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J. Manuf. Sci. Eng. 2017;139(7):071001-071001-24. doi:10.1115/1.4035676.

Many types of artifacts appear in X-ray computed tomography (CT) volume data, which influence measurement quality of industrial cone beam X-ray CT. Most of those artifacts are associated to CT scanning parameters; therefore, a good scanning parameter setting can weaken the influence to improve measurement accuracy. This paper presents a simulation method for evaluating CT scanning parameters for dimensional metrology. The method can aid CT metrology to achieve high measurement accuracy. In the method, image entropy is used as a criterion to evaluate the quality of CT volume data. For entropy calculation of CT volume data, a detailed description about bin width and entropy zone is given. The relationship between entropy values of CT volume data and error parameters of CT metrology is shown and discussed. By use of this method, mainly we focus on specimen orientation evaluation, and some other typical scanning parameters are used to evaluate the proposed method. Two typical specimens are used to evaluate the performance of the proposed method.

Topics: Entropy , Errors , Metrology
Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(7):071002-071002-13. doi:10.1115/1.4035898.

Uncertainty quantification (UQ) is an emerging field that focuses on characterizing, quantifying, and potentially reducing, the uncertainties associated with computer simulation models used in a wide range of applications. Although it has been successfully applied to computer simulation models in areas such as structural engineering, climate forecasting, and medical sciences, this powerful research area is still lagging behind in materials simulation models. These are broadly defined as physics-based predictive models developed to predict material behavior, i.e., processing-microstructure-property relations and have recently received considerable interest with the advent of emerging concepts such as Integrated Computational Materials Engineering (ICME). The need of effective tools for quantifying the uncertainties associated with materials simulation models has been identified as a high priority research area in most recent roadmapping efforts in the field. In this paper, we present one of the first efforts in conducting systematic UQ of a physics-based materials simulation model used for predicting the evolution of precipitates in advanced nickel–titanium shape-memory alloys (SMAs) subject to heat treatment. Specifically, a Bayesian calibration approach is used to conduct calibration of the precipitation model using a synthesis of experimental and computer simulation data. We focus on constructing a Gaussian process-based surrogate modeling approach for achieving this task, and then benchmark the predictive accuracy of the calibrated model with that of the model calibrated using traditional Markov chain Monte Carlo (MCMC) methods.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(7):071003-071003-12. doi:10.1115/1.4035794.

Gear shaping, commonly regarded as the most widely used machining method for cylindrical gear, is in fact an ideal manufacturing method for noncircular gear due to its merit of not being restricted by gear type or pitch curve in contrast to gear hobbing. However, concerning researches are mainly focused on the generation of noncircular straight external gear, paying rare attention to noncircular internal gear and noncircular helix gear. Considering that this paper, through using a three-linkage computer numerical control (CNC) shaping machine, aims to synthesize shaped noncircular gear, covering external, internal, straight, and helix gear. The mathematical model, a three-linkage model, is first established. The corresponding manufacturing process in practice is subsequently discussed. Finally, with practical shaping experiments, the correctness of the proposed model and the appropriateness of the manufacturing process are verified.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(7):071004-071004-13. doi:10.1115/1.4035491.

One of the challenges in micromilling processing is chatter, an unstable phenomenon which has a larger impact on the microdomain compared to macro one. The minimization of tool chatter is the key to good surface quality in the micromilling process, which is also related to the milling tool and the milling structure system dynamics. Frequency response function (FRF) at micromilling tool point describes dynamic behavior of the whole micromilling machine-spindle-tool system. In this paper, based on receptance coupling substructure analysis (RCSA) and the consideration of rotational degree-of-freedom, tool point frequency response function of micromilling dynamic system is obtained by combining two functions calculated from beam theory and obtained by hammer testing. And frequency response functions solved by Timoshenko's and Euler's beam theories are compared. Finally, the frequency response function is identified as the modal parameters, and the modal parameters are transformed into equivalent structural parameters of the physical system. The research work considers the difference of theoretical modeling between the micromilling and end-milling tool and provides a base for the dynamic study of the micromilling system.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(7):071005-071005-10. doi:10.1115/1.4035962.

With many industries increasingly relying on leased equipment and machinery, many original equipment manufacturers (OEMs) are turning to product-service packages where they deliver (typically lease) the physical assets. An integrated service contract will be offered for the asset. A classic example being Rolls Royce power-by-the-hour aircraft engines. Service contracts offered by original equipment manufacturers have predominantly focused on maintenance and upkeep activities for a single asset. Interestingly enough, manufacturing industries are beginning to adopt the product-service paradigm. However, one of the unique aspects in manufacturing settings is that the leased system is often not a single asset but instead a multi-unit system (e.g., an entire production line). In this paper, we develop a lease-oriented maintenance methodology for multi-unit leased systems under product-service paradigm. Unlike traditional maintenance models, we propose a leasing profit optimization (LPO) policy to adaptively compute optimal preventive maintenance (PM) schedules that capture the following dynamics: (1) the structural dependencies of the multi-unit system, (2) opportunistic maintenance of multiple system components, and (3) leasing profit savings (LPSs). We demonstrate the performance of our multi-unit maintenance policy by using a leased automotive manufacturing line and investigate its impact on leasing profits.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(7):071006-071006-9. doi:10.1115/1.4035795.

