研究目的
To evaluate the use of an in-line photodiode based process monitoring system for the monitoring of the operational behaviour of the laser during the selective laser melting of porous biomaterials and to correlate this with the resultant parts mechanical performance.
研究成果
The study demonstrates a clear correlation between the reduction in laser input energy and a decrease in the load bearing capacity of porous structures. Process monitoring data can provide valuable insights into the structural integrity of parts produced by selective laser melting, potentially reducing the need for extensive post-build quality control.
研究不足
The study focuses on the effect of laser energy reduction on the mechanical properties of porous structures but does not explore the full range of potential defects or variations in process parameters that could occur in selective laser melting. The correlation between process monitoring data and mechanical properties is demonstrated, but the method may require adaptation for different geometries or materials.
1：Experimental Design and Method Selection:
The study utilized a Renishaw 500M system equipped with an in-line photodiode based process monitoring system to produce Ti–6Al–4V porous structures. The laser input energy was systematically reduced by 33%, 66%, and 100% at specific layers within the structures to assess the effect on mechanical properties.
2：Sample Selection and Data Sources:
Porous structures were produced using Ti–6Al–4V feedstock powder. The structures were designed to conform to ISO 13314 for compression testing.
3：List of Experimental Equipment and Materials:
Renishaw RenAM500M with a 500 W laser, Ti–6Al–4V grade 23 powder, Phoenix Nanotom |m| system for CT measurements, Hitachi EM4000Plus SEM for strut examination, and a Tinius Olsen 50 kN machine for mechanical testing.
4：Experimental Procedures and Operational Workflow:
Structures were built with varying levels of laser energy reduction at specific layers. Process monitoring data was collected at 100 kHz. Post-build, samples were characterized using CT and SEM, and mechanically tested.
5：Data Analysis Methods:
The process monitoring data was analyzed on a layer-by-layer basis using Matlab R2019a. The deviation between control and build data was calculated to assess the impact on mechanical properties.
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