Home3D PrintingPredict and management: DynamicPrint model-based feedforward course of management for laser powder...

Predict and management: DynamicPrint model-based feedforward course of management for laser powder mattress fusion



Through the LPBF course of, the half is subjected to continuous heating and cooling cycles. This so-called thermal historical past touches each side of the half’s high quality, from its microstructure, defects, geometric fealty, floor integrity, residual stresses and construct threat, and in the end, determines its purposeful properties, akin to power and fatigue life.

The thermal historical past is a fancy bodily phenomenon ruled by the form of the half, processing parameters (laser energy, scan velocity), machine atmosphere (gasoline circulation, laser focus), feedstock materials properties, half orientation and helps, construct circumstances (variety of elements, time between recoat), and stochastic results. Any change in these components will modify the thermal historical past and half properties.

The present follow for LPBF course of qualification depends on an empirical build- and-break strategy. Easy shapes within the type of small cubes, cylinders and mechanical check coupons are constructed underneath totally different processing circumstances, with the microstructure and metallurgical features of the elements characterised with X-ray CT, optical and electron microscopy, and mechanically examined. As soon as these coupon assessments are accomplished, the optimum processing parameters are used for constructing purposeful elements. Alas, practitioners have discovered that the processing parameters optimised from easy coupon shapes seldom switch nor scale to advanced elements, which necessitates additional rounds of build- and-break assessments. All in, the build-and-break strategy could value a number of million {dollars} and years of engineering effort. Certainly, there have to be a wiser, sooner, and extra inexpensive path towards fast half high quality qualification?

The reply lies in understanding the basic thermal physics of why coupon optimised parameters fail to scale. Even when constructed underneath equivalent circumstances, the thermal historical past of a check coupon is markedly totally different from that of a fancy impeller half. Ergo, the microstructure and properties of the 2 will differ radically. To complicate issues additional, the cross-section of a fancy half, and consequently, its thermal historical past will change between layers leading to anisotropic, inhomogeneous properties. For instance, a 41 mm tall chrome steel 316L bell crank half that tends to build up warmth within the latter layers as a consequence of poor thermal conductivity of the powder will see its backside layers cool extra quickly in comparison with the highest layers, because the construct plate absorbs warmth sooner. Certainly, LPBF practitioners innately know that sustaining fixed processing parameters throughout all layers is a recipe for unhappiness.

The answer to fast half high quality qualification and reaching constant half high quality lies not in optimising the processing parameters, however tightly controlling its thermal historical past. With this fundamental idea in thoughts my analysis group at Virginia Tech developed and carried out an strategy referred to as DynamicPrint which adjusts the processing parameters layer-by-layer to take care of a constant thermal historical past and cut back variation partly properties. The key sauce in DynamicPrint is a patented mesh-free graph concept computational thermal simulation mannequin that’s about ten occasions sooner than non-commercial finite aspect approaches, and has been experimentally validated to foretell the thermal historical past inside 5% error.

DynamicPrint makes use of the graph concept thermal mannequin to war-game the impact of adjusting course of parameters layer-by-layer on the thermal historical past. It then autonomously generates a layer-wise processing plan inside hours earlier than the half is printed to acquire a super thermal historical past. A type of digital feedforward mannequin predictive management, DynamicPrint enforces course of parameter boundaries set by the operator, e.g., the laser energy can solely be modified inside sure limits.

DynamicPrint was examined with quite a lot of chrome steel 316L elements on an EOS 290 system on the Commonwealth Middle of Superior Manufacturing, Virginia. DynamicPrint eradicated recoater crashes, deleted helps, lowered microstructure heterogeneity, and improved floor end and geometric integrity. For instance, FIGURE 1 compares the X-ray CT cross-sections of the bell crank half produced underneath uncontrolled (fixed parameter) circumstances and DynamicPrint. Backside line – for fast qualification of LPBF elements it’s important to know, observe, predict, and management the thermal historical past. This analysis was supported by the NSF, NIST, and DoD.

Click on right here to learn the total research or contact [email protected].

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