Patent Description:
Additive manufacturing (AM) is a process of joining materials to make objects from 3D model data, typically layer-upon-layer, as opposed to subtractive manufacturing techniques, such as milling or cutting. AM processes are generally classed according to the machines and technologies used, e.g. laser or extrusion, and the state of the raw material used, i.e. liquid-based, powder-based, or solid-based. The different processing conditions encountered in AM produce different mechanical properties and surface finishes, particularly compared to traditional manufacturing techniques. For example, the surface finish of injection moulding is determined by the mould, whereas the outer surface of AM parts has a rough feel and is visually dull as a result of the layer-upon-layer process. Such a surface finish is often undesirable, particularly if the part is intended for an aesthetic application. Furthermore, surface roughness may adversely affect the mechanical performance of the AM part, such as tear strength, tensile and/or bending strength, Young's modulus of the material, and fracture strain. Additionally, a rough surface finish may be difficult to clean, may stain easily, or may cause undesirable damage to adjacent parts in use. As such, AM parts are often post-processed after manufacture in an attempt to smooth the often undesirable rough outer surface and sometimes also to remove support structures used in the manufacturing process. It is therefore desirable to determine the roughness of an AM part, and in turn the amount of smoothing required to achieve a desired finish for a particular application, before the part is post-processed. Conventional surface measurement (e.g. roughness) methods require a considerable amount of time to produce results, especially for AM parts due to their diverse variety. Therefore, it is desirable to efficiently and accurately determine a surface condition of AM parts, particularly AM polymer parts having applications ranging from aerospace and automotive to consumer and medical, as part of an automated printing and/or smoothing process.

It is an aim of certain embodiments of the present invention to provide a non-contact system and method for efficiently, consistently and accurately determining a surface condition of an AM polymer part relative to a reference surface condition.

It is an aim of certain embodiments of the present invention to provide a non-contact system and method for analysing, recognising, and categorising a wide range of different polymer surfaces.

It is an aim of certain embodiments of the present invention to provide a non-contact system and method for providing surface information to a controller during part manufacture, such as AM part printing or post-processing.

According to a first aspect of the present invention there is provided a method of determining a condition of a surface of an additively manufactured polymer part, as defined in appended claim <NUM>.

Optionally, the reference similarity value is a limit of a reference range of a plurality of different reference ranges each corresponding to a different surface condition. Optionally, the method further comprises analysing the test parameters to determine a reduced dimension value.

Optionally, the reduced dimension value is between <NUM> and <NUM>.

According to the invention, the method comprises normalising the captured image to remove illumination geometry and colour effects.

Optionally, the similarity value is calculated based on an angle between two similarity vectors or a Euclidian distance between the two similarity vectors.

According to a second aspect of the present invention there is provided a computer program as defined in appended claim <NUM>.

According to a third aspect of the present invention there is provided a method of processing an additively manufactured polymer part, comprising:.

Optionally, the apparatus is for post-processing the additively manufactured polymer part.

According to a fourth aspect of the present invention there is provided a system for determining a condition of a surface of an additively manufactured polymer part, comprising:.

Certain embodiments of the present invention will now be described with reference to the accompanying drawings in which:.

As illustrated in <FIG>, a system <NUM> according to certain embodiments of the present invention includes a metrology device <NUM> in wired or wireless communication with a controller <NUM>, such as a desktop computer, laptop, tablet, and/or mobile phone configured to receive information from the metrology device <NUM> and to display a graphical user interface (GUI) to allow a user to interact with and control the metrology device <NUM>. The metrology device <NUM> includes two main modules; a microscope module <NUM> and an illumination module <NUM>.

