DATA-DRIVEN INSPECTION EXPERT SYSTEM FOR ADDITIVE MANUFACTURING

Code:

REACH-2022-THEMEDRIVEN-CERR_8

Domain:

Manufacturing / Industry

Description of the DVC THEME

Additive manufacturing (AM) technology is considered one of the most promising advanced technologies for manufacturing. Compared with traditional manufacturing such as CNC tools, forging, and welding, AM technology has advantages such as no need for tools or molds, high material utilization, a short product manufacturing cycle, and the ability to manufacture complex structures. All AM systems work on the same principle of building a structure additively from a substrate. Some AM processes have the ability to print various materials including polymers, metals, ceramics, and composites. AM is especially suitable for low-cost, short-cycle, rapid prototyping, or even the production of large and complex metal structures.

At present, AM alone is not fully able of producing parts with suitable mechanical properties and surface roughness that meet the requirements of most applications. AM components are known to have various defects, such as powder agglomeration, balling, porosity, internal cracks, and thermal/internal stress, which can significantly affect the quality, mechanical properties, and safety of final parts. 

Defect inspection methods are critical for reducing manufactured defects, controlling the process during manufacturing or improving the surface quality, and keeping under control the mechanical properties of AM components. Traditional defect detection technology can be combined with high-resolution visual images to optimize defect detection and fault prediction and diagnosis technology.

Sub-challenges composing this experiment:

Expected global results:

  • Optimize the printing process by understanding defects sources and occurrences during manufacturing  
  • Improve process efficiency by developing descriptive-predictive-prescriptive data-driven models 
  • Increase processes sustainability level, the total cost of ownership savings

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