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Optimizing AM process parameters for highest quality parts

Optimizing AM process (e.g. LPBF, LMD-p and LMD-w) parameters for highest quality parts

Consulting related to Data analytics Model-based product & process optimisation Process development and optimization in the context of Aerospace (Aeronautics) Automotive Industrial Machinery Manufacturing

Provided by CEA-List 10 months, 3 weeks ago (last modified 10 months, 3 weeks ago); viewed 395 times and licensed 0 times
Unit Price: EUR 20,000.00 / 1.0 Real Cost


Determining the optimal Additive Manufacturing process parameters is often a long process for high quality parts.

CEA List can support your Additive Manufacturing development towards highest quality parts.


Adjustment phases for new additive manufacturing parts are often necessary to identify the best manufacturing parameters. This service provides assistance in identifying these parameters, drawing on CEA's expertise and technical resources. To do this, the approach consists of supplying us with a set of parts (around 10) produced using different parameters likely to be selected. The CEA will then analyze each sample using non-destructive testing methods (X-ray, tomography, ultrasound, etc.) to detect any defects (porosity, cracks, etc.). This measurement will be supplemented by more detailed analyses to count and, if possible, characterize these defects. This approach will enable us to identify the best set of manufacturing parameters from among those evaluated.

Focus: Mainly high value metal parts such as in aeronautics sector.


Reduced calibration/validation phase for production launch

Process parameters optimized for high quality parts

Knowledge gain on influencing parameters in production

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Data Acquisition

In this phase, CEA will acquire data mostly by Non-destructive technologies such as radiography, CT tomography, ultrasound, thermography and electromagnetic. The manufacturer will provide CAD and material characteristics as input for simulation

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Data Analysis

Processing of acquired NDT data (e.g. segmentation) to provide qualitative and quantitative data on defects present in samples (nature, size, concentration, total volume of defects, etc.).

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