Hybrid 3D printing process for aerospace-grade components using coarse & fine metal powders and industrial scrap material

Abstract

A hybrid 3D printing process manufactures high-performance components for aerospace, automotive, medical, and other industries using coarse metal particles (50 200 µm), fine powders (15 45 µm), and processed industrial scrap (e.g., Ti-6Al- 4V swarf, AlSi10Mg millings). A multi-chamber feeder blends the feedstocks within an argon atmosphere, employing modular hardware to circumvent proprietary system constraints. An AI-driven laser system, guided by IoT sensors (e.g., FLIR A100, Keyence LS-9000), achieves low porosity and high strength (ASTM F3056), supporting multi-metal interfaces with 50 µm gradient layers. The process substantially reduces costs and CO emissions while complying with AS9100D, ISO / TS 16949, and ISO 13485 standards, enabling applications such as turbine blades and implants across 12 industries.

Description

[0001] Description

[0002] Title of Invention: Hybrid 3D Printing Process for Aerospace-Grade Components Using Coarse & Fine Metal Powders and Industrial Scrap Material

[0003] [1] The following specification particularly describes the invention and the way it is to be performed.

[0004] Technical Field

[0005] [2] This invention relates to additive manufacturing (AM), specifically a hybrid 3D printing system for producing high-performance components for aerospace, automotive, medical, and other industrial applications. The system integrates coarse metal particles (50-200 pm), fine metal powders (15-45 pm), and processed industrial scrap (e.g., Ti-6AI-4V swarf, aluminum millings) using a multi-chamber feeder, Al-driven laser control, and loT sensors for real-time monitoring, enabling cost-effective and sustainable manufacturing.

[0006] Background Art

[0007] [3] Additive manufacturing (AM) systems for metal components, such as powder bed fusion (PBF) and directed energy deposition (DED), typically rely on fine virgin powders (15-45 pm) processed in proprietary systems, incurring high costs ($500-600 / kg) and generating 30-40% material waste. Examples include:

[0008] [4] US5837960A (Honeywell, issued November 17, 1998) describes a PBF system using fine powders, achieving densities of ~98% but limited to particle sizes <45 pm, excluding coarse particles (>45 pm), industrial scrap, or AI / loT integration. l of 15 [5] EP1568472B1 (EOS GmbH, issued May 23, 2007) discloses selective laser sintering with fine powders, reporting ~0.5% porosity but lacking coarse powder or scrap utilization and Al-driven control.

[0009] [6] IN201911007994 (ARCI, India, filed February 28, 2019) outlines a DED-based repair process using scrap-derived powders, achieving ~95% material efficiency but focused on restoration, not new component manufacturing, without coarse / fine blending or AI / loT.

[0010] [7] GKN Aerospace’s hybrid DED-subtractive process (US20180221954A1 , filed February 8, 2017) uses fine powders, costing ~$550 / kg, without scrap integration or Al control.

[0011] [8] Midhani (India) and 3D Lab (Poland) produce fine powders (US20190134707A1 , filed November 7, 2017, for Midhani), achieving sphericity >0.95 but not supporting hybrid AM with coarse particles or scrap.

[0012] [9] These systems face high material costs, feedstock restrictions (e.g., fine powders only), >0.5% porosity, and 38-42 kg CO2 / kg emissions, limiting accessibility and sustainability, which this invention addresses through coarse / fine blending, scrap processing, and AI / loT control.

[0013] Summary of Invention

[0014]

[0010] The invention provides a hybrid 3D printing process and system for manufacturing high-performance components, overcoming limitations of fine powder-based AM systems. It integrates a multi-chamber feeder processing coarse metal (50-200 pm), fine powder (15-45 pm), and industrial scrap (e.g., Ti- 6AI-4V swarf, aspect ratio >1 .5), with Al-driven laser control and loT sensors ensuring >99.3% density and <0.3% porosity. The process supports multi-metal interfaces, complies with AS9100D, ISO / TS 16949, and ISO 13485, reduces costs by 85% ($120 / kg), and lowers CO2emissions by 90% (4 kg / kg), enabling accessible metal AM across 12 industries. References to specific hardware components (e.g., FLIR A100, IPG YLR-1000) are illustrative and not limiting; equivalent components with similar specifications and functions may be substituted without departing from the scope of the invention

[0015] Technical Problem

[0016]

[0011] Existing AM systems rely on expensive fine virgin powders ($500-600 / kg), proprietary cartridges, and standard laser parameters, limiting the use of coarse particles (>45 pm) or industrial scrap due to inconsistent particle morphology and processing challenges. This results in 30-40% material waste, >0.5% porosity, 38-42 kg CO2 / kg emissions, and 60-70% of aerospace billets discarded as scrap, increasing costs and environmental impact. These limitations also hinder achieving high density (>99.3%) and low porosity (<0.3%) with heterogeneous feedstocks, increasing production complexity.