As illustrated in <FIG>, <FIG> and <FIG>, the microscope module <NUM> includes two infinity-corrected objective lenses <NUM>,<NUM> to individually magnify an image of an AM part surface <NUM> and form the surface image at infinity. The objective lenses provide 4x and 20x magnification respectively. The 4x objective lens <NUM> is used to capture high spatial wavelength features of a surface (surface form) and the 20x objective lens <NUM> is used to capture low spatial wavelength features of a surface (surface texture). Other suitable objective lens configurations, such as <NUM>× magnification, can also be envisaged. The microscope module <NUM> further includes an objective changer <NUM> to allow a user to select one of the objective lenses <NUM>,<NUM> based on the surface being imaged and analysed. The microscope module <NUM> further includes a beam splitter <NUM> coaxially located above the objective lenses <NUM>,<NUM> to direct light transmitted from the illumination module <NUM> to the AM part surface <NUM> and then to pass the reflected light from the surface to a camera <NUM> via a tube lens <NUM>. The camera <NUM> includes a Complementary Metal Oxide Semiconductor (CMOS) sensor configured to capture an image of the AM part surface <NUM> and send the image to the controller <NUM> for processing as described further below. The camera <NUM> aptly has a resolution of around <NUM> × <NUM> pixels and a frame rate of around <NUM> fps, which can be increased to <NUM> fps with limited field of view. The camera <NUM> is a colour camera with progressive scan capability. The camera <NUM> is USB <NUM>, or the like, configured for transferring data to the controller <NUM>. The tube lens <NUM> has a focal length, f, of <NUM> to form the image from the selected objective lens <NUM>,<NUM> to the CMOS sensor <NUM>. A focal length of <NUM> was selected to provide a relatively compact device with an overall length of less than <NUM> but other suitable focal lengths and overall device sizes may be used depending on the desired application. The tube lens <NUM> is a doublet lens that can reduce chromatic aberration from the white light source of the illumination module <NUM>. The tube lens <NUM> has a transmission spectrum from around <NUM> to around <NUM>. Other suitable camera and/or lens configurations can be envisaged without detracting from the scope of the present invention. The microscope module <NUM> also includes a coupler <NUM> to connect the axial illumination module <NUM> to the beam splitter <NUM> of the microscope module <NUM>. The camera <NUM> and the tube lens <NUM> are each mounted in a respective cage block <NUM>,<NUM>, and cage bars <NUM> connect the blocks <NUM>,<NUM> and in turn fix a distance between the camera and tube lens. The distance between the CMOS sensor and the tube lens is <NUM> to correspond to the focal length, f, of the tube lens.

The illumination module <NUM> includes a white light source <NUM>, e.g. an LED, to provide axial illumination to the surface <NUM> being imaged. A suitable LED may have a total power output of around 250mW with an intensity of around 3mW/cm<NUM>. Aptly, the emission is filtered to have a spectrum of around <NUM> to <NUM>. A diffuser lens <NUM> is provided between the light source <NUM> and the beam splitter <NUM> to improve the distribution of light from the light source so that the light has a uniform illumination intensity across the field of view. The transmission spectrum of the diffuser lens <NUM> is around <NUM> to <NUM>. The light source <NUM> and diffuser lens <NUM> are suitably mounted and coupled in a fixed spaced relationship to the beam splitter <NUM>.

As illustrated in <FIG>, two light paths exist when the device <NUM> is in use. The solid lines <NUM> represent light passing from the light source <NUM> and into the beam splitter <NUM> to be reflected <NUM> degrees and directed substantially perpendicularly on to the surface <NUM> to be imaged. This light is then reflected back to the objective lens <NUM>,<NUM> and in view of the objective being infinity-corrected, the light beam coming from the objective is plane parallel. To form an image of the surface <NUM> from the parallel beam, the tube lens <NUM> is used to focus the parallel beam to the CMOS sensor of the camera <NUM>. The CMOS sensor is spaced apart from the tube lens by <NUM> since the focal length of the tube lens is <NUM>. The back-focal plane (BFP) of the objective is also placed <NUM> away from the tube lens, i.e. the BFP is coincident with the objective focal plane. The dashed lines <NUM> represent light that is reflected in parallel from the object surface <NUM> to the objective lens <NUM>,<NUM>, which is then focussed on the BFP of the objective, and which is then made plane parallel to the CMOS sensor by the tube lens <NUM> (the ray coming from the focal plane of the tube lens).

A key principle of the method according to certain embodiments of the present invention is a machine learning approach where the controller is trained to recognise a particular set of surfaces. That is achieved by analysing and categorising the surface texture and patterns (surface descriptors). According to certain embodiments of the present invention, the results are used to categorise the surfaces into types (e.g. type <NUM>, type <NUM>, type <NUM>, etc) to thereby determine whether or not the AM part has been processed (i.e. smoothed) or not, or if it requires further processing/smoothing. The controller uses these types as a reference to recognise a particular surface and assign that surface to an appropriate type.

