Divergent mutational processes distinguish hypoxic and normoxic tumours.

Many primary tumours have low levels of molecular oxygen (hypoxia), and hypoxic tumours respond poorly to therapy. Pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited comprehension of its associations with specific mutational processes, non-coding driver genes and evolutionary features. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we quantify hypoxia in 1188 tumours spanning 27 cancer types. Elevated hypoxia associates with increased mutational load across cancer types, irrespective of underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology directly associates with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in hypoxic tumours, and mutations in PTEN interact with hypoxia to direct tumour evolutionary trajectories. Overall, hypoxia plays a critical role in shaping the genomic and evolutionary landscapes of cancer

Spatial Transformation of a Layer-To-Layer Control Model for Selective Laser Melting

Selective Laser Melting (SLM) is an Additive Manufacturing (AM) technique with challenges in its complexity of process parameters and lack of control schemes. Traditionally, people tried time-domain or frequency-domain control methods, but the complexity of the process goes beyond these methods. In this paper, a novel spatial transformation of SLM models is proposed, which transforms the time-domain process into a spatial domain model and, thus, allows for state-space layer-to-layer control methods. In a space domain, this also provides the convenience of modelling laser path changes. Finally, a layer-to-layer Iterative Learning Control (ILC) method is designed and demonstrates the methodology of spatial control for SLM. A simulation demonstrates its application and performance

Laser Line Scan Characterization of Geometric Profiles in Laser Metal Deposition

Laser Metal Deposition (LMD) is an additive manufacturing process in which material is deposited by blowing powdered metal into a melt pool formed by a laser beam. When fabricating parts, the substrate is subjected to motion control such that the melt pool traces a prescribed path to form each part layer. Advantages of LMD include relatively efficient powder usage, the ability to create functionally-graded parts and the ability to repair high-value parts. The process, however, is sensitive to variations in process parameters and a need for feedback measurements and closed-loop control has been recognized in the literature [1, 2]. To this end, a laser line scanner is being integrated into an LMD system at the Missouri University of Science and Technology. Measurements from the laser line scanner will provide the feedback data necessary for closed-loop control of the process. The work presented here considers characteristics of the laser line scanner as it relates to scanning LMD depositions. Errors associated with the measurement device are described along with digital processing operations designed to remove them. The parameter bead height is extracted from scans for future use in a closed-loop control strategy

Control-Oriented Modeling and Layer-to-Layer Spatial Control of Powder Bed Fusion Processes

Powder Bed Fusion (PBF) is an important Additive Manufacturing (AM) process that is seeing widespread utilization. However, due to inherent process variability, it is still very costly and time consuming to certify the process and the part. This has led researchers to conduct numerous studies in process modeling, in-situ monitoring and feedback control to better understand the PBF process and decrease variations, thereby making the process more repeatable. In this study, we develop a layer-to-layer, spatial, control-oriented thermal PBF model. This model enables a framework for capturing spatially-driven thermal effects and constructing layer-to-layer spatial controllers that do not suffer from inherent temporal delays. Further, this framework is amenable to voxel-level monitoring and characterization efforts. System output controllability is analyzed and output controllability conditions are determined. A spatial Iterative Learning Controller (ILC), constructed using the spatial modeling framework, is implemented in two experiments, one where the path and part geometry are layer-invariant and another where the path and part geometry change each layer. The results illustrate the ability of the controller to thermally regulate the entire part, even at corners that tend to overheat and even as the path and part geometry change each layer

Crop Residue Effects on Surface Radiation and Energy Balance - Review

Crop residues alter the surface properties of soils. Both shortwave albedo and longwave emissivity are affected. These are linked to an effect of residue on surface evaporation and water content. Water content influences soil physical properties and surface energy partitioning. In summary, crop residue acts to soil as clothing acts to skin. Compared to bare soil, crop residues can reduce extremes of heat and mass fluxes at the soil surface. Managing crop residues can result in more favorable agronomic soil conditions. This paper reviews research results of the quantity, quality, architecture, and surface distribution of crop residues on soil surface radiation and energy balances, soil water content, and soil temperature

Fiber-Fed Laser-Heated Process for Printing Transparent Glass

This paper presents the Additive Manufacturing (AM) of glass using a fiber-fed process. Glass fiber with a diameter of 100 μm is fed into a laser generated melt pool. A CO2 laser beam is focused on the intersection between the fiber and the work piece which is positioned on a four-axis computer controlled stage. The laser energy at λ=10.6 μm is directly absorbed by the silica and locally heats the glass above the working point. By carefully controlling the laser power, scan speed, and feed rate, bubble free shapes can be deposited including trusses and basic lenses. Issues unique to the process are discussed, including the thermal breakdown of the glass, buckling of the fiber against an inadequately heated stiff molten region, and dimensional control when depositing viscous material

Iterative Learning Control of Single Point Incremental Sheet Forming Process using Digital Image Correlation

Single Point Incremental Sheet Forming (SPIF) is a versatile forming process that has gained significant traction over the past few decades. Its increased formability, quick part adaption, and reduced set-up costs make it an economical choice for small batch and rapid prototype forming applications when compared to traditional stamping processes. However, a common problem with the SPIF process is its tendency to produce high geometric error due to the lack of supporting dies and molds. While geometric error has been a primary focus of recent research, it is still significantly larger for SPIF than traditional forming processes. In this paper, the convergence behavior and the ability to reduce geometric error using a simple Iterative Learning Control (ILC) algorithm is studied with two different forming methods. For both methods a tool path for the desired reference geometry is generated and a part is formed. A Digital Image Correlation (DIC) system takes a measurement and the geometric error along the tool path is calculated. The ILC algorithm then uses the geometric error to alter the tool path for the next forming iteration. The first method, the Single Sheet Forming (SSF) method, performs each iteration on the same sheet. The second method, the Multi Sheet Forming (MSF) method, performs each iteration on a newly replaced sheet. Multiple experiments proved the capability of each method at reducing geometric error. It was concluded that using the MSF method allows for negative corrections to the forming part and, therefore, leads to better final part accuracy. However, this method is less cost effective and more time consuming than using the standard SSF methodology. In addition, it was found that in order to effectively correct a part with an ILC algorithm, steps must be taken to increase the controllability of the part geometry

Analysis of Geometric Accuracy and Thickness Reduction in Multistage Incremental Sheet Forming using Digital Image Correlation

Incremental Sheet Forming (ISF) is a freeform manufacturing method whereby a 3D geometry is created by progressively deforming a metal sheet with a single point tool following a defined trajectory. The thickness distribution of a formed part is a major consideration of the process and is believed to be improved by forming the geometry in multiple stages. This paper describes a series of experiments in which truncated cone geometries were formed using two multistage methods and compared to the same geometry formed using the traditional single stage method. The geometric accuracy and thickness distributions, including 3D thickness distribution plots, of each are examined using digital image correlation (DIC). The data collected indicate that multistage forming, compared to single stage forming, has a significant effect on the geometric accuracy of the processed sheets. Moreover, the results of the experiments conducted in this paper show that sheets processed with multistage forming do not have a uniform sheet thickness reduction, rather they have a parabolic-like thickness distribution in the processed region