English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The 3D printing industry faces challenges in ensuring reliable and repeatable processes, as increasing slopes can lead to defects and faults forming layer by layer in fabricated parts. Detecting these defects through post-processing quality inspections is time-consuming and laborious. To address this, a new method is proposed that incorporates a scoring scheme to quantitatively evaluate process performance using clad height data during printing. This approach aims to save time and cost while preserving the structural integrity of the part. The study develops a layer-wise point cloud processing technique to convert the incoming unordered data streams into rasterized points. By transforming raw signals into spatially equidistant points representing clad height in a 2-D Cartesian plane, a heatmap tomography of each layer is generated. Subsequently, a novel  Defects-Finder  algorithm is developed to locate and cluster surface defects based on the assigned scores. The findings demonstrate the algorithm&#x2019;s ability to identify the root cause of propagated faults, which can result in severe defects. Additionally, the study employs various statistical measures in the layer-level analysis to evaluate miniature process faults or shifts, which may get overshadowed, including cases of under and over-deposition. Through comparison and validation with physical artifacts, the proposed method proves effective in identifying and assessing process faults. Ultimately, this method enhances big data visualization for cost-effective quality control and boosts the overall productivity of additive manufacturing processes. By streamlining defect detection and performance evaluation, it addresses the challenges faced by the industry in ensuring reliable 3D printing outcomes.

In-situ Process Monitoring and Defects Detection based on Geometrical Topography with Streaming Point Cloud Processing in Directed Energy Deposition