Method for producing and determining cleanability of parts of a biotreatment system

Abstract

The present disclosure relates to a method (100) of producing a part for use in a biological treatment system, the method comprising: manufacturing (110) a part using an additive manufacturing process; and post-treating (120) a surface of the part, wherein the surface is intended to be wetted in use; wherein the post-treated surface has one or more of the following region roughness parameter values measured (220) according to ISO 25178-2: 2022, in which after filtering the surface to remove surface features having a wavelength below 2.5 [mu] m and surface features having a wavelength above 11 [mu] m, the post-treated surface has one or more of the following region roughness parameter values, measured (220) according to ISO 25178-2: 2022, and after filtering the surface to remove surface features having a wavelength below 2.5 [mu] m and surface features having a wavelength above 11 [mu] m. Measuring one or more region roughness parameter values for the surface: an average valley area Sda between 50 [mu] m2 and 600 [mu] m2; an average hill area Sha between 50 [mu] m2 and 500 [mu] m2; a peak density Spd between 3000 mm <-2 > and 10000 mm <-2 >; and a kurtosis Sku of between 6 and 20.

Description

Technical Field

[0001] The present disclosure relates to a method of producing a part for use in a biological treatment system and a method of determining the cleanability of a surface of a part for use in a biological treatment system. Background Art

[0002] 3D printing technology, also known as additive manufacturing (AM), has been around since the 1980s, primarily for rapid prototyping of products for development within certain industries. The technological growth and mass production possibilities of various AM technologies have demonstrated their potential to complement and even replace conventional manufacturing techniques. Among the advantages AM offers the bioprocessing industry is the ability to increase geometric complexity while reducing costs and material waste, while requiring minimal manufacturing skills.

[0003] Among existing AM technologies, powder bed fusion (PBF) is the most developed and mature platform, capable of providing models of varying shapes and sizes using powder-based materials. However, various technical and regulatory challenges hinder the implementation of PBF technology in the bioprocessing field. In particular, technical aspects related to cleanability, sterility, surface finish, and dimensions should be designed according to good engineering principles to minimize bacterial adhesion on the component surface.

[0004] Despite continuous improvements in regulations, standards, and quality control for the production of bioprocessing equipment, bacterial adhesion leading to biofilm formation is a serious threat to human health and is responsible for 80% of microbial infections in the human body. Beyond its health impacts, biofilm formation has significant economic consequences across various sectors. For example, in the biopharmaceutical field, bacterial adhesion (which leads to biofilm formation within the interior of bioprocessing equipment) results in billions of dollars in lost revenue. Currently, there is a lack of understanding of how 3D-printed surfaces interact with bacteria, and how to prevent bacterial growth and reduce biofilm formation to meet the high microbiological requirements placed on components designed for use in contact with biological systems.

[0005] Among the surface properties of materials, surface roughness is considered the most critical parameter affecting biofilm formation. Some studies have shown that rougher surfaces increase bacterial adhesion and, thus, irreversible biofilm formation increases proportionally with roughness. However, other studies have reported that smoother surfaces do not significantly affect bacterial adhesion.

[0006] Besides surface roughness, the second most important aspect influencing bacterial adhesion is surface wettability, which is typically reported in terms of the apparent contact angle. The contact angle regulates the interaction between the solid and liquid phases. While it has been demonstrated that surfaces with very high or low wettability reduce biofilm formation, no consistent pattern has been observed for surfaces with intermediate wettability.

[0007] Therefore, there is a need for improved methods for verifying the suitability of 3D-printed parts for use in bioprocessing systems. In particular, there is a need for methods that more thoroughly verify whether a 3D-printed part is likely to cause biofilm formation, as well as the cleanability of the 3D-printed part. Additionally, there is a need for improved methods for manufacturing 3D-printed parts to ensure they are suitable for use in bioprocessing systems.

[0008] Accordingly, there is provided the invention as defined by the appended claims. Summary of the Invention

[0009] This summary introduces concepts that are described in greater detail in the detailed description. It should not be used to identify essential features of the claimed subject matter, nor should it be used to limit the scope of the claimed subject matter.

[0010] According to a first aspect of the present disclosure, there is provided a method of producing a part for use in a bioprocessing system, the method comprising: manufacturing the part using an additive manufacturing process; and post-treating a surface of the part, wherein the surface is intended to be wetted in use; wherein the post-treated surface has one or more of the following areal roughness parameter values measured in accordance with ISO 25178-2:2022, wherein the one or more areal roughness parameter values are measured for the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm: 2 and 600 μm 2 The average valley area Sda between 50μm 2 and 500 μm 2 The average hill area between 3000mm -2 and 10000mm -2 and a peak density Spd between 6 and 20.

[0011] The method of the first aspect allows for the manufacture of additively manufactured parts having cleanable surfaces. In particular, by customizing the post-processing to include one or more of the regional roughness parameter values listed in the method of the first aspect, the additively manufactured part can be verified as cleanable without the need for inefficient and time-consuming verification methods. The current process for verifying the cleanability of additively manufactured parts involves destructive testing of a certain number of prototypes, resulting in additional waste and reduced manufacturing efficiency. In particular, an alternative method for verifying the cleanability of additively manufactured parts would involve contaminating a certain number of prototypes to promote biofilm growth before attempting to clean the prototypes and determining the effectiveness of such cleaning. Due to the need for biofilm growth, such methods are time-consuming. Such methods also involve manual work and the potential for introducing additional contaminants due to manual handling of the selected prototypes being tested. Such methods require the manufacture of additional parts to be tested and may require testing of many parts to provide statistical validity. Each surface will also need to be measured using a profilometer or microscope to determine whether the part is cleanable.

[0012] In contrast, by tailoring the post-processing of the surface of the additively manufactured part so as to provide one or more regional surface roughness values that correspond to the surface roughness values of cleanable surfaces currently used in the bioprocessing industry, the method of the first aspect allows verification that the additively manufactured part produced using the method is cleanable.

[0013] According to a second aspect of the present disclosure, there is provided an additively manufactured part for use in a bioprocessing system, wherein the additively manufactured part comprises a surface intended to be wetted in use, wherein the surface has one or more of the following areal roughness parameter values measured in accordance with ISO 25178-2:2022, wherein the one or more areal roughness parameter values are measured for the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm: 2 and 600 μm 2 The average valley area Sda between 50μm 2 and 500 μm 2 The average hill area between 3000mm -2 and 10000mm -2 and a peak density Spd between 6 and 20.

[0014] According to a third aspect of the present disclosure, there is provided a method of determining the cleanability of a surface of a part for use in a bioprocessing system, the method comprising: manufacturing the part, wherein the part comprises a surface intended to be wetted in use; and one or more of the following steps: after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm, measuring an average valley area Sda of the surface, and if the average valley area Sda of the surface is within 50 μm, 2 and 600 μm 2 After filtering the surface to remove surface features with wavelengths below 2.5 μm and surface features with wavelengths above 11 μm, the average hill area Sha of the surface is measured, and if the average valley area Sda of the surface is between 50 μm and 11 μm, the surface is determined to be cleanable; 2 and 500 μm 2 After filtering the surface to remove surface features with wavelengths below 2.5 μm and surface features with wavelengths above 11 μm, the peak density Spd of the surface is measured, and if the peak density Spd of the surface is between 3000 mm -2 and 10000mm -2 and after filtering the surface to remove surface features with wavelengths below 2.5 μm and surface features with wavelengths above 11 μm, measuring the kurtosis Sku of the surface, and determining that the surface is cleanable if the kurtosis Sku of the surface is between 6 and 20.

[0015] The method of the third aspect allows for determining whether a surface of a part (and in particular, the surface of an additively manufactured part) is cleanable in an efficient manner. Current processes for determining the cleanability of additively manufactured parts involve destructive testing of a number of prototypes, resulting in additional waste and reduced manufacturing efficiency, as explained above.

[0016] In contrast, the method of the second aspect allows determining whether the surface of a part (particularly an additively manufactured part) is cleanable based on measurements of one or more regional surface roughness values that have the strongest correlation with cleanable surfaces currently used in the bioprocessing industry. This allows for a more efficient way to verify that the surface of a part is cleanable. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Specific embodiments are described below, by way of example only, and with reference to the accompanying drawings, in which:

[0018] Figure 1 A flow chart illustrating a method of producing a part for use in a biological processing system.

[0019] Figure 2 A flow chart illustrating a method of determining the cleanability of a surface of a part for use in a biological processing system.

