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Research Papers

J. Manuf. Sci. Eng. 2017;139(9):091001-091001-12. doi:10.1115/1.4036908.

Additive manufacturing (AM) is rapidly becoming a local manufacturing modality in fabricating complex, custom-designed parts, providing an unprecedented form-free flexibility for custom products. However, significant variability in part geometric quality and mechanical strength due to the shortcomings of AM processes has often been reported. Presently, AM generally lacks in situ quality inspection capability, which seriously hampers the realization of its full potential in delivering qualified practical parts. Here, we present a monitoring approach and a periodic structure design for developing test artifacts for in situ real-time monitoring of the material and bonding properties of a part at fiber/bond-scale. While the production method used in current work is filament based, the proposed approach is generic as defects are always due to materials in a bonding zone and their local bonding attributes in any production modality. The artifact design detailed here is based on ultrasonic wave propagation in phononic coupons consisting of repeating substructures to monitor and eventually to assess the bond quality and placement uniformity—not only for geometry but also for defect states. Periodicity in a structure leads to the dispersion of waves, which is sensitive to geometric/materials properties and irregularities. In this proof-of-concept study, an experimental setup and basic artifact designs are described and off-line/real-time monitoring data are presented. As a model problem, the effects of printing speed on the formation of stop bands, wave propagation speeds and fiber placement accuracy in samples are detected and reported.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(9):091002-091002-11. doi:10.1115/1.4036992.

The quality of threaded pipe connections is one of the key quality characteristics of drill pipes, risers, and pipelines. This quality characteristic is evaluated mainly by a pair of critical points, which are corresponding to the mechanical deformations formed in the pipe connection process. However, these points are difficult to detect because of nonlinear patterns generated by latent process factors in torque signals, which conceal the true critical points. To address this problem, we propose a novel three-phase state-space model that incorporates physical interpretations of connection process to detect pairwise critical points. We also develop a two-stage recursive particle filter to estimate the locations of the underlying critical points. Results of a real threaded pipe connection case show that the detection performance of the proposed method is more powerful than that of other existing methods.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(9):091003-091003-10. doi:10.1115/1.4036910.

This paper describes in detail the deformation behavior of the rolls and strip predicted from the three-dimensional finite element analysis of skin-pass rolling. The predictions are made on the basis of the coupled analysis of elastic deformation of the rolls and elastic–plastic deformation of the strip. Predictions from the proposed finite element (FE) model are compared with experimental data from laboratory-scale cold rolling mills. Then, proposed are models for the prediction of the roll force profile and for the prediction of the residual stress profile. The prediction accuracy of the models is examined through comparison with the predictions from the FE model.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(9):091004-091004-11. doi:10.1115/1.4036714.

Residual stress (RS) has significant impact on cutting process optimization. However, conventional process modeling approaches are limited to only single cutting pass on very short length and time scales due to the exceedingly high computational cost. This work provides a new concept of equivalent loading which enables an efficient modeling approach to predict RS in an actual machined surface by incorporating multiple cutting passes and crossing different length and time scales. The predicted residual stress profiles are validated in turning Inconel 718 superalloy under different edge geometries and process conditions.

Topics: Stress , Cutting
Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(9):091005-091005-14. doi:10.1115/1.4036641.

The objective of this work is to develop and apply a spectral graph theoretic approach for differentiating between (classifying) additive manufactured (AM) parts contingent on the severity of their dimensional variation from laser-scanned coordinate measurements (3D point cloud). The novelty of the approach is in invoking spectral graph Laplacian eigenvalues as an extracted feature from the laser-scanned 3D point cloud data in conjunction with various machine learning techniques. The outcome is a new method that classifies the dimensional variation of an AM part by sampling less than 5% of the 2 million 3D point cloud data acquired (per part). This is a practically important result, because it reduces the measurement burden for postprocess quality assurance in AM—parts can be laser-scanned and their dimensional variation quickly assessed on the shop floor. To realize the research objective, the procedure is as follows. Test parts are made using the fused filament fabrication (FFF) polymer AM process. The FFF process conditions are varied per a phased design of experiments plan to produce parts with distinctive dimensional variations. Subsequently, each test part is laser scanned and 3D point cloud data are acquired. To classify the dimensional variation among parts, Laplacian eigenvalues are extracted from the 3D point cloud data and used as features within different machine learning approaches. Six machine learning approaches are juxtaposed: sparse representation, k-nearest neighbors, neural network, naïve Bayes, support vector machine, and decision tree. Of these, the sparse representation technique provides the highest classification accuracy (F-score > 97%).

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(9):091006-091006-11. doi:10.1115/1.4036784.

When machining narrow grooves, corners, and other complex cavities with trochoidal milling, the irrationally large trochoidal step usually leads to chatter, while the conservative trochoidal step constrains the machining efficiency. In this paper, a stability prediction model of trochoidal milling is established to solve these problems. An approach considering trochoidal steps and spindle speeds is presented to predict stability boundary of trochoidal milling. With considering the varying cutter-workpiece engagements, the stability of trochoidal milling process is predicted by obtaining the stability lobes of different cutter location (CL) points along the trochoidal milling tool paths. Based on the proposed stability model, a trochoidal step optimization strategy is developed to improve the machining efficiency of trochoidal milling under other parameters in a given situation. Cutting experiments are performed on the machining center GMC 1600H/2 to show the effectiveness of the proposed trochoidal milling stability model. Finally, simulations are adopted to illustrate the optimization strategy.

Commentary by Dr. Valentin Fuster

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