The hybrid stereolithography (SLA) process integrates a laser scanning-based system and a mask projection-based system. Multiple laser paths are used to scan the border of a 2D pattern, whereas a mask image is adopted to solidify the interior area. By integrating merits of two subsystems, the hybrid SLA process can achieve high surface quality without sacrificing productivity. For the hybrid system, closed polygonal contours are required to direct the laser scanning, and a binary image is also needed for the mask projection. We proposed a novel image-based slicing method. This approach can convert a 3D model into a series of binary images directly, and each image is corresponding to the cross section of the model at a specific height. Based on the resultant binary image, we use an image processing method to gradually shrink the pattern in the image. Boundaries of the shrunk image are traced and then restored as polygons to direct the laser spot movement. The final shrunk image serves as the input for the mask projection. Experimental results of test cases demonstrate that the proposed method is substantially more efficient than the traditional approaches. Its accuracy is also studied and discussed.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(7):071007-071007-9. doi:10.1115/1.4035963.

Fused deposition modeling (FDM) is currently one of the most widely utilized prototyping technologies. Studies employing statistical techniques have been conducted to develop empirical relationships between FDM process factors and output variables such as dimensional accuracy, surface roughness, and mechanical properties of the fabricated structures. However, the effects of nozzle temperature (T), nozzle-bed distance (NBD), and their interactions on strut width (SW) have not been investigated. In the present work, a two-way factorial study with three levels of T and NBD in triplicates was undertaken. A fixed-effects model with interaction was proposed and remedial measures based on the error analysis were performed to obtain correct inferences. The factor main/interaction effects were all found to be statistically significant (p < 0.05) using analysis of variance (ANOVA). Multiple comparisons were conducted between treatment means using the Tukey's method. A multiple linear regression (MLR) model (R2 = 0.95) was subsequently developed to enable the prediction of SW. The developed MLR model was verified experimentally; by (1) the fabrication of individual struts and (2) the fabrication of single-layer scaffolds with parallel raster patterns. The percentage error between the predicted and observed widths of individually fabricated struts was 3.2%, and the error between predicted and observed SW/spacing for the single-layer scaffolds was ≤ 5.5%. Results indicate that a similar statistical methodology could be potentially employed to identify levels of T and NBD that yield defined width struts using open architecture, personal or commercial FDM setups, and existing/new materials.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(7):071008-071008-7. doi:10.1115/1.4035964.

In this paper, an additive manufacturing (AM) process, magnetic field-assisted projection stereolithography (M-PSL), is developed for 3D printing of three-dimensional (3D) smart polymer composites. The 3D-printed magnetic field-responsive smart polymer composite creates a wide range of motions, opening up possibilities for various new applications, like sensing and actuation in soft robotics, biomedical devices, and autonomous systems. In the proposed M-PSL process, a certain amount of nano- or microsized ferromagnetic particles is deposited in liquid polymer by using a programmable microdeposition nozzle. An external magnetic field is applied to direct the magnetic particles to the desired position and to form the desired orientation and patterns. After that, a digital mask image is used to cure particles in photopolymer with desired distribution patterns. The magnetic-field-assisted projection stereolithography (M-PSL) manufacturing process planning, testbed, and materials are discussed. Three test cases, an impeller, a two-wheel roller, and a flexible film, were performed to verify and validate the feasibility and effectiveness of the proposed process. They were successfully fabricated and remote controls of the printed samples were demonstrated, showing the capability of printed smart polymer composites on performing desired functions.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2017;139(7):071009-071009-9. doi:10.1115/1.4035796.

Bending 3D free form metal plates is a common process used in many heavy industries such as shipbuilding. The traditional method is the so-called line heating method, which is not only labor intensive but also inefficient and error-prone. This paper presents a new incremental bending method based on minimum energy principle and model-less control. First, the sheet metal is discretized into a number of strips connected through virtual springs. Next, by applying the minimum energy principle, the punching and supporting points are calculated for the strip. Then, the bended shape of the strip is computed based on the beam bending theory. This process is continued until the final shape is reached. To compensate the bending error, the computer vision-based model-less control is applied. The computer vision detects the bending error based on which additional bending steps are calculated. The new method is tested in a custom build incremental bending machine. Different metal plates are formed. For a metal plate of 1000 × 800 × 5 mm3, the average bending error is less than 3 mm. In comparison with the existing methods, the new method has a number of advantages, including simple, fast, and highly energy efficient.

Commentary by Dr. Valentin Fuster

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