[0017] Solution to Problem

[0018]

[0012] The invention solves these problems by:

[0019]

[0013] Using a multi-chamber feeder to blend coarse metal particles (50-200 pm), fine powders (15-45 pm), and processed industrial scrap (e.g., Ti-6AI-4V swarf, AISi Mg millings).

[0014] Employing an Al-driven laser controller, trained on datasets including particle size distributions, melt pool dynamics, and defect patterns, with loT sensors (e.g., FLIR A100, Keyence LS-9000) for real-time defect correction

[0020]

[0015] Achieving >99.3% density (ASTM F3056), <0.3% porosity, and multi-metal interfaces with 50 pm gradients, with tensile strength >1 ,100 MPa (ASTM E8).

[0021]

[0016] The system uses modular hardware (e.g., ISO-compliant interfaces) and Al algorithms , with Al trained on experimental and simulated datasets of particle size, melt pool dynamics, and defect patterns, reducing costs by 85% ($120 / kg vs. $600 / kg) and CO2emissions by 90% (4 kg / kg vs. 40 kg / kg), enabling sustainable, high-performance AM for aerospace, automotive, and medical applications.

[0022] Advantageous Effects of Invention

[0023]

[0017] The invention enables cost-effective, sustainable AM by reducing material costs by ~80-90% and environmental impact while achieving high-performance metrics for aerospace, automotive, and medical applications. It supports multimetal components and bypasses proprietary system limitations, broadening accessibility across 12 industries, including defense, space, and renewables.

[0024] Brief Description of Drawings

[0025]

[0018] The drawings illustrate the invention’s technical features.

[0026]

[0019] [Fig. 1] Schematic of the hybrid 3D printing system, showing the multichamber feeder (10, 10a-d), print head (12), laser system (13-14, 21 ), Al controller (15), loT sensors (16), enclosure (17), workpiece (18), build platform (19), and tool head (20), as recited in claims 9-11 .

[0020] [Fig. 2] Cross-sectional view of a multi-metal interface with Ti-6AI-4V core (22), AISiWMg coating (23), and gradient layer (24, 24a, 50 pm), as recited in claim 6.

[0027]

[0021] [Fig. 3] Flowchart of the AI / loT control algorithm, depicting input data (25), Al processing (26), and parameter adjustment (27), supporting claims 1 and 13.

[0028] Description of Embodiments

[0029]

[0022] The invention provides a hybrid 3D printing process and system for manufacturing components compliant with AS9100D, ISO / TS 16949, and ISO 13485 standards, using: Coarse metal (50-200 pm) (e.g., Ti-6AI-4V swarf) or virgin sources. Fine powder (15-45 pm) from virgin or recycled material. Industrial scrap (e.g., aluminum millings) processed on-demand.

[0030]

[0023] The system integrates a multi-chamber feeder, Al-driven laser control, and loT sensors, achieving: Density: >99.3% (ASTM F3056), Porosity: <0.3%, Tensile strength: >1 ,100 MPa (ASTM E8), Cost: 85% reduction ($120 / kg vs. $600 / kg), CO2emissions: 90% reduction (4 kg / kg vs. 40 kg / kg).

[0031]

[0024] Feedstock Preparation

[0032]

[0025] Coarse Metal (50-200 pm): Sourced from Ti-6AI-4V swarf (e.g., Boeing 737 waste), processed via plasma spheroidization (Tekna TekSphero, 50-100 kW, Ar 50-80 SLPM, sphericity >0.90, ASTM B213), cleaned (5% HCI + 1% HF), sieved to 50-200 pm (D50: 100 pm), and stored in chamber 10a (Fig. 1 ).

[0033]

[0026] Fine Powder (15-45 pm): Virgin or recycled Ti-6AI-4V (sphericity >0.95, ASTM B294) for medical implants (ASTM F136). Stored in chamber 10b (Fig. 1 ).