[0020] Figure 3 Shown is the weight of artificial test soil (ATS) deposited on top of sample surfaces, including surfaces produced by CNC milling and surfaces produced by SLS that have been subjected to different degrees of post-processing, according to a first example.

[0021] Figure 4 Shown is the percentage of ATS removed from the sample surface after cleaning and drying according to the first example.

[0022] Figure 5 Relative light unit (RLU) values for a contaminated sample surface according to a first example are shown.

[0023] Figure 6 Show Figure 5 The RLU values in the table are presented, along with the RLU values of the positive and negative controls for each sample surface.

[0024] Figure 7 Graph showing relative areas of sample surfaces according to the first example.

[0025] Figure 8 Graph showing the complexity of the sample surface according to the first example.

[0026] Figure 9 The divergence of the roughness parameters between a surface produced by CNC milling and a post-processed surface produced by SLS according to a first example is shown.

[0027] Figure 10 Shown are values of the maximum valley aspect ratio Sdarx of the sample surface according to the first example.

[0028] Figure 11 Shown are values of the peak density per unit area Spd of the sample surface according to the first example.

[0029] Figure 12 Shown are values of the arithmetic mean height Sa of the sample surface according to the first example.

[0030] Figure 13 Show Figure 4 The percentage of ATS shown in Figure 10 The correlation between the Sdarx values is shown in .

[0031] Figure 14 Show Figure 4 The percentage of ATS shown in Figure 11 The correlation between the Spd values is shown in .

[0032] Figure 15 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the autocorrelation length Sal of the sample surface is shown in FIG.

[0033] Figure 16 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the kurtosis Sku of the sample surface is shown in FIG.

[0034] Figure 17 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the pit density Svd of the sample surface is shown in FIG.

[0035] Figure 18 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the average valley area Sda of the sample surface is shown in FIG.

[0036] Figure 19 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the maximum valley area Sdax of the sample surface is shown in FIG.

[0037] Figure 20 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the valley area standard deviation Sdaq of the sample surface is shown in FIG.

[0038] Figure 21 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the average hill area Sha of the sample surface is shown in FIG.

[0039] Figure 22 According to the first example Figure 4 Figure 2 shows the correlation between the percentage of ATS and the value of the maximum hillock area Shax of the sample surface.

[0040] Figure 23 According to the first example Figure 4 Figure 2 shows the correlation between the percentage of ATS and the value of the standard deviation Shaq of the hill area on the sample surface.

[0041] Figure 24 According to the first example Figure 4 The correlation between the percentage of ATS and the value of valley count Sdn of the sample surface is shown in FIG.

[0042] Figure 25 According to the first example Figure 4 The correlation between the percentage of ATS and the value of hillock count Shn on the sample surface is shown in FIG.

[0043] Figure 26 According to the first example Figure 4 Figure 2 shows the correlation between the percentage of ATS and the value of the average valley equivalent diameter Sded of the sample surface.

[0044] Figure 27 According to the first example Figure 4 Figure 2 shows the correlation between the percentage of ATS and the value of the maximum valley equivalent diameter Sdedx of the sample surface.

[0045] Figure 28 According to the first example Figure 4 Figure 2 shows the correlation between the percentage of ATS and the value of the standard deviation Sdedq of the valley equivalent diameter of the sample surface.

[0046] Figure 29 According to the first example Figure 4 Figure 2 shows the correlation between the percentage of ATS and the value of the average mound equivalent diameter Shed of the sample surface.

[0047] Figure 30 According to the first example Figure 4 Figure 2 shows the correlation between the percentage of ATS and the value of the maximum hillock equivalent diameter Shedx of the sample surface.

[0048] Figure 31 According to the first example Figure 4 Figure 2 shows the correlation between the percentage of ATS and the standard deviation of Shedq, the equivalent diameter of mounds on the sample surface.

[0049] Figure 32 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the average valley shape factor Sdff of the sample surface is shown in FIG.

[0050] Figure 33 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the maximum valley shape factor Sdffx of the sample surface is shown in FIG.

[0051] Figure 34 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the average hillock shape factor Shff of the sample surface is shown in FIG.

[0052] Figure 35 According to the first example Figure 4The correlation between the percentage of ATS and the value of the maximum hillock shape factor Shffx of the sample surface is shown in FIG.

[0053] Figure 36 According to the first example Figure 4 The correlation between the percentage of ATS and the value of the maximum valley roundness Sdrnx of the sample surface is shown in FIG.

[0054] Figure 37 According to the first example Figure 4 Figure 2 shows the correlation between the percentage of ATS and the value of the maximum hillock roundness Shrnx of the sample surface.

[0055] Figure 38 Graph showing advancing and receding contact angles of a sample surface according to a first example. DETAILED DESCRIPTION

[0056] Embodiments of the present disclosure are explained below with particular reference to the manufacture and cleanability determination of parts used in bioprocessing systems. However, it will be appreciated that the methods described herein can also be used to manufacture and determine the cleanability of parts used in other environments. Furthermore, embodiments of the present disclosure are explained below with particular reference to determining the cleanability of additively manufactured parts. However, it will be further appreciated that the methods described herein can also be used to determine the cleanability of parts manufactured using other manufacturing techniques.

[0057] Figure 1 is a flow chart of a method 100 of producing a part.

[0058] At 110, a part is manufactured using an additive manufacturing process. As an example, the part can be manufactured using an additive manufacturing process such as powder bed fusion (PBF), laser powder bed fusion, or electron beam melting (EBM). In particular, the PBF process can include selective layer sintering (SLS). In one example, the manufactured part is intended for use in a bioprocessing system and includes a surface that is intended to be wetted when the part is used in the bioprocessing system.

[0059] At 120, the surface of the part is post-processed to provide one or more regional roughness parameter values within a range. In one example, post-processing the surface includes laser polishing the surface.

[0060] In one example, one or more regional roughness parameters can be measured by analyzing surface images obtained using a confocal laser scanning microscope (CLSM), such as the VK-X1000 confocal laser scanning microscope available from Keyence Corporation in Osaka, Japan. The regional roughness parameters are associated with scale-limited surfaces, so the measurement of one or more regional roughness parameters first involves filtering one or more CLSM images of the surface of the part according to an appropriate scale range. In the examples described herein, surface feature wavelengths below 2.5 μm are filtered out from the CLSM images, and surface feature wavelengths above 11 μm are filtered out from the CLSM images. In alternative examples, different filtering ranges (such as 2 μm to 25 μm) can be used. However, it will be appreciated that the choice of filtering range affects the value of the regional roughness parameter. Therefore, implementing different filtering ranges can change the values of the regional roughness parameters listed below.

[0061] Post-processing of the part at 120 may include post-processing the surface to provide one or more surface wettability values (e.g., advancing contact angle values and / or receding contact angle values) within a certain range. The dynamic contact angle, including the advancing and receding contact angles, may be measured using an optical tensiometer, such as the ThetaLite optical tensiometer available from Biolin Scientific of Gothenburg, Sweden. In one example, a needle method may be used to measure the dynamic contact angle, wherein a needle is applied at 0.4 μL s -1 The advancing contact angle was measured for an advancing time of 100 s and at an advancing rate of 0.4 μL s -1 The receding contact angle was measured for a receding time of 100 seconds with a receding rate of 100 seconds. Prior to analysis, the samples were sterilized with 70% isopropyl alcohol (IPA) and dried with compressed nitrogen to remove contaminants and dust particles. To measure the dynamic contact angle, the advancing contact angle was first measured by dispensing a 5 μL oversized droplet on the sample surface. The needle was then brought close to the surface and a 0.4 μL s -1 The droplet volume was gradually increased with a filling rate of 0.4 μL s -1 The dispensing rate is measured by reducing the volume of the droplet. Calculation of advancing and receding contact angles can be measured using software such as OneAttension software available from Biolin Scientific of Gothenburg, Sweden.

[0062] The values of the regional roughness parameters provided by the post-processing of the surface at 120 are correlated with regional roughness parameters that have been identified as providing correlations with the cleanability of the surface. As described in more detail in the examples below, these regional roughness parameters have been determined by comparing the post-processed surface of a part manufactured using SLS with the surface of a part manufactured using computer numerical control (CNC) milling. The surfaces of parts manufactured using CNC milling represent surfaces used in today's bioprocessing industry and have been shown not to cause bacterial adhesion and biofilm formation. Determining the relevant one or more roughness parameters does not form part of method 100. Instead, it will be appreciated based on the discussion below that the comparison of the SLS surface with the reference CNC milled surface produces the most relevant roughness parameters. Post-processing of the surface at 120 is performed so as to provide one or more regional roughness parameter values for the most relevant roughness parameters.