A method of determining a surface condition of an AM part according to certain embodiments of the present invention includes a machine learning approach. Machine learning includes training and testing steps which are described further below.

Machine training is carried out to 'train' the algorithm to recognise a particular surface. The surface images used for the training process are taken at different locations across a polymer AM part of a certain material, to ensure unbiased training. The training data is stored and can later be referred to for recognition of that particular surface, i.e. machine training only needs performing once.

The testing procedure is surface recognition of the parts using already prepared training data. The testing is done to confirm the training data is sufficient to recognise the particular surface. From the testing step, a decision can be made whether the training process is enough or needs more training process (with more training data or a different training parameter, i.e. numbers of reduced dimension).

The machine learning calculations according to certain embodiments of the present invention are carried out in the following way:
Firstly, the controller <NUM> is trained to recognise particular surfaces and/or particular surface conditions: each of a plurality of descriptor parameters X (as listed below) is evaluated by its variance (Eigen values λ) and covariance (S). The averaged parameter X is first calculated, followed by the matrix covariance: <MAT> <MAT>.

After that, variance (Eigen values λ) of the projected data is calculated: <MAT>.

Hence:
<IMG>
where N is the number of training data (the initial set of data used to train the machine, at least <NUM> training data is recommended), m is dimension of each data, S is variance-covariance matrix, λ is Eigen values of S, Umk is Eigen vectors of S, and K is reduced dimension. The latter parameter reduced dimension is the number of principal components (PC) that are to be considered, therefore reduced dimension can be any number between <NUM> and <NUM> (see <NUM> surface descriptors listed below). The larger the number of the reduced dimension, the better the learning process for the surface detection capability. However, the larger the number of the reduced dimension, the longer the training time. It has been found that a reduced dimension of around <NUM> is optimum.

After the Eigen values are calculated, the descriptors X are used to define the similarity of the surfaces. Firstly, weight matrix ωk is calculated: <MAT>.

Weight matrices of different parameters together form similarity vector <MAT>: <MAT>.

Finally, the similarity of two surfaces can be calculated in two ways:.

After the above 'training' steps, the controller saves files consisting of the Eigen values and vectors of S matrix, and similarity values for each type of AM polymer surface and then uses these training files to recognise and categorise surfaces.

The obtained similarity values are categorised according to the defined threshold value, e.g. if the similarity value falls below the defined threshold, the surface is categorised to be the same type with respect to a reference surface. The threshold value is defined as five times the value of similarity value defined from the training process. Surfaces can also be categorised according to the reference threshold value, e.g. a surface with a similarity of <NUM> - <NUM> compared to a 'Type <NUM>' surface may be assigned to be a 'Type <NUM>' surface; a similarity of <NUM> - <NUM> may be assigned to be a 'Type <NUM>' surface; and a similarity of <NUM> - <NUM> may be assigned to be a 'Type <NUM>' surface, etc. wherein the different 'types' of reference surface may be categorised with respect to their surface roughness, for example.

At this stage the testing step is done to confirm surfaces can be recognised from the gathered training data. If needed, the training can be repeated with higher number of training data N.

In use, the machine learning process is as follows:.

A method <NUM> of 'recognising' a surface condition of an AM part will now be described in more detail with reference to <FIG>. At step <NUM>, 2D image data of the surface <NUM> is captured by the CMOS sensor of the metrology device <NUM> as illustrated in <FIG>. The image data is sent to the controller by wired or wireless communication and, at step <NUM>, is normalized. Normalization is performed to remove the variation of illumination geometry and illumination colour. Conventional normalization methods include histogram equalisation and gamma illumination correction.

At step <NUM>, a plurality of descriptors is determined based upon the normalized image data. The controller and method are configured to detect and analyse <NUM> different parameters, or `surface descriptors', which are correlated to different surface conditions (see machine learning process described above). The parameters are divided into two groups: colour-related (<NUM> parameters) and texture-related (<NUM> parameters). These are as follows:.

At step <NUM>, the above descriptors are analysed according to the Principle Component Analysis (PCA) method which is an unsupervised learning method which reduces the dimensionality of data. The reduced data is plotted along its principal axes (as shown in <FIG>). PCA maximises variance in data along their principal axes.

At step <NUM>, the analysed data is compared against different stored training data and in case of a match, the surface is "recognised" to be of a particular type.