[0034]

[0027] Industrial Scrap: AISiWMg millings processed via plasma spheroidization (40-60 kW) and sieved to 45-150 pm. Stored in chamber 10c (Fig. 1 ).

[0035]

[0028] Printer Configuration

[0029] Multi-Chamber Feeder (10, Fig. 1): Includes chambers 10a (coarse), 10b (fine), 10c (scrap), and blend output nozzle 10d, operating in an Ar-purged environment (O2<100 ppm, NASA-STD-6016).

[0036]

[0030] Laser System: IPG YLR-1000 (1 kW, 50-150 pm spot). SCANLAB QS-30 (7 m / s).

[0037]

[0031] loT Sensors: FLIR A100 (melt pool monitoring). Keyence LS-9000 (particle analysis).

[0038]

[0032] AI / loT Control: Al trained on datasets of particle size, melt pool dynamics, and defect patterns, adjusting laser power (±50W) and scan speed (±100 mm / s) in <10 ms (Fig. 3).

[0039]

[0033] Multi-Metal Printing: Produces gradient layers (50 pm, 50% Ti-6AI-4V / 50% AISi Mg) with bond strength >80% of base material (ASTM D1002, Fig. 2).

[0040]

[0034] Comparison of Feedstocks: Coarse particles reduce costs but require precise laser control for density; fine powders ensure surface quality for medical applications; scrap enhances sustainability but demands consistent spheroidization.

[0041] Examples

[0042]

[0035] Aerospace Turbine Blade (Claim 7): Uses 80% Ti-6AI-4V coarse (75-150 pm), 20% fine (15-45 pm). Parameters: 550W, 800 mm / s, 90 pm hatch, Ar (O2<50 ppm). Post-processed via annealing (650°C / 2h, vacuum). Metrics: 1 ,050 MPa yield, 107cycles at 500 MPa, $120 / kg.

[0043]

[0036] Rocket Nozzle (Claim 2): Uses 100% Ta coarse (60-180 pm). Parameters: 600W, 700 mm / s, HIP (920°C / 100 MPa / 3h). Metrics: 99.8% density, NASA-STD- 6016 compliant.

[0037] Automotive Suspension Component (Claim 6): Uses 70% Ti-6AI-4V coarse, 30% fine. Parameters: 520W, 850 mm / s, anneal (600°C / 1 .5h). Metrics: 1 ,080 MPa yield, 500h salt spray (ASTM B117), $130 / kg.

[0044]

[0038] Medical Hip Implant (Claim 8): Uses 100% Ti-6AI-4V ELI fine (ASTM F136). Features 20-30% porosity lattice, Ra <0.8 pm. Parameters: Cleanroom, HIP (900°C / 100 MPa / 2h). Metrics: 950 MPa yield, ISO 10993-5 / 6 compliant.

[0045] Industrial Applicability

[0046]

[0039] The invention applies to industries including aerospace (e.g., turbine blades, saving ~$400 / kg), automotive (e.g., suspension components), medical (e.g., implants), defense, and space, offering cost-effective, sustainable AM with reduced supply chain dependency.

[0047] Reference Signs List

[0048]

[0040] 10: Multi-chamber feeder

[0049]

[0041] 10a: Coarse metal chamber

[0050]

[0042] 10b: Fine powder chamber

[0051]

[0043] 10c: Industrial scrap intake

[0052]

[0044] 10d: Blend output nozzle

[0053]

[0045] 12: Print head

[0054]

[0046] 13: Galvo scanner

[0055]

[0047] 14: Fiber laser

[0056]

[0048] 15: Al controller

[0057]

[0049] 16: loT sensors

[0058]

[0050] 17: Enclosure

[0059]

[0051] 18: Workpiece

[0060]

[0052] 19: Build platform

[0053] 20: Tool head

[0061]

[0054] 21 : Laser system

[0062]

[0055] 22: Ti-6AI-4V core

[0063]

[0056] 23: AISH OMg coating

[0064]

[0057] 24, 24a: Gradient layer

[0065]

[0058] 25: Input data

[0066]

[0059] 26: Al processing

[0067]

[0060] 27: Parameter adjustment

[0068] Patent Literature

[0069]

[0061] PTL1: US5837960A, Honeywell.

[0070]

[0062] PTL2: EP1568472B1 , EOS GmbH.

