Research Papers

J. Manuf. Sci. Eng. 2018;140(4):041001-041001-8. doi:10.1115/1.4037606.

The objective of this study was to develop a strategy for miniaturizing heat exchangers (HXs) used for the thermal management of sorbent beds within adsorption refrigeration systems. The thermal mass of the microchannel heat exchanger (MCHX) designed and fabricated in this study is compared with that of commercially available tube-and-fin HXs. Efforts are made to quantify the overall effects of miniaturization on system coefficient of performance (COP) and specific cooling power (SCP). A thermal model for predicting the cycle time for desorption is developed, and experiments are used to quantify the effect of the intensified HX on overall system performance.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041002-041002-7. doi:10.1115/1.4037605.

Service-oriented robotic manufacturing system (SORMS) is an integrated system, in which the industrial robots (IRs) operate within a service-oriented manufacturing model, and can be virtualized and servitized as services, so as to provide on-demand, agile, configurable, and sustainable manufacturing capability services to users in workshop environment. Manufacturing capability of such systems can be divided into three layers, including manufacturing cell layer, production process layer, and workshop layer. However, currently most of the existing works carried out the optimization on each layer individually. Manufacturing cells are the component parts of a production process, and there are close relationships between them and can affect the operation and performance for each other; therefore, it is essential to jointly consider the manufacturing capability service optimization on both layers. In this context, a cross-layer optimization model is proposed to conquer the existing limitation and provide a comprehensive performance assurance to SORMSs. The proposed model has different decision-making mechanisms on each layer, and the communications and interaction between the two layers can further coordinate the optimizations. A case study based on robotic assembly is implemented to demonstrate the availability and effectiveness of the proposed model.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041003-041003-14. doi:10.1115/1.4038568.

Development of monitoring devices becomes crucially important in selective laser melting (SLM) due to the high process complexity and the high value of the products obtained. This work discusses the design of a coaxial monitoring system for SLM using multiple sensors. In particular, an optical model is developed for the propagation of the process emission from the workpiece to the monitoring module. The model is used to determine the field of view (FOV) around the monitored zone. The lens arrangements and the optical filters are chosen according to the model results. They were implemented to construct a monitoring module consisting of two cameras viewing visible and near-infrared wavelength bands, as well as a photodiode viewing the back-reflected laser emission, all integrated in a coaxial configuration. The system functionality is tested with a prototype SLM machine during the processing of 18Ni300 maraging steel, a material known to be prone to porosity. In particular, different remelting strategies were employed as possible correction strategies to reduce porosity. The signals were interpreted as being indicators of the change in absorptivity of the laser light by the powder bed, of the plasma and molten pool, as well as of the evolution of the temperature field.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041004-041004-12. doi:10.1115/1.4038995.

To date, metal foam products have rarely made it past the prototype stage. The reason is that few methods exist to manufacture metal foam into the shapes required in engineering applications. Laser forming is currently the only method with a high geometrical flexibility that is able to shape arbitrarily sized parts. However, the process is still poorly understood when used on metal foam, and many issues regarding the foam's mechanical response have not yet been addressed. In this study, the mechanical behavior of metal foam during laser forming was characterized by measuring its strain response via digital image correlation (DIC). The resulting data were used to verify whether the temperature gradient mechanism (TGM), well established in solid sheet metal forming, is valid for metal foam, as has always been assumed without experimental proof. Additionally, the behavior of metal foam at large bending angles was studied, and the impact of laser-induced imperfections on its mechanical performance was investigated. The mechanical response was numerically simulated using models with different levels of geometrical approximation. It was shown that bending is primarily caused by compression-induced shortening, achieved via cell crushing near the laser irradiated surface. Since this mechanism differs from the traditional TGM, where bending is caused by plastic compressive strains near the laser irradiated surface, a modified temperature gradient mechanism (MTGM) was proposed. The densification occurring in MTGM locally alters the material properties of the metal foam, limiting the maximum achievable bending angle, without significantly impacting its mechanical performance.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041005-041005-10. doi:10.1115/1.4038002.

The emergence of cloud computing, industrial internet of things (IIoT), and new machine learning techniques have shown the potential to advance prognostics and health management (PHM) in smart manufacturing. While model-based PHM techniques provide insight into the progression of faults in mechanical components, certain assumptions on the underlying physical mechanisms for fault development are required to develop predictive models. In situations where there is a lack of adequate prior knowledge of the underlying physics, data-driven PHM techniques have been increasingly applied in the field of smart manufacturing. One of the limitations of current data-driven methods is that large volumes of training data are required to make accurate predictions. Consequently, computational efficiency remains a primary challenge, especially when large volumes of sensor-generated data need to be processed in real-time applications. The objective of this research is to introduce a cloud-based parallel machine learning algorithm that is capable of training large-scale predictive models more efficiently. The random forests (RFs) algorithm is parallelized using the MapReduce data processing scheme. The MapReduce-based parallel random forests (PRFs) algorithm is implemented on a scalable cloud computing system with varying combinations of processors and memories. The effectiveness of this new method is demonstrated using condition monitoring data collected from milling experiments. By implementing RFs in parallel on the cloud, a significant increase in the processing speed (14.7 times in terms of increase in training time) has been achieved, with a high prediction accuracy of tool wear (eight times in terms of reduction in mean squared error (MSE)).

