AM PravaH - Taking additive manufacturing techniques to a new height !
A National Strategy for Additive Manufacturing was unveiled by the Indian Ministry of Electronics and Information Technology (Meity) on February 24th, 2022. The objective was to grow additive manufacturing's market share by 5% globally by 2025. The GDP would increase by $1 billion as a result.
Just a quick sneak peek into few objectives of the strategy:
The Indian government has seen the enormous potential of additive manufacturing, and as a result, new initiatives for R&D, skilling, and start-up incubation have been implemented across the country. However, for India to reap the rewards of additive manufacturing, there is a great need for progress in this area.
Additive manufacturing techniques include Powder Bed Fusion (PBF), Direct Energy Deposition (DED), Fuse Deposition Modelling (FDM), etc. These techniques need alterations in the input parameters based on a particular application. Anomalies are formed inside the produced parts if the input parameters are not selected meticulously. Eliminating anomalies in the AM processes is the solution to all of the major issues. Some of these anomalies are listed below:
Lack of fusion: The primary cause of the creation of this defect is insufficient overlap between the passes. Laser properties and scanning strategy impact the mechanism of defect formation. Based on the desired temperature field during the deposition process, these parameters must be chosen. As a result, a thorough understanding of the temperature field is essential.
Keyhole Collapse: When a high energy density heat source is used a deep V-shaped melt pool is formed by vaporising the elements within the pool. Without careful control of keyhole melting, it can become unstable and repeatedly form and collapse which leads to void formation after solidification. Hence careful selection of parameters such as source energy, power and density, travel speed, spot size needs to be considered for a stable melt pool.
Gas Porosity: It is caused because of gas entrapment, supersaturation of dissolved gases and chemical reactions that produce gaseous species within the molten pool. High cooling rates as well as low cooling rates can lead to gas porosity. In case of rapid cooling of the molten pool, gases formed during melting don't get enough time to escape which leads to their entrapment. However in case of slow cooling rate these pores float to the surface to form surface connected porosity. Sometimes these pores grow in size and combine with the neighbouring pores to form a larger pore.
Thermal Cracking: Thermal cracking is driven by temperature gradient and initiated by the interaction between metallurgical and mechanical factors. Factors influencing thermal cracking are solidification rate, backfilling, dendrite coherency, eutectic fraction, surface tension and grain morphology. Out of these parameters, dendrite coherency, eutectic fraction, surface tension and grain morphology are microscale parameters. Hence, one needs to study microscale properties of the material to understand the defect mechanism.
Balling: It is caused due to the spattering of the liquid material from the melt pool. When the melt pool is unstable, outward forces exceed inward forces causing the molten material to spatter. When these drops of molten material hit on the solid surface around them they solidify and take a spherical shape with the point of contact being tangent to the surface of contact. Since a stable melt pool is the key, the parameters that affect the melt pool stability plays a role in controlling the balling effect.
As it is evident from the above discussion, it is important to carefully select the parameters for the additive manufacturing process. Random selection of parameters leads to an unstable melt pool that gives rise to problems shown in the figure. Hence the end goal is to achieve a stable melt pool. There are two ways this can be carried through experiments or computational simulations.
Experiments require permutations and combinations of parameters which are costly. But It can be achieved with cheaper price using computational simulations once the methodology has been validated with an experimental case study.
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What is AM PravaH?
AM PravaH is the world’s first all inclusive 3D computational software for various additive manufacturing processes such as LPBF, wire and powder DED, binder jetting, etc. As mentioned before, laser power, scanning speed, laser beam radius, layer thickness, preheat temperature, hatch spacing, substrate dimensions, specific heat, thermal conductivity, density, solidification range and Youngs modulus are crucial parameters for a stable melt pool. Carrying out experiments with these 12 variables alone would require a minimum 4096 tests (i.e. 2^12). This would make the budget exorbitantly high. But AM PravaH lets the users manipulate these parameters with ease and provides converged solutions with minimal time and cost.
Heating of the elements in the additive manufacturing process leads to changes from Part scale level to microscale level. Hence, the property changes taking place at microscale level needs to be taken into account for macroscale level and part scale level analysis. AM PravaH couples physics at multiple scales to provide the most accurate solution.
At microscale level, users can analyse grain morphology such as change in the grain size and shape due to heating. Then is it possible to visualise euler angles as well as columnar and equiaxed structures. At Macroscale level, users can analyse and visualise melt pool dynamics, particle dynamics, multiphase interface tracking, phase change, thermodynamics, surface tension, marangoni effect and laser-particle tracking. At the partscale level, users can perform finite element analysis and thermal analysis.
Words hold weight only when backed by numbers. Hence, we have a dedicated verification and validation (V&V) manua l for various industry relevant case studies. In all the V&V case studies, the average percentage accuracy achieved by AM PravaH is above 95%.
Physics modelling is not an optional step but an essential part that needs to be incorporated in between the design to production pipeline. By doing so companies can be greatly benefited in terms of better build quality and reduction in manufacturing cost. Physics modelling using AM PravaH thus bridges the gap between the design phase and production phase by optimising the manufacturing process.
Paanduv Applications has always been supportive of sustainable innovations. Artificial Intelligence (AI) is one of the major areas of interest for most of the companies in the world and hence along with the physics based modelling module, AM PravaH comes with an AI module. Users can feed data to this AI module from AM PravaH’s physics based modelling simulation to make further predictions. Furthermore, if the user has data from experiments then he/she can also feed this data to the AI module to make further predictions.
To summarise, AM PravaH provides an end-to-end computational modeling solution. It improves economics due to early defect prediction, improves yield quality, improves cycle time, reduces waste and provides in depth knowledge of the process. The AI module of the AM PravaH reduces computational time exponentially giving real time results. This allows for faster decision making providing a business advantage. Flexibility of selecting a wide range of parameters allows for utmost accuracy. It models all the 4 Phases included in the 3D printing making it a truly all inclusive 3D computational simulations software.
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