[0063] As further described in the examples below, the surface of a part manufactured using CNC milling can be compared to the surface of a part manufactured using SLS. In particular, relevant regional roughness parameters are identified based on a comparison of a reference CNC milled surface with a post-treated SLS surface. In the examples described below, the post-treated SLS surface selected for comparison with the CNC milled surface is a post-treated SLS surface with a high degree of cleanability (i.e., a post-treated surface with the same degree of cleanability as the CNC milled surface). Therefore, the examples below involve determining the cleanability of various post-treated SLS surfaces and a reference CNC milled surface in order to determine the post-treated SLS surface with the highest degree of cleanability. As indicated above, surfaces produced by CNC milling are currently used in bioprocessing equipment because they have a high degree of cleanability. Therefore, in the examples, the surfaces selected for comparison each have a high degree of cleanability, and the two surfaces are compared in order to determine the regional roughness parameter with the lowest divergence between the surfaces.

[0064] Standardized areal roughness parameters are defined in ISO 25178. In particular, roughness parameters used to describe surface topography are defined in ISO 25178-2:2022. Most existing research on the effect of surface roughness on biofilm formation has focused solely on the arithmetic mean deviation Ra and Sa, which describe the average height from a two-dimensional profile and a three-dimensional surface, respectively. In contrast, the present disclosure involves considering many different areal roughness parameters, which have been found to be more correlated with surface cleanability than the arithmetic mean deviation Ra and Sa.

[0065] Specifically, the surface of the part may be post-processed at 120 to provide one or more areal roughness parameter values that are related to one or more of the following 25 areal roughness parameters measured according to ISO 25178-2:2022: kurtosis, Sku; peak density, Spd (mm -2 ); pit density, Svd (mm -2 ); average valley area, Sda (μm 2 ); Maximum valley area, Sdax (μm 2 ); valley area standard deviation Sdaq (μm 2 ); average hill area, Sha (μm 2 ); Maximum hill area, Shax (μm 2 ); standard deviation of hill area, Shaq (μm 2 ); valley count, Sdn; hill count, Shn; average valley equivalent diameter, Sded (μm); maximum valley equivalent diameter Sdedx (μm); valley equivalent diameter standard deviation, Sdedq (μm); average hill equivalent diameter, Shed (μm); maximum hill equivalent diameter, Shedx (μm); hill equivalent diameter standard deviation, Shedq (μm); average valley shape factor, Sdff; maximum valley shape factor, Sdffx; average hill shape factor, Shff; maximum hill shape factor, Shffx; maximum valley roundness, Sdrnx; maximum hill roundness, Shrnx; maximum valley aspect ratio, Sdarx; and autocorrelation length, Sal (μm). In the following example, these area roughness parameters are identified as parameters that have high similarity based on comparisons of surfaces found to have a high degree of cleanability and have a strong correlation with surface cleanability.

[0066] ISO 25178-2:2022 includes many parameters that fall into the category of "feature" parameters. Many of the parameters listed above fall into this category. According to this category, there are three types of features: area features (hills and valleys), line features (routes and ridges), and point features (peaks, pits, and saddle points). The regional roughness parameters associated with regional surface features include parameters associated with the height, area, volume, and count (number) of regional surface features (hills and valleys). The related regional roughness parameters listed above include all regional roughness parameters in ISO 25178-2:2022 that are associated with the area of regional surface features.

[0067] To identify hills and valleys on a surface, a watershed algorithm is typically applied to divide the surface into regions. Smaller segments are then pruned out using the Wolf pruning method, which removes regions below a certain height / depth threshold (e.g., 5% of the maximum height Sz of the surface). Surface segmentation is defined in ISO 25178-2:2022 and can be performed using surface analysis software, such as MountainsLab (RTM) software available from Digital Surf in Besançon, France.

[0068] Additional regional roughness parameters associated with regional surface features include parameters related to the roundness, shape factor, equivalent diameter, and aspect ratio of regional surface characteristics. Roundness is the ratio of the horizontal area of the pattern (hill or valley) to the area of a circle with a diameter equal to the maximum diameter. A circular object will produce a value of 1, while a rectangular object will produce a value less than 1. The shape factor is a measure of the compactness of the shape (i.e., the fill volume fraction). An elongated object will produce a value close to zero, while a compact object will produce a value close to 1. The equivalent diameter is the diameter of a circle with the same area as the pattern (hill or valley). The aspect ratio is the ratio of the maximum diameter to the minimum diameter and distinguishes between compact patterns and rectangular patterns (where a circular disk has a value of 1 and a rectangular pattern has a value greater than 1).

[0069] As explained above, the values of the regional roughness parameters listed above depend on the scale to which the surface is confined. The surface of the part can be post-processed at 120 to provide one or more regional roughness parameter values within the ranges listed in the following paragraphs. The regional roughness parameter values listed below result from filtering the surface features to remove surface feature wavelengths below 2.5 μm and to remove surface feature wavelengths above 11 μm.

[0070] Post-processing of the surface at 120 may provide one or more of the following:

[0071] (i) a Kurtosis (Sku) value for the scale-limited surface of 2.5 μm to 11 μm of between 6 and 20, preferably between 7 and 18, more preferably between 8 and 16, or most preferably between 9 and 14. Kurtosis (Sku) is a measure of surface sharpness, with higher Sku values indicating sharper peaks and pits (as opposed to rounder features);

[0072] (ii) at 3000mm -2 and 10000mm -2 Between, preferably 3500mm -2 and 9000mm -2 between, more preferably 4000mm -2 and 8000mm -2 between, more preferably between 4500mm-2 and 7500mm -2 between, more preferably between 5000mm -2 and 7000mm -2 between, or most preferably at 5500mm -2 and 6500mm -2 The peak density Spd of the scale-limited surface between 2.5 μm and 11 μm;

[0073] (iii) at 3000mm -2 and 10000mm -2 Between, preferably 3500mm -2 and 8000mm -2 between, more preferably 4000mm -2 and 7000mm -2 between, more preferably between 4500mm -2 and 6000mm -2 between, or most preferably 4000mm -2 and 5000mm -2 The pit density Svd of the surface is limited by the scale of 2.5μm to 11μm;

[0074] (iv) at 50 μm 2 and 600 μm 2 between, preferably between 75 μm 2 and 500 μm 2 between 100 μm and 200 μm, more preferably between 100 μm and 200 μm 2 and 400 μm 2 between 1 and 25 μm, more preferably between 125 μm and 25 μm 2 and 300 μm 2 between, or most preferably between 150 μm 2 and 250 μm 2 The average valley area Sda of the scale-limited surface between 2.5 μm and 11 μm;

[0075] (v) less than 7000 μm 2 , preferably less than 6000 μm 2 , more preferably less than 5000 μm 2 , more preferably less than 4000 μm 2 , or most preferably less than 3000 μm 2 The maximum valley area Sdax of the surface is limited by the scale of 2.5μm to 11μm;

[0076] (vi) Less than 600 μm 2 , preferably less than 500 μm 2 , more preferably less than 400 μm2 , more preferably less than 300 μm 2 , and most preferably less than 250 μm 2 The standard deviation Sdaq of the valley area of the surface with a scale limit of 2.5μm to 11μm;

[0077] (vii) at 50 μm 2 and 500 μm 2 between, preferably between 75 μm 2 and 400 μm 2 between 100 μm and 200 μm, more preferably between 100 μm and 200 μm 2 and 300 μm 2 between 1 and 25 μm, more preferably between 125 μm and 25 μm 2 and 250 μm 2 between, or most preferably between 150 μm 2 and 200 μm 2 The average hill area Sha of the scale-limited surface between 2.5 μm and 11 μm;

[0078] (viii) Less than 6000 μm 2 , preferably less than 5000 μm 2 , more preferably less than 4000 μm 2 , more preferably less than 3000 μm 2 , or most preferably less than 2000 μm 2 The maximum hill area Shax of the surface is limited by the scale of 2.5μm to 11μm;

[0079] (ix) Less than 500 μm 2 , preferably less than 400 μm 2 , more preferably less than 300 μm 2 , more preferably less than 250 μm 2 , and most preferably less than 200 μm 2 The standard deviation of the hill area Shaq of the scale-limited surface of 2.5μm to 11μm;

[0080] (x) a valley count Sdn of a scale-limited surface of 2.5 μm to 11 μm between 1000 and 2000, preferably between 1100 and 1900, more preferably between 1200 and 1800, more preferably between 1300 and 1700, and most preferably between 1400 and 1650;