[0071]

[0063] PTL3: IN201911007994, ARCI

[0072]

[0064] PTL4: US20180221954A1 , GKN Aerospace

[0073]

[0065] PTL5: US20190134707A1 , Midhani

Claims

Claims

1. A hybrid additive manufacturing process for producing high-performance components, comprising: a) providing a multi-chamber feeder configured to blend coarse metal particles (50-200 pm), fine metal powders (15-45 pm), and processed industrial scrap including Ti-6AI-4V swarf or AISiWMg millings into a feedstock; b) blending the coarse metal particles, fine metal powders, and processed industrial scrap in the multi-chamber feeder to form a feedstock; c) depositing the feedstock onto a build platform using a print head; d) selectively melting the feedstock with a laser system controlled by an Al-driven controller trained on experimental and simulated datasets of particle size distributions, melt pool dynamics, and defect patterns, wherein the Al-driven controller adjusts laser power and scan speed in real-time based on data from loT sensors; and e) forming a component with a density of at least 99.3% (ASTM F3056), porosity less than 0.3%, and tensile strength of at least 1 ,100 MPa (ASTM E8).

2. The process of claim 1 , wherein the coarse metal particles comprise Ti- 6AI-4V swarf processed by plasma spheroidization to achieve a sphericity of at least 0.90 (ASTM B213).

3. The process of claim 1 , wherein the fine metal powders comprise Ti-6AI- 4V conforming to ASTM F136 for medical implants.

4. The process of claim 1 , wherein the industrial scrap comprises AISiWMg millings processed to a particle size of 45-150 pm.

5. The process of claim 1 , wherein the loT sensors include a FLIR A100 for melt pool monitoring and a Keyence LS-9000 for particle size analysis, providing real-time data to the Al-driven controller.

6. The process of claim 1 , further comprising forming a multi-metal interface with a gradient layer of 50 pm thickness comprising a transition between Ti-6AI-4V and AISiWMg, achieving a bond strength of at least 80% of the base material (ASTM D1002).

7. The process of claim 1 , wherein the component is an aerospace turbine blade formed using 80% coarse Ti-6AI-4V particles (75-150 pm) and 20% fine Ti-6AI-4V powders (15-45 pm), with parameters including 550W laser power, 800 mm / s scan speed, and 90 pm hatch spacing in an argon environment with oxygen content less than 50 ppm.

8. The process of claim 1 , wherein the component is a medical hip implant formed using 100% Ti-6AI-4V ELI fine powders (15-45 pm) conforming to ASTM F136, with a lattice structure having 20-30% porosity and surface roughness Ra less than 0.8 pm.

9. A hybrid additive manufacturing system for producing high-performance components, comprising: a) a multi-chamber feeder configured to blend coarse metal particles (50- 200 pm), fine metal powders (15-45 pm), and processed industrial scrap including Ti-6AI-4V swarf or AISiWMg millings into a feedstock; b) a print head configured to deposit the feedstock onto a build platform; c) a laser system including a fiber laser and galvo scanner for selectively melting the feedstock; d) an Al-driven controller trained on experimental and simulated datasets of particle size distributions, melt pool dynamics, and defect patterns, configured to adjust laser power and scan speed in real-time; e) loT sensors configured to provide real-time data to the Al-driven controller; and f) an enclosure maintaining an argon environment with oxygen content less than 100 ppm,wherein the system produces components with a density of at least 99.3% (ASTM F3056), porosity less than 0.3%, and tensile strength of at least 1 ,100 MPa (ASTM E8).

10. The system of claim 9, wherein the multi-chamber feeder includes: a) a first chamber (10a) for coarse metal particles; b) a second chamber (10b) for fine metal powders; c) a third chamber (10c) for processed industrial scrap; and d) a blend output nozzle (1 Od) for delivering the feedstock.

11. The system of claim 9, wherein the laser system comprises an IPG YLR- 1000 fiber laser with a 50-150 pm spot size and a SCANLAB QS-30 scanner operating at up to 7 m / s.

12. The system of claim 9, wherein the components include a multi-metal interface with a 50 pm gradient layer between Ti-6AI-4V and AISi Mg, achieving a bond strength of at least 80% of the base material (ASTM D1002).

13. The process of claim 1 , wherein the Al-driven controller uses an algorithm trained on at least 10,000 experimental and simulated datasets to adjust laser parameters in real-time.

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