. Under this framework, the paper develops and compares three surface models to characterize the nonstationary relationship including two varying coefficient-based spatial models and an inference rule-based spatial model. Real-world case studies were conducted to demonstrate the proposed methods for improving surface modeling.

Topics: Modeling , Algorithms
Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):011003-011003-12. doi:10.1115/1.4041427.

In this paper, we present a new approach of combining point-by-point selective powder delivery with powder bed fusion for multiple material (metal/glass) components printing. Dual ultrasonic vibration was used to achieve stable flowrates of both 316 L steel and soda-lime glass powders which were dispensed selectively and separately. The effects of the stand-off distance and the scanning speeds on the quality of the formed layers were investigated. The results showed that the ratio between the stand-off distance and the powder size (h/d) should be lower than 3 for accurate selective material deposition. However, in practical processing, for preventing the nozzle from being damaged by the parts, the stand-off distance was larger than three times of the powder size. Different laser processing parameters were developed for processing the metal and glass due to material property differences. The interfaces between 316 L and soda-lime glass were examined. A number of 3D objects consisting of metal and glass were printed using this method.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):011004-011004-9. doi:10.1115/1.4041326.

Natural fibers are emerging in many industrial sectors to perform eco-friendly materials such as bio-composites. However, machining of natural fiber reinforced polymer (NFRP) composites remains a complex manufacturing process and the machinability of industrial components underlies a specific approach that involves the multiscale structure of natural fibers. This paper presents first a multiscale method used in machinability rating of NFRP. The fundamentals of the multiscale method are hence applied to experimentally assess the machinability of a complete industrial bio-composite part. Results show that machining NFRP composites requires specific analysis scales that are intimately linked to the natural fibrous structure. The multiscale method can be used to improve the experimental design of NFRP machining and, above all, to determine the optimum process parameters that reflect the multiscale machining characteristics of these bio-based materials.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):011005-011005-11. doi:10.1115/1.4041624.

Understanding the binder–powder interaction and primitive formation is critical to advancing the binder jetting Additive Manufacturing process and improving the accuracy, precision, and mechanical properties of the printed parts. In this work, the authors propose an experimental approach based on sessile drop goniometry on a powder substrate to characterize the binder wetting powder process. As a binder drop penetrates into a prepared powder substrate, the dynamic contact angle formed in powder pores is calculated based on the measured binder penetration time, and the binder penetration depth is measured from the binder-powder granule retrieved from the powder substrate. Coupled with models of capillary flow, the technique provides a fundamental understanding of the binder–powder interaction that determines the material compatibility and printing parameters in binder jetting. Enabled by this gained understanding, it was determined that suspending nanoparticles in a binder could increase the capillary-driven penetration depth, which was then reduced by the further increase of the nanoparticle solid loading and resultant binder viscosity.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):011006-011006-8. doi:10.1115/1.4041570.

Hybrid manufacturing has become particularly attractive for refurbishing of high-value freeform components. Components may experience unique geometric distortions and/or wear-driven material loss in service, which require the use of part-specific, adaptive repair strategies. The current work presents an integrated adaptive geometry transformation method for additive/subtractive hybrid manufacturing based on rigid and nonrigid registrations of parent region material and geometric interpolation of the repair region material. In this approach, rigid registration of nominal part geometry to actual part geometry is accomplished using iterative alignment of profiles in the parent material. Nonrigid registration is used to morph nominal part geometry to actual part geometry by transformation of the profile mean line. Adaptive additive and subtractive tool paths are then used to add material based on constant stock margin requirements, as well as to produce blend repairs with smooth transition between parent and repair regions. A range of part deformation conditions due to profile twist and length changes are evaluated for the case of a compressor blade/airfoil geometry. Accuracy of the resulting adaptive geometry transformation method were quantified by (1) surface comparisons of actual and transformed nominal geometry and (2) blend region surface accuracy. Performance of the adaptive repair strategy relative to a naïve strategy is evaluated by the consideration of material efficiency and process cycle time. It is shown that the adaptive repair strategy resulted in an increase in material efficiency by 42.2% and a decrease in process time by 17.8%, depending on the initial deformation imposed on the part geometry.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):011007-011007-12. doi:10.1115/1.4041569.

Vacuum-assisted resin transfer molding (VARTM) has several inherent shortcomings such as long mold filling times, low fiber volume fraction, and high void content in fabricated laminates. These problems in VARTM mainly arise from the limited compaction of the laminate and low resin pressure. Pressurized infusion (PI) molding introduced in this paper overcomes these disadvantages by (i) applying high compaction pressure on the laminate by an external pressure chamber placed on the mold and (ii) increasing the resin pressure by pressurizing the inlet resin reservoir. The effectiveness of PI molding was verified by fabricating composite laminates at various levels of chamber and inlet pressures and investigating the effect of these parameters on the fill time, fiber volume fraction, and void content. Furthermore, spatial distribution of voids was characterized by employing a unique method, which uses a flatbed scanner to capture the high-resolution planar scan of the fabricated laminates. The results revealed that PI molding reduced fill time by 45%, increased fiber volume fraction by 16%, reduced void content by 98%, improved short beam shear (SBS) strength by 14%, and yielded uniform spatial distribution of voids compared to those obtained by conventional VARTM.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):011008-011008-13. doi:10.1115/1.4041709.

