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J. Manuf. Sci. Eng. 2018;140(9):091001-091001-14. doi:10.1115/1.4039647.

Slicing of Si wafers through abrasive processes generates various surface defects on wafers such as cracks and surface contaminations. Also, the processes cause a significant material loss during slicing and subsequent polishing. Recently, efforts are being made to slice very thin wafers, and at the same time understand the thermal and microstructural damage caused due to sparking during wire-electrical discharge machining (wire-EDM). Wire-EDM has shown potential for slicing ultra-thin Si wafers of thickness < 200 μm. This work, therefore, presents an extensive experimental work on characterization of the thermal damage due to sparking during wire-EDM on ultra-thin wafers. The experiments were performed using Response surface methodology (RSM)-based central composite design (CCD). The damage was mainly characterized by scanning electron microscope (SEM), transmission electron microscopy (TEM), and Raman spectroscopy. The average thickness of thermal damage on the wafers was observed to be ∼16 μm. The damage was highly influenced by exposure time of wafer surface with EDM plasma spark. Also, with an increase in diameter of plasma spark, the surface roughness was found to increase. TEM micrographs have confirmed the formation of amorphous Si along with a region of fine grained Si entrapped inside the amorphous matrix. However, there were no signs of other defects like microcracks, twin boundaries, or fracture on the surfaces. Micro-Raman spectroscopy revealed that in order to slice a wafer with minimum residual stresses and very low presence of amorphous phases, it should be sliced at the lowest value of pulse on-time and at the highest value of open voltage (OV).

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(9):091002-091002-16. doi:10.1115/1.4040264.

The goal of this work is to monitor the laser powder bed fusion (LPBF) process using an array of sensors so that a record may be made of those temporal and spatial build locations where there is a high probability of defect formation. In pursuit of this goal, a commercial LPBF machine at the National Institute of Standards and Technology (NIST) was integrated with three types of sensors, namely, a photodetector, high-speed visible camera, and short wave infrared (SWIR) thermal camera with the following objectives: (1) to develop and apply a spectral graph theoretic approach to monitor the LPBF build condition from the data acquired by the three sensors; (2) to compare results from the three different sensors in terms of their statistical fidelity in distinguishing between different build conditions. The first objective will lead to early identification of incipient defects from in-process sensor data. The second objective will ascertain the monitoring fidelity tradeoff involved in replacing an expensive sensor, such as a thermal camera, with a relatively inexpensive, low resolution sensor, e.g., a photodetector. As a first-step toward detection of defects and process irregularities that occur in practical LPBF scenarios, this work focuses on capturing and differentiating the distinctive thermal signatures that manifest in parts with overhang features. Overhang features can significantly decrease the ability of laser heat to diffuse from the heat source. This constrained heat flux may lead to issues such as poor surface finish, distortion, and microstructure inhomogeneity. In this work, experimental sensor data are acquired during LPBF of a simple test part having an overhang angle of 40.5 deg. Extracting and detecting the difference in sensor signatures for such a simple case is the first-step toward in situ defect detection in additive manufacturing (AM). The proposed approach uses the Eigen spectrum of the spectral graph Laplacian matrix as a derived signature from the three different sensors to discriminate the thermal history of overhang features from that of the bulk areas of the part. The statistical accuracy for isolating the thermal patterns belonging to bulk and overhang features in terms of the F-score is as follows: (a) F-score of 95% from the SWIR thermal camera signatures; (b) 83% with the high-speed visible camera; (c) 79% with the photodetector. In comparison, conventional signal analysis techniques—e.g., neural networks, support vector machines, linear discriminant analysis were evaluated with F-score in the range of 40–60%.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;140(9):091003-091003-15. doi:10.1115/1.4039889.

Exploring the hardening mechanisms during high speed machining (HSM) is an effective approach to improve the fatigue strength and the wear resistance of machined surface and to control the fragmentation of chips in a certain range of hardness. In this paper, the microhardness variation is explored from the perspective of microstructural evolutions, as a direct consequence of the severe deformation during HSM Ti-6Al-4V alloy. A microstructure-sensitive flow stress model coupled the phenomena of grain refinement, deformation twinning, and phase transformations is first proposed. Then the microstructure-sensitive flow stress model is implemented into the cutting simulation model via a user-defined subroutine to analyze the flow stress variation induced by the microstructure evolutions during HSM Ti-6Al-4V. Finally, the relationship between the microhardness and flow stress is developed and modified based on the classical theory that the hardness is directly proportional to the flow stress. The study shows that the deformation twinning (generated at higher cutting speeds) plays a more important role in the hardening of Ti-6Al-4V compared with the grain refinement and phase transformation. The predicted microhardness distributions align well with the measured values. It provides a novel thinking that it is plausible to obtain a high microhardness material via controlling the microstructure alterations during machining process.

Commentary by Dr. Valentin Fuster

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