[0081] (xi) a hillock count Shn of a scale-limited surface of 2.5 μm to 11 μm between 1000 and 3000, preferably between 1200 and 2800, more preferably between 1400 and 2600, more preferably between 1600 and 2400, and most preferably between 1800 and 2200;

[0082] (xii) an average valley equivalent diameter Sded of the scale-limiting surface of 2.5 to 11 μm, between 6 and 24 μm, preferably between 8 and 22 μm, more preferably between 10 and 20 μm, more preferably between 12 and 18 μm, and most preferably between 14 and 16 μm;

[0083] (xiii) a maximum valley equivalent diameter Sdedx of the scale-limited surface of 2.5 to 11 μm that is less than 90 μm, preferably less than 80 μm, more preferably less than 70 μm, more preferably less than 65 μm, and most preferably less than 60 μm;

[0084] (xiv) a standard deviation Sdedq of the valley equivalent diameters of the scale-limited surface of 2.5 μm to 11 μm of less than 14 μm, preferably less than 11 μm, more preferably less than 9 μm, more preferably less than 8 μm, and most preferably less than 7 μm;

[0085] (xv) an average hill equivalent diameter Shed of the scale-confining surface of 2.5 to 11 μm, between 5 μm and 23 μm, preferably between 7 μm and 21 μm, more preferably between 9 μm and 19 μm, more preferably between 11 μm and 17 μm, and most preferably between 13 μm and 15 μm;

[0086] (xvi) a maximum hill equivalent diameter Shedx of the scale-limited surface of 2.5 μm to 11 μm that is less than 70 μm, preferably less than 65 μm, more preferably less than 60 μm, more preferably less than 55 μm, and most preferably less than 50 μm;

[0087] (xvii) a standard deviation of the hill equivalent diameter Shedq of the scale-limited surface from 2.5 μm to 11 μm of less than 14 μm, preferably less than 11 μm, more preferably less than 9 μm, more preferably less than 8 μm, and most preferably less than 7 μm;

[0088] (xviii) an average valley shape factor Sdff for scale-confined surfaces of 2.5 μm to 11 μm of between 0.44 and 0.54, preferably between 0.45 and 0.53, more preferably between 0.46 and 0.52, more preferably between 0.47 and 0.51, or most preferably between 0.48 and 0.50;

[0089] (xix) a maximum valley shape factor Sdffx of the scale-confined surface of 2.5 μm to 11 μm between 0.815 and 0.875, preferably between 0.825 and 0.865, or more preferably between 0.835 and 0.855;

[0090] (xx) an average hillock shape factor Shff for scale-confined surfaces of 2.5 μm to 11 μm between 0.45 and 0.55, preferably between 0.46 and 0.54, more preferably between 0.47 and 0.53, more preferably between 0.48 and 0.52, or most preferably between 0.49 and 0.51;

[0091] (xxi) a maximum hillock shape factor Shffx of a scale-confined surface of 2.5 μm to 11 μm between 0.80 and 0.94, preferably between 0.82 and 0.92, more preferably between 0.84 and 0.90, or most preferably between 0.86 and 0.88;

[0092] (xxii) a maximum valley roundness Sdrnx of the scale-limited surface of 2.5 μm to 11 μm between 0.815 and 0.875, preferably between 0.825 and 0.865, or more preferably between 0.835 and 0.855;

[0093] (xxiii) a maximum hillock roundness Shrnx of a scale-limited surface of 2.5 μm to 11 μm between 0.83 and 0.90, preferably between 0.84 and 0.89, more preferably between 0.85 and 0.88, or most preferably between 0.86 and 0.87;

[0094] (xxiv) a maximum valley aspect ratio Sdarx of the scale-limited surface of 2.5 μm to 11 μm of between 11 and 18, preferably between 12 and 17, more preferably between 13 and 16, and most preferably between 14 and 15; and

[0095] (xxv) An autocorrelation length Sal of a scale-confined surface of 2.5 to 11 μm that is less than 4.5 μm, preferably less than 4.25 μm, more preferably less than 4 μm, or most preferably less than 3.75 μm.

[0096] The post-treated surface may have more than one of the areal roughness parameter values listed at (i) to (xxv). In one example, the post-treated surface may have all of the areal roughness parameter values listed at (i) to (xxv).

[0097] In one specific example, the post-treated surface may have one or more of the regional roughness parameter values listed at (i), (ii), (iv), and (vii) (and may, for example, have all of the regional roughness parameter values listed at (i), (ii), (iv), and (vii)). In this example, the post-treated surface may also have one or more of the regional roughness parameter values listed at (iii), (v), (viii), (xviii), (xx), and (xxv).

[0098] Given the strong correlation of these parameters with surface cleanability, if the values of one or more of the above roughness parameters are similar to the corresponding values for surfaces produced by CNC milling and / or cleanable post-processed surfaces of parts produced by additive manufacturing, then the surface of the manufactured part is likely to be cleanable. Therefore, post-processing the surface to include one or more of these values is likely to result in the surface being cleanable.

[0099] As explained above, post-processing of the part at 120 may include post-processing the surface to provide one or more surface wettability values (e.g., advancing contact angle values and / or receding contact angle values) within a certain range. Specifically, post-processing of the surface at 120 may provide one or more of the following:

[0100] (xxvi) an advancing contact angle of the post-treated surface between 80 and 110 degrees, preferably between 82.5 and 107.5 degrees, more preferably between 85 and 105 degrees, or most preferably between 87.5 and 102.5 degrees; and

[0101] (xxvii) a receding contact angle of the post-treated surface between 55 and 95 degrees, preferably between 57.5 and 92.5 degrees, more preferably between 60 and 90 degrees, more preferably between 62.5 and 87.5 degrees, or most preferably between 65 and 85 degrees.

[0102] Once a part has been manufactured using method 100, the surface of the part will have one or more of the areal roughness parameter values listed at (i) to (xxv), and optionally one or more of the surface wettability values listed at (xxvi) and (xxvii), depending on the post-processing performed at 120.

[0103] The part manufactured using method 100 is an additively manufactured part. The additively manufactured part can be formed from a material such as a polymer. Furthermore, the additively manufactured part can be formed using layers of powdered material that have been thermally bonded together using a process such as PBF (e.g., SLS). This means that the additively manufactured part can include a first region (or regions) where the material has not yet melted and a second region (or regions) where the material has melted and resolidified.

[0104] Method 100 allows for the manufacture of additively manufactured parts having cleanable surfaces. In particular, by customizing the post-treatment applied at 120 to include one or more of the regional roughness parameter values listed above, the additively manufactured part can be verified as cleanable without the need for inefficient and time-consuming verification methods. Current processes for verifying the cleanability of additively manufactured parts involve destructive testing of a certain number of prototypes, resulting in additional waste and reduced manufacturing efficiency. In particular, alternative methods for verifying the cleanability of additively manufactured parts would involve contaminating a certain number of prototypes to promote biofilm growth before attempting to clean the prototypes and determining the effectiveness of such cleaning. Due to the need for biofilm growth, such methods are time-consuming. Such methods also involve manual work and the potential for introducing additional contaminants due to manual handling of the selected prototypes being tested. Such methods require the manufacture of additional parts to be tested and may require testing of many parts to provide statistical validity. Each surface would also need to be measured using a profilometer or microscope to determine whether the part is cleanable.

[0105] In contrast, by tailoring post-processing of the surface of the additively manufactured part to provide one or more regional surface roughness values that correspond to the surface roughness values of cleanable surfaces manufactured using CNC milling, method 100 allows verification that the additively manufactured part produced using method 100 is cleanable.

[0106] Figure 2 is a flow chart of a method 200 of determining the cleanability of a surface of a part.

[0107] At 210, a part is manufactured. The part includes a surface intended to be wetted during use. The part may be manufactured using an additive manufacturing process such as powder bed fusion (PBF). Manufacturing the part at 210 may also include post-processing the part.

[0108] At 220, one or more areal roughness parameters of the surface are measured (eg, from a CLSM image, as described with reference to method 100). The method may include filtering the CLSM image using an appropriate scale range, such as 2.5 μm to 11 μm.

[0109] At 230 , it is determined whether the surface is cleanable based on the one or more areal roughness parameters measured at 220 .

[0110] If it is determined at 230 that the surface is cleanable, method 200 may include classifying the part as cleanable at 240 .

