Method for the quality assessment of a component produced by means of an additive manufacturing method

Patent metadata

Field	Value
Publication number	US-9964496-B2
Application number	US-201514827346-A
Country	US
Kind code	B2
Filing date	Aug 17, 2015
Priority date	Aug 21, 2014
Publication date	May 8, 2018
Grant date	May 8, 2018

Abstract

The invention relates to a method for the quality assessment of a component produced by means of an additive manufacturing method. In the course of the method, it is checked first of all whether the component violates predetermined absolute limits in order to rule out the existence of serious malfunctions in the additive manufacturing process. Subsequently, a component-dependent targeting process is determined. On the basis of this targeting process, the limits for deviations are established and deviating actual values of the component are isolated and assessed by means of various parameters.

First claim

What is claimed is:

1. A method for the quality assessment or the quality class of a component for a machine produced by means of an additive manufacturing method, comprising:
a) preparation of a first data set, wherein the first data set comprises absolute limit values that each characterize a maximum allowed range of values at an assigned component position of a machine component being produced;
a1) providing an optical tomography device as an acquisition device;
b) acquisition of a second data set by means of the acquisition device that captures images of the machine component being produced, wherein the second data set comprises actual values corresponding to the first data set, which characterize the assigned component position of the machine component being produced;
c) comparison of the first data set and the second data set by means of a computing device and
c1) classification of the machine component as being qualitatively fundamentally not OK if at least one actual value lies outside its assigned maximum allowed range of values; or
c2) classification of the component as being qualitatively fundamentally OK if no actual value lies outside its assigned maximum allowed range of values; and
if the machine component has been classified as being qualitatively fundamentally not OK, it can be concluded that there is a serious breakdown or malfunction;
if the machine component has been classified as being qualitatively fundamentally OK:
d) preparation of a third data set, which comprises mean values that are determined from a plurality of actual values of the second data set by means of the computing device, wherein the plurality of actual values characterize an interrelated machine component region composed of a plurality of component positions;
e) determination of at least one best-fit function that is dependent on a geometry of the machine component on the basis of the third data set by means of the computing device;
f) determination of threshold values that depend on the geometry of the machine component by means of the computing device, wherein the threshold values characterize an allowed range of scatter of the actual values around target values predetermined by the best-fit function;
g) checking by means of the computing device whether at least one actual value lies outside of the range of scatter characterized by the threshold values, and
g1) if no actual value lies outside of the range of scatter, the machine component is classified as being qualitatively OK; or
g2) if at least one actual value lies outside the range of scatter, then all actual values that lie outside of the range of scatter are compiled to obtain a fifth data set, and a quality of the machine component is assessed on the basis of the fifth data set and at least one predetermined quality criterion.

2. The method according to claim 1 , wherein at least one absolute limit value of the first data set is predetermined on the basis of an empirical value and/or on the basis of a measured value of a reference component of flawless structure.

3. The method according to claim 1 , wherein the additive manufacturing method and/or the acquisition device is checked if the machine component is classified in step c1) as being fundamentally not OK.

4. The method according to claim 1 , wherein the mean values of the third data set are determined as an arithmetic mean and/or as a mode and/or as a median and/or as a geometric mean and/or as a harmonic mean and/or as a quadratic mean and/or as a cubic mean.

5. The method according to claim 1 , wherein the mean values of the third data set are determined on the basis of the actual values that characterize an irradiated layer area of the machine component and/or a region of an irradiated layer area of the machine component.

6. The method according to claim 1 , the actual values in the fifth data set are weighted initially on the basis of at least one weighting factor for assessment of the quality of the machine component.

7. The method according to claim 6 , wherein, as weighting factor, an indicator index and/or a number of indicators in the machine component and/or a distance from an adjacent indicator and/or a position in the machine component are or is used.

8. The method according to claim 1 , wherein as quality criterion, a color value and/or a gray-scale value and/or a size and/or a shape is used.

Assignees

• MTU Aero Engines AG

Inventors

• Hess Thomas
• Ladewig Alexander

Classifications

• G05B2219/32213

  If parameter out of tolerance during limited time, accept product on condition · CPC title

• G01N2201/12

  Circuits of general importance; Signal processing · CPC title

• G05B2219/32212

  If parameter out of tolerance reject product · CPC title

• G05B19/41875 Primary

  characterised by quality surveillance of production · CPC title

• G05B2219/32191

  Real time statistical process monitoring · CPC title