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041006-041006-11. doi:10.1115/1.4038514.

Roller infusion by nip rollers is widely used in the infusion industry with broad applications, which is also adopted as one of the seven steps of a newly developed manufacturing process for making fungal mycelium-based biocomposites. One important technical issue related to infusion textile reinforcements for such biocomposites is how to predict and control the infusion fluid penetration depth, which directly affects the quality and performances of the preformed textile skins. Currently, the analytical relations between the modeling parameters and the final infusion penetration depth are still not well understood. Few studies have been performed on such topic and some of which used oversimplified assumptions. A new analytical model is developed in this paper, and the infusion penetration curves are plotted based on certain input parameters including infusion speed, infusion fluid flow rate, and clamping forces of the two rollers. The model-calculated results are then validated by experiments that are performed with the same parameters. The measured parameters of prepared non-Newtonian starch-based natural glue are used both in the modeling and experiments, and the results are close enough for model validation.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041007-041007-11. doi:10.1115/1.4038892.

The purpose of this paper is to study the effect of cutting tool surface geometry and the atomization-based cutting fluid (ACF) spray parameters on the characteristics of the thin film formed in an ACF delivery system. A computational model is developed using three submodels that are used to predict the carrier gas flow, droplet trajectories and the film formation, respectively. The model is validated through film thickness measurements using a laser displacement sensor. Turning inserts with chip-breaking grooves along with a conventional flat insert are used to study the effect of cutting tool surface geometry on the model-predicted film characteristics, including film thickness and velocity. Machining experiments are also conducted to investigate the effect of film characteristics on the machining performance in terms of tool wear, which show that the tool wear is minimum at a certain desired film thickness value and large film velocity value. Carrier gas pressure and cutting fluid flow rate are also varied to study the effect of ACF spray parameters on the film characteristics. Increase in the fluid flow results in increase in both film thickness and velocity, while an increase in the gas pressure results in the reduction of the film thickness but an increase in the film velocity.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041008-041008-6. doi:10.1115/1.4037431.

Ball burnishing is a process used to smooth rough surfaces. For not rotational symmetric parts, the process is typically conducted on milling machines. Since it is an incremental process, it is relatively time consuming. Therefore, a rolling tool is developed, which superposes the rotation of the milling spindle with the feed of the machine to increase the rolling velocity. In order to achieve constant rolling forces, hydrostatic ball-burnishing tools are used. Within this work, the influence of this tool concept on the processing time as well as on the leveling of surface irregularities is investigated. This is achieved by a comparison with a conventional ball-burnishing process. Finally, the rotating tool is used to investigate the influence of high rolling speeds on the leveling of the surface. All experiments were carried out with thermally coated specimens. A model for calculating the strain rates at the roughness peaks during ball burnishing is derived. For the experiments carried out with the rotating rolling tool, rolling velocities of 50,000 mm/min were realized. Calculations with the developed model showed that this results in local strain rates at the roughness peaks of up to 1384 s−1. In addition, the flow stresses at the roughness peaks were calculated. Compared with quasi-static experiments, the flow stress drops to less than the half under high velocities. This results in a better leveling of the surface for rolling velocities between 10,000 mm/min and 25,000 mm/min. A further rise of the rolling speed increases the flow stress again and thereby reduces the possible leveling.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041009-041009-13. doi:10.1115/1.4039092.

Macroscale finite element (FE) models, with their ability to simulate additive manufacturing (AM) processes of metal parts and accurately predict residual stress distribution, are potentially powerful design tools. However, these simulations require enormous computational cost, even for a small part only a few orders larger than the melt pool size. The existing adaptive meshing techniques to reduce computational cost substantially by selectively coarsening are not well suited for AM process simulations due to the continuous modification of model geometry as material is added to the system. To address this limitation, a new FE framework is developed. The new FE framework is based on introducing updated discretized geometries at regular intervals during the simulation process, allowing greater flexibility to control the degree of mesh coarsening than a technique based on element merging recently reported in the literature. The new framework is evaluated by simulating direct metal deposition (DMD) of a thin-walled rectangular and a thin-walled cylindrical part, and comparing the computational speed and predicted results with those predicted by simulations using the conventional framework. The comparison shows excellent agreement in the predicted stress and plastic strain fields, with substantial savings in the simulation time. The method is then validated by comparing the predicted residual elastic strain with that measured experimentally by neutron diffraction of the thin-walled rectangular part. Finally, the new framework's capability to substantially reduce the simulation time for large-scale AM parts is demonstrated by simulating a one-half foot thin-walled cylindrical part.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041016-041016-11. doi:10.1115/1.4038573.