[0111] If it is determined at 230 that the surface is not cleanable, the method 200 may include modifying a design process associated with the design of the surface at 250. Alternatively, or in addition, if it is determined at 230 that the surface is not cleanable, the method may include modifying a manufacturing process associated with the manufacturing of the surface at 260. Modifying the manufacturing process may include incorporating one or more post-processing operations (such as laser polishing) into the manufacturing process and / or modifying one or more post-processing operations performed at 210.

[0112] The one or more areal roughness parameters measured at 220 may include one or more of: kurtosis, Sku; peak density, Spd (mm -2 ); pit density, Svd (mm -2 ); average valley area, Sda (μm 2 ); Maximum valley area, Sdax (μm 2 ); valley area standard deviation, Sdaq (μm 2 ); average hill area, Sha (μm 2 ); Maximum hill area, Shax (μm 2 ); standard deviation of hill area, Shaq (μm 2 ); valley count, Sdn; hill count, Shn; average valley equivalent diameter, Sded (μm); maximum valley equivalent diameter, Sdedx (μm); standard deviation of valley equivalent diameter, Sdedq (μm); average hill equivalent diameter, Shed (μm); maximum hill equivalent diameter, Shedx (μm); standard deviation of hill equivalent diameter, Shedq (μm); average valley shape factor, Sdff; maximum valley shape factor, Sdffx; average hill shape factor, Shff; maximum hill shape factor, Shffx; maximum valley roundness, Sdrnx; maximum hill roundness, Shrnx; maximum valley aspect ratio, Sdarx; and autocorrelation length, Sal (μm).

[0113] Each of the above parameters, except for kurtosis (Sku) and autocorrelation length (Sal), falls within the family of "feature" parameters defined in ISO 25178-2:2022. This family defines three different types of features: area features (hills and valleys), line features (paths and ridges), and point features (peaks and pits). Thus, the one or more roughness parameters measured at 220 may include one or more area roughness parameters associated with surface features (as defined in ISO 25178-2:2022).

[0114] Many of the above parameters that fall within the "feature" parameter family in ISO 25178-2:2022 are associated with areal features (i.e., hills and valleys). Thus, the one or more roughness parameters measured at 220 may include one or more areal roughness parameters associated with areal surface features.

[0115] The areal roughness parameters listed above include all characteristic parameters in ISO 25178-2:2022 that are related to the area of hills on the surface (i.e., Sha, Shax, Shaq), and all characteristic parameters in ISO 25178-2:2022 that are related to the area of valleys on the surface (i.e., Sda, Sdax, Sdaq). Therefore, the one or more roughness parameters measured at 220 may include one or more areal roughness parameters that are related to the area of areal surface features (i.e., related to the area of surface hills and / or the area of surface valleys as defined in ISO 25178-2:2022).

[0116] The areal roughness parameters listed above include all characteristic parameters in ISO 25178-2:2022 that are related to the equivalent diameter of hills on the surface (i.e., Shed, Shedx, Shedq) and all characteristic parameters in ISO 25178-2:2022 that are related to the equivalent diameter of valleys on the surface (i.e., Sded, Sdedx, Sdedq). Therefore, the one or more roughness parameters measured at 220 may include one or more areal roughness parameters that are related to the equivalent diameter of areal surface features (i.e., related to the equivalent diameter of surface hills and / or the equivalent diameter of surface valleys as defined in ISO 25178-2:2022).

[0117] The surface may be determined to be cleanable at 230 if, after filtering the surface features to remove surface feature wavelengths below 2.5 μm and to remove surface feature wavelengths above 11 μm, one or more of the following conditions are met:

[0118] (i) the surface has a Kurtosis (Sku) value of between 6 and 20, preferably between 7 and 18, more preferably between 8 and 16, or most preferably between 9 and 14, for a scale limit of 2.5 μm to 11 μm. Kurtosis (Sku) is a measure of surface sharpness, where higher Sku values indicate sharper peaks and pits (as opposed to rounder features);

[0119] (ii) The peak density Spd of the scale-limited surface from 2.5 μm to 11 μm is at 3000 mm -2 and 10000mm -2 Between, preferably 3500mm -2 and 9000mm -2 between 4000 mm and 1000 mm, more preferably between 4000 mm and 1000 mm -2 and 8000mm -2 between, more preferably between 4500mm -2 and 7500mm -2 between, more preferably between 5000mm -2 and 7000mm -2 between, or most preferably at 5500mm -2 and 6500mm -2 between;

[0120] (iii) The pit density Svd of the surface limited by the scale of 2.5μm to 11μm is 3000mm -2 and 10000mm -2 Between, preferably 3500mm -2 and 8000mm -2 between 4000 mm and 1000 mm, more preferably between 4000 mm and 1000 mm -2 and 7000mm -2 between, more preferably between 4500mm -2 and 6000mm -2 between, or most preferably 4000mm -2 and 5000mm -2 between;

[0121] (iv) The average valley area Sda of the surface with a scale limit of 2.5 μm to 11 μm is 50 μm 2 and 600 μm 2 between, preferably 75 μm 2 and 500 μm 2 More preferably, between 100 μm 2 and 400 μm 2 between 1 and 25 μm, more preferably between 125 μm and 25 μm. 2 and 300 μm 2 between, or most preferably between 150 μm 2 and 250 μm2 between;

[0122] (v) The maximum valley area Sdax of the surface with a scale limit of 2.5 μm to 11 μm is less than 7000 μm 2 , preferably less than 6000 μm 2 , more preferably less than 5000 μm 2 , more preferably less than 4000 μm 2 , or most preferably less than 3000 μm 2 ;

[0123] (vi) The standard deviation of valley area Sdaq of the surface with a scale limit of 2.5 μm to 11 μm is less than 600 μm 2 , preferably less than 500 μm 2 , more preferably less than 400 μm 2 , more preferably less than 300 μm 2 , and most preferably less than 250 μm 2 ;

[0124] (vii) The average hill area Sha of the scale-limited surface is 2.5 μm to 11 μm and is 50 μm 2 and 500 μm 2 between, preferably 75 μm 2 and 400 μm 2 More preferably, between 100 μm 2 and 300 μm 2 between 1 and 25 μm, more preferably between 125 μm and 25 μm. 2 and 250 μm 2 between, or most preferably between 150 μm 2 and 200 μm 2 between;

[0125] (viii) The maximum hill area Shax of the scale-limited surface between 2.5 μm and 11 μm is less than 6000 μm 2 , preferably less than 5000 μm 2 , more preferably less than 4000 μm 2 , more preferably less than 3000 μm 2 , or most preferably less than 2000 μm 2 ;

[0126] (ix) The standard deviation of the hill area Shaq of the scale-limited surface between 2.5 μm and 11 μm is less than 500 μm 2 , preferably less than 400 μm 2 , more preferably less than 300 μm 2 , more preferably less than 250 μm 2, and most preferably less than 200 μm 2 ;

[0127] (x) a valley count Sdn of the scale-limited surface of 2.5 μm to 11 μm between 1000 and 2000, preferably between 1100 and 1900, more preferably between 1200 and 1800, more preferably between 1300 and 1700, and most preferably between 1400 and 1650;

[0128] (xi) a hill count Shn of the scale-limited surface of 2.5 μm to 11 μm is between 1000 and 3000, preferably between 1200 and 2800, more preferably between 1400 and 2600, more preferably between 1600 and 2400, and most preferably between 1800 and 2200;

[0129] (xii) the average valley equivalent diameter Sded of the scale-confined surface of 2.5 μm to 11 μm is between 6 μm and 24 μm, preferably between 8 μm and 22 μm, more preferably between 10 μm and 20 μm, more preferably between 12 μm and 18 μm, and most preferably between 14 μm and 16 μm;

[0130] (xiii) the maximum valley equivalent diameter Sdedx of the scale-limited surface of 2.5 μm to 11 μm is less than 90 μm, preferably less than 80 μm, more preferably less than 70 μm, more preferably less than 65 μm, and most preferably less than 60 μm;

[0131] (xiv) the standard deviation Sdedq of the valley equivalent diameters of the scale-limited surface from 2.5 μm to 11 μm is less than 14 μm, preferably less than 11 μm, more preferably less than 9 μm, more preferably less than 8 μm, and most preferably less than 7 μm;

[0132] (xv) the average hill equivalent diameter Shed of the scale-confined surface of 2.5 μm to 11 μm is between 5 μm and 23 μm, preferably between 7 μm and 21 μm, more preferably between 9 μm and 19 μm, more preferably between 11 μm and 17 μm, and most preferably between 13 μm and 15 μm;

