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J. Manuf. Sci. Eng. 2019;141(7):071001-071001-12. doi:10.1115/1.4043581.

Assembly system configuration determines the topological arrangement of stations with defined logical material flow among them. The design of assembly system configuration involves (1) subassembly planning that defines subassembly tasks and between-task material flows and (2) workload balancing that determines the task-station assignments. The assembly system configuration should be flexibly changed and updated to cope with product design evolution and updating. However, the uncertainty in future product evolution poses significant challenges to the assembly system configuration design since the higher cost can be incurred if the assembly line suitable for future products is very different from that for the current products. The major challenges include (1) the estimation of reconfiguration cost, (2) unavailability of probability values for possible scenarios of product evolution, and (3) consideration of the impact of the subassembly planning on the task-station assignments. To address these challenges, this paper formulates a concurrent optimization problem to design the assembly system configuration by jointly determining the subassembly planning and task-station assignments considering uncertain product evolution. A new assembly hierarchy similarity model is proposed to estimate the reconfiguration effort by comparing the commonalities among different subassembly plans of current and potential future product designs. The assembly system configuration is chosen by maximizing both assembly hierarchy similarity and assembly system throughput under the worst-case scenario. A case study motivated by real-world scenarios demonstrates the applicability of the proposed method including scenario analysis.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2019;141(7):071002-071002-10. doi:10.1115/1.4043536.

Support structures are always associated with laser-based powder-bed fusion (L-PBF) processes, particularly for additive manufacturing of metallic components of complex geometry with overhang structures and for reducing component distortion. Existing L-PBF processes use the same material for both built components and support structures. Removing the metallic support structures from L-PBF fabricated components by the traditional post-treatment method is difficult and time-consuming. This paper demonstrates an easy-to-remove composite support material and related processing procedures in an L-PBF process. For additive manufacturing of 316L components, a SiC-316L composite was developed as a support material. This is combined with hybrid powder-bed and point-to-point selective powder deposition for the additive manufacturing of the components. A specific experimental multiple material L-PBF system was developed and employed to produce 316L components with SiC-316L composite as support structures successfully. An interfacial grid structure using 316L steel was used to avoid component contamination and inferior surface roughness of the 316L component. The experimental results demonstrated that the SiC-316L composite with 40 vol. % 320 grit SiC was feasible to be applied as a support material for 316L stainless steel component additive manufacture in a modified PBF system.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2019;141(7):071003-071003-14. doi:10.1115/1.4043578.

Prediction of temperature in the tool, chip, and workpiece surface is important to study tool wear, residual stresses in the machined part, and to design cutting tool substrates and coating. This paper presents a finite difference method-based prediction of temperature distribution in the tool, chip, and workpiece surface for transient conditions. The model allows inclusion of anisotropic materials such as coating or different material properties. The energy is created in the primary shear zone where the metal is sheared, the secondary deformation zone where the chip moves on the tool rake face with friction, and the tertiary zone where the flank face of the tool rubs against the finished part surface. The model allows both sticking and sliding friction contact of the moving chip on the rake face of the tool. The distribution of temperature is evaluated by meshing chip, workpiece surface zone, and tool into small discrete elements. The heat transfer among the elements is modeled, and the temperature is predicted at the center of each element. The heat transfer to the tool, workpiece, and chip is iteratively evaluated. The predicted temperature values are compared against the experimental measurements collected with coated tools in turning.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2019;141(7):071004-071004-11. doi:10.1115/1.4043622.

Porosity is an inherent attribute in selective laser melting (SLM) and profoundly degrades the build part quality and its performance. This study attempts to understand and characterize the keyhole pores formed during single-track scanning in SLM. First, 24 single tracks were generated using different line energy density (LED) levels, ranging from 0.1 J/mm to 0.98 J/mm, by varying the laser power and the scanning speed. The samples were then scanned by micro-computed tomography to measure keyhole pores and analyze the pore characteristics. The results show a general trend that the severity of the keyhole porosity increases with the increase of the LED with exceptions of certain patterns, implying important individual contributions from the parameters. Next, by keeping the LED constant in another set of experiments, different combinations of the power and the speed were tested to investigate the individual effect. Based on the results obtained, the laser power appears to have a greater effect than the scanning speed on both the pore number and the pore volume as well as the pore depth. For the same LED, the pore number and volume increase with increasing laser power until a certain critical level, beyond which, both the pore number and volume will decrease, if the power is further increased. For the LED of 0.32 J/mm, 0.4 J/mm, and 0.48 J/mm, the critical laser power that reverses the trend is about 132 W, 140 W, and 144 W, respectively.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2019;141(7):071005-071005-12. doi:10.1115/1.4043575.

A new adaptive disturbance feedforward control strategy of the strip thickness in a hot strip rolling mill with online parameter estimation is proposed. The feedforward control strategy makes use of the measured strip temperature and strip entry thickness. To avoid that these disturbances cause a nonuniform strip exit thickness, the Sims’ roll gap model and a linear mill stand deflection model are used to compute control inputs, which compensate for these disturbances. By minimizing the difference between the expected roll force from the model and the measured roll force, uncertain parameters of the model and also errors of the strip tracking are estimated in real time. The estimated parameters are immediately used in the adaptive feedforward controller. Experimental results of the proposed control approach obtained from an industrial hot strip rolling mill show a significant improvement of the strip thickness accuracy compared to the existing standard controllers. The proposed adaptive feedforward control strategy is now in permanent operation at the considered rolling mill.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2019;141(7):071006-071006-7. doi:10.1115/1.4043649.

Robot accuracy degradation sensing, monitoring, and assessment are critical activities in many industrial robot applications, especially when it comes to the high accuracy operations which may include welding, material removal, robotic drilling, and robot riveting. The degradation of robot tool center accuracy can increase the likelihood of unexpected shutdowns and decrease manufacturing quality and production efficiency. The development of monitoring, diagnostic and prognostic (collectively known as prognostics and health management (PHM)) technologies can aid manufacturers in maintaining the performance of robot systems. PHM can provide the techniques and tools to support the specification of a robot’s present and future health state and optimization of maintenance strategies. This paper presents the robotic PHM research and the development of a quick health assessment at the U.S. National Institute of Standards and Technology (NIST). The research effort includes the advanced sensing development to measure the robot tool center position and orientation; a test method to generate a robot motion plan; an advanced robot error model that handles the geometric/nongeometric errors and the uncertainties of the measurement system, and algorithms to process measured data to assess the robot’s accuracy degradation. The algorithm has no concept of the traditional derivative or gradient for algorithm converging. A use case is presented to demonstrate the feasibility of the methodology.

Commentary by Dr. Valentin Fuster

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