This paper extends the general threading model developed in Part I to the case of thin-walled workpieces. Structural behavior of a cylindrical shell is dominated by the low-damped flexural modes. Due to the circumferential patterns of the shell modes, the cutting forces result in different instantaneous displacements around the circumference of the workpiece. The residual shell vibrations can affect the chip thickness when the corresponding point arrives at the cutting region. In this paper, the workpiece surface is discretized, and the instantaneous shell deformations due to the cutting forces are evaluated. The dynamic equation of motion for threading thin-walled workpieces is derived, and the stability and surface location errors are analyzed. The proposed threading model is validated experimentally on real-scale oil pipes for different pass numbers and infeed values. Sample approaches for chatter suppression are demonstrated experimentally.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041017-041017-13. doi:10.1115/1.4038510.

Shape control of composite parts is vital for large-scale production and integration of composite materials in the aerospace industry. The current industry practice of shape control uses passive manual metrology. This has three major limitations: (i) low efficiency: it requires multiple trials and a longer time to achieve the desired shape during the assembly process; (ii) nonoptimal: it is challenging to reach optimal deviation reduction; and (iii) experience-dependent: highly skilled engineers are required during the assembly process. This paper describes an automated shape control system that can adjust composite parts to an optimal configuration in a manner that is highly effective and efficient. The objective is accomplished by (i) building a finite element analysis (FEA) platform, validated by experimental data; (ii) developing a surrogate model with consideration of actuator uncertainty, part uncertainty, modeling uncertainty, and unquantified uncertainty to achieve predictive performance and embedding the model into a feed-forward control algorithm; and (iii) conducting multivariable optimization to determine the optimal actions of actuators. We show that the surrogate model considering uncertainties (SMU) achieves satisfactory prediction performance and that the automated optimal shape control system can significantly reduce the assembly time with improved dimensional quality.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(4):041018-041018-14. doi:10.1115/1.4038998.

Cutting process modeling is still a significant challenge due to the severe plastic deformation of the workpiece and intense friction between the workpiece and tool. Nowadays, a novel experimental approach based on digital image correlation (DIC) technique has been utilized to study the severe deformation of the workpiece. However, the experimentally measured velocity field does not necessarily satisfy the equilibrium equation that is one of the fundamental governing equations in solid mechanics due to the measurement errors; hence, accurate stress fields could hardly be derived. In this paper, we propose a hybrid DIC-FEM approach to optimize the velocity field and generate a stress field that is in an equilibrium state. First, the analysis region for finite element modeling (FEM) is selected according to the captured image, and the DIC results are used to track the deformation history of the material points. Secondly, the deviatoric stresses of the analysis region are calculated by employing the plastic theory. Thirdly, the hydrostatic pressures are acquired through solving over-constrained equations derived through FEM. Finally, the velocity field is optimized to satisfy the equilibrium equation and the boundary conditions (BCs) with the DIC results serving as an initial value of the workpiece velocity field. To validate this approach, the deformations including the velocity and strain yielded by the hybrid method are compared with the DIC results. The stress fields are presented to demonstrate the satisfaction of the equilibrium equation and the BCs. Moreover, cutting forces calculated through the integration of the stress tensors are compared against the FEM simulations and the experimental measurement.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Manuf. Sci. Eng. 2018;140(4):044501-044501-7. doi:10.1115/1.4039111.

Parallel turning is garnering attention as one of the most important technologies for multitasking machine tools. This is because a potential exists to enhance the stability limits compared to the turning operation using a single tool when cutting conditions are properly selected. Although stability prediction models for parallel turning have been developed in recent years, in-process monitoring and in-process chatter techniques are almost not discussed. In this study, to suppress chatter vibration, an unequal pitch turning method was proposed. In this method, the upper tool was controlled based on the optimum pitch angle calculated from spindle speed and chatter frequency. Chatter frequency was identified from estimated cutting force by a disturbance observer (DOB). From the result of the parallel turning test, it is clear that chatter vibration can be avoided by controlling the upper tool based on optimum pitch angle. Meanwhile, the pitch angle difference that can suppress chatter had a certain range. Subsequently, the robustness of the optimum pitch angle difference is experimentally evaluated by both the continuous moving test and the stepwise moving test of the pitch angle.

Commentary by Dr. Valentin Fuster

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