[0133] (xvi) the maximum hill equivalent diameter Shedx of the scale-limited surface of 2.5 μm to 11 μm is less than 70 μm, preferably less than 65 μm, more preferably less than 60 μm, more preferably less than 55 μm, and most preferably less than 50 μm;

[0134] (xvii) the standard deviation of the hill equivalent diameters Shedq of the scale-limited surfaces of 2.5 μm to 11 μm is less than 14 μm, preferably less than 11 μm, more preferably less than 9 μm, more preferably less than 8 μm, and most preferably less than 7 μm;

[0135] (xviii) the average valley shape factor Sdff of the scale-confined surface from 2.5 μm to 11 μm is between 0.44 and 0.54, preferably between 0.45 and 0.53, more preferably between 0.46 and 0.52, more preferably between 0.47 and 0.51, or most preferably between 0.48 and 0.50;

[0136] (xix) a maximum valley shape factor Sdffx of the scale-confined surface of 2.5 μm to 11 μm is between 0.815 and 0.875, preferably between 0.825 and 0.865, or more preferably between 0.835 and 0.855;

[0137] (xx) an average hill shape factor Shff of the scale-confined surface from 2.5 μm to 11 μm between 0.45 and 0.55, preferably between 0.46 and 0.54, more preferably between 0.47 and 0.53, more preferably between 0.48 and 0.52, or most preferably between 0.49 and 0.51;

[0138] (xxi) a maximum hill shape factor Shffx of the scale-confined surface of 2.5 μm to 11 μm is between 0.80 and 0.94, preferably between 0.82 and 0.92, more preferably between 0.84 and 0.90, or most preferably between 0.86 and 0.88;

[0139] (xxii) the maximum valley roundness Sdrnx of the scale-limited surface of 2.5 μm to 11 μm is between 0.815 and 0.875, preferably between 0.825 and 0.865, or more preferably between 0.835 and 0.855;

[0140] (xxiii) the maximum hillock roundness Shrnx of the scale-limited surface of 2.5 μm to 11 μm is between 0.83 and 0.90, preferably between 0.84 and 0.89, more preferably between 0.85 and 0.88, or most preferably between 0.86 and 0.87;

[0141] (xxiv) the maximum valley aspect ratio Sdarx of the scale-confined surface of 2.5 μm to 11 μm is between 11 and 18, preferably between 12 and 17, more preferably between 13 and 16, and most preferably between 14 and 15; and

[0142] (xxv) The autocorrelation length Sal of the scale confinement surface of 2.5 μm to 11 μm is less than 4.5 μm, preferably less than 4.25 μm, more preferably less than 4 μm, or most preferably less than 3.75 μm.

[0143] If, after filtering the surface features to remove surface feature wavelengths below 2.5 μm and to remove surface feature wavelengths above 11 μm, more than one of the conditions listed at (i) through (xxv) is met, the surface may be determined to be cleanable at 230. In one example, the surface may be determined to be cleanable at 230 if all of the conditions listed at (i) through (xxv) are met.

[0144] In one specific example, if after filtering the surface features to remove surface feature wavelengths below 2.5 μm and removing surface feature wavelengths above 11 μm, one or more of the conditions listed at (i), (ii), (iv), and (vii) are satisfied (e.g., all of the conditions listed at (i), (ii), (iv), and (vii)), then the surface may be determined to be cleanable at 230. In this example, if after filtering the surface features to remove surface feature wavelengths below 2.5 μm and removing surface feature wavelengths above 11 μm, one or more of the conditions listed at (iii), (v), (viii), (xvii), (xx), and (xxv) are satisfied, then the surface may be determined to be cleanable at 230.

[0145] Method 200 allows for determining in an efficient manner whether the surface of a part (and in particular, the surface of an additively manufactured part) is cleanable. Current processes for determining the cleanability of additively manufactured parts involve destructive testing of a certain number of prototypes, resulting in additional waste and reduced manufacturing efficiency. In particular, alternative methods for determining the cleanability of additively manufactured parts would involve contaminating a certain number of prototypes in order to promote biofilm growth before attempting to clean the prototypes and determining the effectiveness of such cleaning. Due to the need for biofilm growth, such methods are time consuming. Such methods also involve manual work and the potential for introducing additional contaminants due to manual handling of the selected prototypes being tested. Such methods require the manufacture of additional parts to be tested and may require testing of many parts in order to provide statistical validity. Each surface will also need to be measured using a profilometer or microscope in order to determine whether the part is cleanable.

[0146] In contrast, method 200 allows determining whether an AM part is cleanable based on measurements of one or more regional surface roughness values that have the strongest correlation with cleanable surfaces manufactured using CNC milling. This allows for verifying that an AM part is cleanable in a more efficient manner.

[0147] Example

[0148] This example describes a comparison of the surface of a part manufactured using SLS with the surface of a part manufactured using CNC milling, where both surfaces have high cleanability (and are therefore similarly cleanable). The example involves determining the region roughness parameter with the greatest similarity based on the comparison of these highly cleanable surfaces in order to establish a region roughness parameter that is strongly correlated with cleanability.

[0149] In this example, two grades of polypropylene (PP) were used as received, namely medical-grade isotactic PP homopolymer and industrial-grade isotactic PP. The medical-grade isotactic PP homopolymer, available from Nordbergs Tekniska AB in Vallentuna, Sweden, was used for conventional milling, while the industrial-grade isotactic PP, available from Ricoh Co., Ltd. in Tokyo, Japan, was used in SLS.

[0150] The CNC reference samples (identified herein as PP_CNC) were fabricated using a VF-8 milling machine available from Haas Automation, Inc., Oxnard, California, U.S.A. A disc sample with a diameter of 25 mm and a height of 5 mm was selected as the reference sample.

[0151] SLS disk samples (designated herein as PP_SLS or PP_PBF) with a diameter of 25 mm and a height of 5 mm were produced by SLS. The layer thickness used in the SLS process was 0.1 mm, with a printing tolerance of ±0.3%. The sample orientation during the printing process was horizontal (0°).

[0152] Six different types of post-treatment were used to obtain different surface textures of the PP_SLS samples: (i) no post-treatment (i.e., as-printed SLS samples), herein designated as PP_SLS_0h or PP_PBF_0h; (ii) SLS samples tumbled with medium grinding media for 5 h, herein designated as PP_SLS_5h or PP_PBF_5h; (iii) SLS samples tumbled with medium grinding media for 10 h, herein designated as PP_SLS_10h or PP_PBF_10h; (iv) (i) SLS samples tumbled for 15 hours using medium abrasive media, herein designated as PP_SLS_15h or PP_PBF_15h; (ii) SLS samples tumbled for 13 hours using medium abrasive media and polished for 3 hours using small abrasive media, herein designated as PP_SLS_13h_3P or PP_PBF_13h_3P; and (iii) SLS samples laser polished (LP) using a technique developed by the Fraunhofer Institute, herein designated as PP_SLS_LP or PP_PBF_LP. Post-processing method (vi) involves irradiating the surface of the SLS sample with laser radiation to melt the material near the surface, which closes cracks and pores on the surface and reduces surface roughness. The surface is then allowed to resolidify in a smooth state.

[0153] For tumbling surface finishing, ceramic triangular grinding media (CTAM) with coarse grain size, large particle size, matte surface, and medium media wear rate were used, along with detergent. The detergent was used for each sample at a constant concentration and dosage and did not provide any chemical energy to the surface finishing process. Instead, it was used to optimize the mechanical grinding energy provided by the media. During the tumbling process, the combination of circulating flow in the immersion tank and detergent application caused the sample to tumble in the detergent flow and remain below the surface of the detergent in the immersion tank.

[0154] Dynamic contact angles, including advancing and receding contact angles, were measured using a Theta Lite optical tensiometer available from Biolin Scientific, Gothenburg, Sweden. -1 The advancing contact angle was measured for an advancing time of 100 s and at an advancing rate of 0.4 μL s -1 The receding contact angle was measured for a receding time of 100 seconds.

[0155] Prior to analysis, the samples were sterilized with 70% isopropyl alcohol (IPA) and dried with compressed nitrogen to remove contaminants and dust particles. The needle method was used to measure the dynamic contact angle. First, the advancing contact angle was measured by dispensing a 5 μL oversized droplet on the sample surface. Then, the needle was brought close to the surface and a 0.4 μL s -1The droplet volume was gradually increased with a filling rate of 0.4 μL s -1 The dispensing rate is used to reduce the volume of the droplet. The advancing and receding contact angles are calculated using OneAttension software available from Biolin Scientific in Gothenburg, Sweden. A total of three measurements are performed for each sample at room temperature.

