RESUMEN

Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and control within the desired level. Ultimately, the overall build will not achieve a near-net shape and will further hinder the part from performing its functionality without post-processing. Previous research primarily utilizes data analytical models (e.g., regression model, artificial neural network (ANN)) to forwardly predict the deposition width and height variation based on single or cross-linked process variables. However, these methods cannot effectively determine the optimal printable zone based on the desired deposition shape due to the inability to inversely deduce from these data analytical models. Additionally, the process variables are intercorrelated, and the bead width, height, and depth of penetration are highly codependent. Therefore, existing analysis cannot grant a reliable prediction model that allows the deposition (bead width, height, and penetration height) to remain within the desired level. This paper presents a novel machine learning framework for quantitatively analyzing the correlated relationship between the process parameters and deposition shape, thus providing an optimal process parameter selection to control the final deposition geometry. The proposed machine learning framework can systematically and quantitatively predict the deposition shape rather than just qualitatively as with other existing machine learning methods. The prediction model can also present the complex process-quality relations, and the determination of the deposition quality can guide the WAAM to be more prognostic and reliable. The correctness and effectiveness of the proposed quantitative process-quality analysis will be validated through experiments.

RESUMEN

Robotic additive manufacturing (AM) has gained much attention for its continuous material deposition capability with continuously changeable building orientations, reducing support structure volume and post-processing complexity. However, the current robotic additive process heavily relies on manual geometric reasoning that identifies additive features, related building orientations, tool approach direction, trajectory generation, and sequencing all features in a non-collision manner. In addition, multi-directional material accumulation cannot ensure the nozzle always stays above the building geometry. Thus, the collision between these two becomes a significant issue that needs to be solved. Hence, the common use of a robotic additive is hindered by the lack of fully autonomous tools based on the abovementioned issues. We present a systematic approach to the robotic AM process that can automate the abovementioned planning procedures in the aspect of collision-free. Typically, input models to robotic AM have diverse information contents and data formats, hindering the feature recognition, extraction, and relations to the robotic motion. Our proposed method integrates the collision-avoidance condition to the model decomposition step. Therefore, the decomposed volumes can be associated with additional constraints, such as accessibility, connectivity, and trajectory planning. This generates an entire workspace for the robotic additive building platform, rotatability, and additive features to determine the entire sequence and avoid potential collisions. This approach classifies the uniqueness of autonomous manufacturing on the robotic AM system to build large and complex metal components that are non-achievable through traditional one-directional AM in a computationally effective manner. This approach also paves the path in constructing an in situ monitoring and closed-loop control on robotic AM to control and enhance the build quality of the robotic metal AM process.

RESUMEN

Overexploitation, habitat destruction, human-driven climate change and disease spread are resulting in the extinction of innumerable species, with amphibians being hit harder than most other groups [1]. Few species of amphibians are widespread, and those that are often represent complexes of multiple cryptic species. This is especially true for range-restricted salamanders [2]. Here, we used the widespread and critically endangered Chinese giant salamander (Andrias davidianus) to show how genetically uninformed management efforts can negatively affect species conservation. We find that this salamander consists of at least five species-level lineages. However, the extensive recent translocation of individuals between farms, where the vast majority of extant salamanders now live, has resulted in genetic homogenization. Mitochondrial DNA (mtDNA) haplotypes from northern China now predominate in farms. Unfortunately, hybrid offspring are being released back into the wild under well-intentioned, but misguided, conservation management. Our findings emphasize the necessity of genetic assessments for seemingly well-known, widespread species in conservation initiatives. Species serve as the primary unit for protection and management in conservation actions [3], so determining the taxonomic status of threatened species is a major concern, especially for amphibians. The level of threat to amphibians may be underestimated, and existing conservation strategies may be inadvertently harmful if conducted without genetic assessment.

Asunto(s)

RESUMEN

The purpose of this study was to determine whether limited occurrence data for highly threatened species can provide useful spatial information to inform conservation. The study was conducted across central and southern China. We developed a habitat suitability model for the Critically Endangered Chinese giant salamander (Andrias davidianus) based on one biotic and three abiotic parameters from single-site locality records, which represent the only relevant environmental data available for this species. We then validated model quality by testing whether increased percentage of predicted suitable habitat at the county level correlated with independent data on giant salamander presence. We randomly selected 48 counties containing historical records which were distinct from, and independent of, the single-site records used to develop the model, and 47 additional counties containing >50% predicted suitable habitat. We interviewed 2,812 respondents near potential giant salamander habitat across these counties and tested for differences in respondent giant salamander reports between counties selected using each method. Our model predicts that suitable giant salamander habitat is found widely across central and southern China, with counties containing ≥50% predicted suitable habitat distributed in 13 provinces. Counties with historical records contain significantly more predicted suitable habitat than counties without historical records. There are no statistical differences in any patterns of respondent giant salamander reports in surveyed counties selected from our model compared with the areas of known historical giant salamander distribution. A Chinese giant salamander habitat suitability model with strong predictive power can be derived from the restricted range of environmental variables associated with limited available presence-only occurrence records, constituting a cost-effective strategy to guide spatial allocation of conservation planning. Few reported sightings were recent, however, with most being over 20 years old, so that identification of areas of suitable habitat does not necessarily indicate continued survival of the species at these locations.