A method of analysing and categorising an AM polymer part will now be described by way of example only to further describe certain embodiments of the present invention.

Nylon-<NUM> and thermoplastic polyurethane (TPU) parts having different surface grades (rough to smooth: Type <NUM>, Type <NUM>, Type <NUM>, Type <NUM> and Type <NUM>, see <FIG>) are used, wherein the Type <NUM> surface is used as a reference.

First, the machine learning process is carried out as described above to produce similarity values with respect to the reference surface (Type <NUM> in this example). The obtained parameters are stored as a. txt file named "Material_learning_data", for example "Polyamide12_learning_data".

After the parameters representing the different surface conditions are stored, in for example a memory of the controller <NUM>, the system <NUM> can then be used to recognise the surfaces of an AM part made from the same material as the part used during learning process:.

The system and method according to certain embodiments of the present invention may aptly be used on an AM polymer part after or during a controlled post-processing, e.g. smoothing, operation to determine the surface condition of the AM part and in turn the amount of processing required to achieve a desired surface condition. For example, if the desired surface roughness has not been achieved, the processing parameters of a controlled post-processing operation may be adjusted responsive to feedback from the controller <NUM> of the system <NUM> to a control unit of the post-processing apparatus and the AM part/s may be processed again until the desired surface condition is achieved. Such feedback may also allow the post-processing apparatus to learn and improve in terms of efficiency and automation.

For validation and calibration purposes, four different simulated surfaces representing surfaces with different features, and in turn different surface conditions, were generated and analysed (see <FIG>). This demonstrates that different surfaces can be distinguished by the surface describing parameters in the PCA space according to certain embodiments of the present invention. Type <NUM> surface was considered rough (non-processed), whereas Type <NUM> was considered smooth (processed). Type <NUM> and Type <NUM> surfaces fall in between Type <NUM> and Type <NUM>. Type <NUM> surface was taken as a reference surface to be compared to all other surfaces. The results of the surface classification are shown in <FIG>. The spatial difference along the parameter axis allows a user and/or the controller to distinguish between the surfaces. It can be seen in <FIG> that by using PC3 and PC1 (see algorithms above) the surfaces can be well distinguished.

Testing of the pixel noises of the CMOS sensor was done to understand the impact of such noises on the measurements. Each CMOS sensor pixel can have a value between <NUM> and <NUM>, and the aim is to monitor the noise of each pixel. The pixel intensity on the CMOS sensor was recorded overtime. In total <NUM> pixel intensities were recorded successively. The results of the sensitivity test are illustrated in <FIG>. It can be seen that the pixel noise is around ±<NUM> pixel value (<NUM>% confidence interval).

The effect of pixel intensity noise on the calculation of the similarity value was also measured. Such a test was carried out by selecting Type <NUM> image (see <FIG>) and applying varying levels of noise on the image pixels. The results are illustrated in <FIG>. A considerable increase in similarity value (decrease in image similarity) can be observed at a pixel noise of around <NUM>. Therefore, pixel noise of around ±<NUM> pixel value does not significantly impact similarity value and also the measurement results.

The method according to certain embodiments of the present invention uses unique parameters associated with different surface conditions of a wide variety of AM polymers to recognise and categorise the surface. Such polymers include, for example, Polyamide <NUM>, Polyamide <NUM>, Polyamide <NUM>, Thermoplastic polyurethane (TPU), Polycarbonate (PC), Acrylonitrile butadiene styrene (ABS), Acrylonitrile styrene acrylate (ASA), Polypropylene (PP), Polyvinylidene fluoride (PVDF), Polyphenylene sulphide (PPS), Polyether ether ketone (PEEK), Ethylene propylene rubber (EDPM), Nitrile rubber (NBR), and Thermoplastic elastomers (TPE), or the like.

Claim 1:
A method of determining a condition of a surface of an additively manufactured polymer part, comprising:
capturing an image of a surface of an additively manufactured polymer part;
normalising the captured image to remove illumination geometry and illumination colour;
extracting a plurality of test parameters, the test parameters comprising colour parameters and texture parameters respectively relating to a colour and a texture of the surface of the additively manufactured polymer part from the captured image; and
determining the condition of the surface of the additively manufactured polymer part based on the test parameters extracted from the captured image, wherein the test parameters are each evaluated using Principle Component Analysis.