[0156] Surface roughness was determined using a VK-X1000 confocal laser scanning microscope (CLSM) available from Keyence Corporation, Osaka, Japan. Each surface image has a 700 x 525 μm 2 The surface was divided into four equal areas to calculate each roughness parameter in each of the mentioned areas. The following statistics are reported: mean, standard deviation, maximum and minimum values. The analysis was performed using Multi-File Analyser software. Three images of each sample were taken at 20x magnification. The following roughness parameters were calculated following ISO 25178-2:2022: Sq, Ssk, Sku, Sp, Sv, Sz, Sa, Smr, Smc, Sdc, Sal, Str, Std, Ssw, Sdq, Sdr, Vm, Vv, Vmp, Vmc, Vvc, Vvv, Spd, Spc, S10z, S5p, S5v, Sda, Sha, Sdv, Shv, Svd, Svc, Shh, Shhx, Shhq, Shax, Shaq, Shvx, Shv q, Sdd, Sddx, Sddq, Sdax, Sdaq, Sdvx, Sdvq, Shn, Sdn, Shrn, Shrnx, Shrnq, Shff, Shffx, Shffq, Shed, Shedx, S hedq, Shar, Sharx, Sharq, Sdrn, Sdrnx, Sdrnq, Sdff, Sdffx, Sdffq, Sded, Sdedx, Sdedq, Sdar, Sdarx, and Sdarq.

[0157] The top surface of the sample was visualized using a TM-1000 tabletop scanning electron microscope (SEM) available from Hitachi, Ltd., Tokyo, Japan, at an accelerating voltage of 15 kV. No conductive coating was used for tabletop SEM evaluation. Images were collected at 40×, 120×, 150×, and 180× magnifications.

[0158] Cleanability is initially determined using contamination studies involving artificial test soil (ATS). ATS is a standardized test soil containing proteins, hemoglobin, carbohydrates, cellulose, lipids, and mucins, used to simulate use testing. It is often used to soil medical devices such as flexible endoscopes for cleaning validation purposes. ATS provides a conditioning film on the surface that models the conditioning films typically produced by proteins and carbohydrates in biopharmaceutical applications.

[0159] The contamination study involved gravimetric analysis to determine the percentage of ATS removed from each sample by cleaning. The gravimetric analysis included (i) a negative device control, which was a sample collected from a defined surface area on a test sample that was not soiled with ATS (identified as "A" and measured in mg); (ii) a positive device control, which was a sample collected from a defined surface area on a test sample that had been soiled with ATS and allowed to dry in an incubator at 37°C for 3 hours (identified as "B" and measured in mg); and (iii) a test device, which was a sample collected from a defined surface area on a test sample that had been soiled with ATS, allowed to dry in an incubator at 37°C for 3 hours and then cleaned by a defined method (identified as "C" and measured in mg). Three samples were analyzed. Cleanability can be defined using either the residual level of the analyte (after cleaning (mg) = CA) or the percentage of analyte removed = ((BA) - (CA)) x 100 / (BA).

[0160] Contamination studies also involve relative light unit (RLU) measurements based on the detection of adenosine 5-triphosphate (ATP) bioluminescence. This method is based on the detection of ATP using firefly luciferase and the luciferin cofactor.

[0161] The experimental work procedure consisted of three main stages: (i) disinfection; (ii) ATS preparation; and (iii) ATS application. In the disinfection stage, the samples were cleaned with deionized water, immersed in 70% isopropyl alcohol (IPA) and dried with nitrogen. After this, one specimen of each sample batch was removed to measure the cleanliness (negative device control) and to ensure the efficiency of the disinfection step. In the ATS preparation stage, ATS was added to sterile water at a concentration of 0.09 g / ml. Prior to application, the mixture was vortexed for 10 minutes before being allowed to stand for 20 minutes.

[0162] In the ATS application stage, each sample (kept in a sterile culture dish) is initially weighed. Then, ATS is applied to each sample by covering the entire sample surface with 1 ml of contaminant. The sample is then dried at 37 ° C for 3 hours in a vibration incubator that provides consistent temperature and humidity during the drying process. Then, the weight of the sample (in the sterile culture dish) is remeasured. The sample is then cleaned at 121 ° C for 30 minutes in an autoclaved beaker. The beaker contains 30 ml of 70% IPA and the vibration speed is 100 rpm for 20 minutes. Then, before reweighing, the sample is placed on a sterilized culture dish and dried at 37 ° C for 2 hours. ATP measurement is performed by wiping each surface for 30 seconds and measuring RLU immediately thereafter.

[0163] Figure 3 The weight of ATS deposited on the top surface of the samples is shown. Specifically, after drying for 3 hours, an average of 0.102 ± 0.011 g of ATS was deposited on each surface, thereby demonstrating the consistent reproducibility of the method for depositing ATS on the sample surfaces.

[0164] Figure 4 The percentage of ATS removed after cleaning and drying in the incubator is shown.For the PP_CNC and PP_SLS_LP samples, the entire ATS layer delaminated from the surface (ie, 100% of the ATS was removed).

[0165] Figure 5 ATP results obtained for experiments performed after cleaning and drying are shown. As shown, the lowest contamination was achieved for PP_CNC and PP_SLS_LP. The PP_CNC sample produced an average of 20 RLU, while the PP_SLS_LP sample produced an average of 48 RLU. Both are within the range considered "safe" for treatment. Conversely, high RLU values were observed for all tumbled SLS samples, regardless of the applied post-treatment time. Furthermore, a higher standard deviation in RLU was observed for the tumbled SLS samples due to the inconsistent surface texture of the samples.

[0166] Figure 6 The RLU values of the negative and positive controls are shown to verify the reproducibility of the experimental procedure. Figure 6 As shown in , the values for the test samples made from CNC and SLS (followed by LP) were within the values of the negative and positive controls, while the values for the remaining AM samples were close to or higher than their corresponding positive control values.

[0167] It can be concluded that when the gravimetric analysis ( Figure 13 and Figure 14 ) and ATP bioluminescence ( Figure 15 and Figure 16 ) are two examples of manufacturing processes that reduce contamination levels when measured by both LP and CNC. On the other hand, surfaces produced by SLS and mechanical tumbling do not show any signs of cleaning improvement.

[0168] In this example, the filter applied to the CLSM image is determined by identifying the most significant scale for characterizing the surface, which is identified by scale-sensitive fractal analysis. Scale-sensitive fractal analysis is a multi-scale method that includes area-scale analysis to calculate the area of a surface that varies with scale. Using this method, the relative area and complexity of a surface are calculated by using a virtual tiling algorithm in which the surface topography is covered by triangular tiles. Each tile has the same area and represents the scale of the measurement. The relative area at a specific scale is estimated by taking the ratio of the calculated area to the nominal area at that scale. The calculated area is the product of the number of triangular tiles used to cover the surface and the scale or area of a single tile. Complexity is a measure of the slope of the relative area plot at each scale multiplied by the order of magnitude.

[0169] Figure 7 Graph showing the relative area of the surface of different samples as a function of scale. Figure 7 , PP_SLS_LP is at 1μm 2 The data series with the lowest value at the scale of PP_CNC is 1μm 2 The data series with the second lowest value at the scale of PP_SLS_13h_3P is at 1μm 2 The data series with the highest value at the scale of PP_SLS_0h is at 1μm 2 The second highest value is in the data series at the scale of 1 μm, and the tumbled SLS sample shows a consistent increase in the 1 μm 2 The value gradually decreases under the scale of .

[0170] For PP_CNC, Figure 7 Shown at small scales (less than 1 μm 2 ), the relative area reaches 1.4 as a maximum value, while above 60 μm 2 At this scale, surface features are not considered. Therefore, the relevant scale ranges from 1 μm 2 Up to 60μm 2 For PP_SLS_LP, above 20μm 2 At the large scale, surface features are not considered, while at the small scale, the relative area is the lowest among all samples (1.12). The trend for PP_SLS_LP is similar to that for PP_CNC.

[0171] Figure 7It is also shown that PP_SLS_0h has a high relative area (1.9) at small scales and a high relative area (1.9) at scales up to 30000 μm. 2 The relative area is relatively high at the scale of 60 μm, which means that the roughness is considered at all scales. The rolling effect reduces the relative area, but it can still be considered at large scales (above 60 μm). 2 ) were observed for roughness. As shown by the similarity in the trends for PP_SLS_10h and PP_SLS_15h, there was a negligible effect of tumbling after 10 hours of post-treatment. These tumbling times resulted in a maximum relative area of 1.45. Using the polishing medium for up to 3 hours did not affect the surface texture.