Laser powder bed fusion (LPBF) has the ability to produce three-dimensional lightweight metal parts with complex shapes. Extensive investigations have been conducted to tackle build accuracy problems caused by shape complexity. For metal parts with stringent requirements, surface roughness, laser beam positioning error, and part location effects can all affect the shape accuracy of LPBF built products. This study develops a data-driven predictive approach as a promising solution for geometric accuracy improvement in LPBF processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize geometrical deviations of built products through compensating computer aided design models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by part geometrical deviation, laser beam positioning error, and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):011009-011009-12. doi:10.1115/1.4041710.

Probabilistic sequential prediction of cutting forces is performed applying Bayesian inference to Kienzle force model. The model uncertainties are quantified using the Metropolis algorithm of the Markov chain Monte Carlo (MCMC) approach. Prior probabilities are established and posteriors of the models parameters and force predictions are completed using the results of orthogonal turning experiments. Two types of tools with chamfer (rake) angles of 0 deg and −10 deg are tested under various cutting speed and feed per revolution values. First, Bayesian inference is applied to two force models, Merchant and Kienzle, to investigate the cutting force prediction at the low feed values for the 0 deg rake angle tool. Second, the results of the posteriors of the Kienzle model parameters are used as prior probabilities of the −10 deg rake angle tool. The simulation results of the 0 deg and −10 deg tool rake angle are compared with the experiments which are obtained under other cutting conditions for model verification. Maximum prediction errors of 7% and 9% are reported for the tangential and feed forces, respectively. This indicates a good capability of the Bayesian inference for model parameter identification and cutting force prediction considering the inherent uncertainty and minimum input experimental data.

Topics: Cutting , Uncertainty , Chain
Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):011010-011010-17. doi:10.1115/1.4041626.

Additive manufacturing (or three-dimensional (3D) printing) is constantly growing as an innovative process for the production of complex-shape components. Among the seven recognized 3D printing technologies, fused deposition modeling (FDM) covers a very important role, not only for producing representative 3D models, but, mainly due to the development of innovative material like Peek and Ultem, also for realizing structurally functional components. However, being FDM a production process involving high thermal gradients, non-negligible deformations and residual stresses may affect the 3D printed component. In this work we focus on meso/macroscopic simulations of the FDM process using abaqus software. After describing in detail the methodological process, we investigate the impact of several parameters and modeling choices (e.g., mesh size, material model, time-step size) on simulation outcomes and we validate the obtained results with experimental measurements.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Manuf. Sci. Eng. 2018;141(1):014501-014501-10. doi:10.1115/1.4041625.

In a new blade manufacturing process, manufacturers precisely forge blade billets with the blade suction and the pressure surfaces within tolerance. After that, only two blade edge billets should be machined to the leading- and the trailing-edges within tolerance. If these edge design surfaces are used to generate tool paths for machining the edge billets, the machined edges are not continuous with the suction and the pressure surfaces. To address this problem, an optimal approach to constructing process models of edge surfaces is proposed for adaptive blade machining. Specifically, the modified edge surfaces are optimized within the design tolerance and are continuous with the billet suction and pressure surfaces. These surfaces are used to generate tool paths for machining the edge billets. This approach addresses the current technical challenge in the new blade manufacturing process and can substantially promote this process in blade mass production.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):014502-014502-9. doi:10.1115/1.4041627.

The continuing evolution of ultraprecision machining places an increasing need to perform surface measurement in the manufacturing environment. Development of on-machine surface measurement (OMSM) tools for ultraprecision machining processes will enable the reduction of measurement cycle time as well as the potential improvement of machining accuracy. In the present study, an in-house designed interferometer probe is integrated onto an ultraprecision diamond turning machine. System configuration, calibration scheme and various scanning strategies are first presented. The benefit of OMSM preserves the consistency between the machining and measurement coordinate system. Two applications of OMSM for ultraprecision turning process are further investigated. To further improve the surface accuracy, corrective machining is carried out based on the on-machine measured data. The profile accuracy of a cosine curve sample was improved after corrective machining with OMSM. Moreover, process investigation with OMSM was employed to model the effect of process parameters on the form error in ultraprecision cylindrical turning process. OMSM enables the consistent measurement of part coordinates for each experimental run, which is critical for acquiring a deterministic response for empirical modeling. A reduced quadratic model was built by means of response surface methodology and verified by the test for significance of the regression model. The confirmation tests show that the model predicted value conformed to the experimental value with a difference less than 4%.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(1):014503-014503-9. doi:10.1115/1.4041778.

This work presents the evaluation of three commercially available coolant grades (dicyclohexylamine-based coolant, a triethanolamine-based coolant, and an ester-based coolant) when machining Ti-6Al-4V alloy with high-pressure coolant delivery. The evaluations were based on tool life, tool failure modes, surface integrity, and chip formation. The dicyclohexylamine-based coolant was the more effective coolant when machining at the highest pressure of 20.3 MPa due to its stability at elevated temperature, whereas the triethanolamine-based coolant performed effectively at a pressure of 11 MPa due to its low surface tension properties. Deterioration of the ester-based coolant was found in almost all coolant pressures due to its low resistance to oxidation. Surfaces generated when machining with all coolants grades were generally acceptable with negligible physical damage.

Commentary by Dr. Valentin Fuster

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