[0172] Figure 8 A graph showing how the complexity of the surface of different samples varies with scale. Figure 8 In 1μm 2 The data series in ascending order of complexity on the scale of are: PP_SLS_0h, PP_SLS_LP, PP_SLS_10h, PP_SLS_15h (which is very similar in value to PP_SLS_10h), PP_SLS_13h_3P, PP_SLS_5h and PP_CNC.

[0173] like Figure 8 As shown in Figure 2, PP_CNC exhibits low complexity at both medium and large scales, with a maximum size of 2 μm. 2 For PP_SLS_0h, the highest complexity (100) is at high scale (about 2000 μm 2 ). For the tumbled SLS samples, the complexity decreases as the post-processing time increases up to 10 hours. Polishing has no effect on the complexity, as shown by PP_SLS_13h_3P, which has a trend similar to the as-printed samples. PP_SLS_LP has the lowest complexity at small scales, where the complexity increases from 100 μm 2 Tend to be unified.

[0174] from Figure 8 The scale range used to filter the raw CLSM images and calculate the areal roughness parameters can be obtained. The PP_CNC (reference) sample was used to determine the scale range, which ranged from 3 μm 2 Up to 60μm 2 In this range, surface features of increasing complexity are produced during machining. To calculate the filter length, the endpoints of the area range (i.e., 3 μm 2 and 60 μm 2) times the square root of 2, resulting in a length range of 2.5 μm to 11 μm. This scale is used to apply a bandpass filter based on a robust Gaussian filter (ie, between 2.5 μm and 11 μm).

[0175] As explained above, the PP_CNC and PP_SLS_LP samples were found to have the highest cleanliness. Therefore, the PP_CNC and PP_SLS_LP samples were compared using the minimum divergence of the roughness parameters between the samples to identify the most relevant roughness parameters. This was done using Equation 1 below. The 72 areal roughness parameters listed in ISO 25178-2:2022 were considered.

[0176]

[0177] Figure 9 The divergence of the 72 evaluated surface roughness parameters is shown. Figure 9 As shown in , when comparing PP_CNC and PP_SLS_LP samples, Sdarx is the roughness parameter that best correlates with explaining the similarity. This parameter is newly included in ISO 25178-2:2022 and defines the ratio of the maximum diameter to the minimum diameter (aspect ratio), as given by the equation Sdarx = max(D max / D min ). This parameter distinguishes between compact and rectangular patterns (eg, in the case of a disk, Sdarx=1, while for a rectangular pattern, Sdarx>1). Specifically, Sdarx defines the maximum aspect ratio for the valley region.

[0178] Figure 10 The maximum valley aspect ratios of the reference (PP_CNC) and AM samples are shown. Figure 10 As shown in , the same maximum aspect ratio is found for the PP_CNC and PP_SLS_LP samples, while more rounded valleys are obtained for the SLS sample post-treated by tumbling. Regardless of the post-treatment time, Sdarx is approximately constant for each tumbled SLS sample.

[0179] Figure 11 The peak density per unit area (Spd) of the reference (PP_CNC) and AM samples is shown. Figure 9 As shown in , Spd has a low (near zero) divergence value resulting from the comparison of PP_CNC and PP_SLS_LP and is closely related to ATS removal. Figure 11 It shows that there is a larger peak density for PP_CNC and PP_SLS_LP samples (both at 5000 mm -2 Up to 6000mm -2 within the scope of ).

[0180] To demonstrate the need to use roughness parameters other than the mean roughness Sa, Figure 12 A diagram of Sa is shown in FIG. Figure 12 The similarity in Sa values reveals the limitations of using this single parameter to describe the surface characteristics of components produced by CNC machining and AM techniques. In particular, Sa cannot describe the differences observed between different manufacturing processes.

[0181] Figure 13 shows the correlation between ATS removal and Sdarx, while Figure 14 The correlation between ATS removal and Spd is shown. Figure 9 As shown in , the autocorrelation length (Sal) has a low (less than two) divergence value resulting from the comparison of PP_CNC and PP_SLS_LP and is closely related to ATS removal. The autocorrelation length defines the spatial distance between each surface feature in the surface. Figure 15 The correlation between ATS removal and Sal for each sample is shown. Figure 15 It can be seen that the reference (PP_CNC) sample has a short distance between features, which may hinder the possibility of ATS attachment and bonding to the surface, while the AM surface is porous and has a larger distance between surface features. Figures 13 to 15 As shown in Figure 3, the tumbling effect does not improve the efficiency of removing ATS from the surface, and in fact causes the opposite behavior. That is, the as-printed sample (PP_SLS_0h) with higher porosity and average roughness has a higher percentage of ATS separated when compared to the SLS samples that were post-processed by tumbling for 5 hours, 10 hours, and 15 hours. In contrast, the as-printed sample (PP_SLS_0h) with higher porosity and average roughness has a higher percentage of ATS separated when compared to the SLS samples that were post-processed by tumbling for 5 hours, 10 hours, and 15 hours. Figures 13 to 15 As seen, the SLS samples post-processed by laser polishing showed similar effects to the CNC samples.

[0182] exist Figure 10 In the graph of Sdarx shown in Figure 1, the Sdarx value of the PP_CNC sample is 14.16, and the Sdarx value of the PP_LP sample is 14.23. Post-treatment can be applied to the surface of the part produced by SLS to provide an Sdarx value consistent with the Sdarx values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide an Sdarx value between 11 and 18, preferably between 12 and 17, more preferably between 13 and 16, and most preferably between 14 and 15.

[0183] exist Figure 11 In the Spd diagram shown in FIG, the Spd value of the PP_CNC sample is 5793 mm -2 , and the Spd value of the PP_LP sample is 5563 mm -2Post-treatment can be applied to the surface of the parts manufactured by SLS to provide Spd values consistent with the Spd values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide 3000mm -2 and 10000mm -2 Between, preferably 3500mm -2 and 9000mm -2 between, more preferably 4000mm -2 and 8000mm -2 between, more preferably 4500mm -2 and 7500mm -2 between, more preferably 5000mm -2 and 7000mm -2 between, or most preferably 5500mm -2 and 6500mm -2 Spd value between.

[0184] exist Figure 15 In the graph of Sal shown in Figure 1 , the Sal value for the PP_CNC sample is 2.97 μm, and the Sal value for the PP_LP sample is 3.34 μm. Post-treatment can be applied to the surface of the part produced by SLS to provide a Sal value consistent with the Sal values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a Sal value of less than 4.5 μm, preferably less than 4.25 μm, more preferably less than 4 μm, or most preferably less than 3.75 μm.

[0185] exist Figure 16 In the graph of Sku shown in Figure 1 , the Sku value for the PP_CNC sample is 13.73, and the Sku value for the PP_LP sample is 11.26. Post-treatment can be applied to the surface of the part produced by SLS to provide a Sku value consistent with the Sku values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a Sku value between 6 and 20, preferably between 7 and 18, more preferably between 8 and 16, or most preferably between 9 and 14.

[0186] exist Figure 17 In the graph of Svd shown in FIG, the Svd value of the PP_CNC sample is 4944 mm -2 , and the Svd value of the PP_LP sample is 4570 mm -2 Post-treatment can be applied to the surface of the parts manufactured by SLS to provide Svd values consistent with the Svd values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide 3000mm -2 and 10000mm-2 Between, preferably 3500mm -2 and 8000mm -2 between, more preferably 4000mm -2 and 7000mm -2 between, more preferably 4500mm -2 and 6000mm -2 between, or most preferably 4000mm -2 and 5000mm -2 The Svd value between .

[0187] exist Figure 18 In the graph of Sda shown in FIG, the Sda value of the PP_CNC sample is 208.4 μm 2 , and the Sda value of the PP_LP sample is 229.6 μm 2 Post-treatment can be applied to the surface of the parts produced by SLS in order to provide Sda values that are consistent with the Sda values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a 50 μm 2 and 600 μm 2 between, preferably 75μm 2 and 500 μm 2 More preferably 100 μm 2 and 400 μm 2 between, more preferably 125 μm 2 and 300 μm 2 between, or most preferably 150 μm 2 and 250 μm 2 The Sda value between .

[0188] exist Figure 19 In the graph of Sdax shown in FIG, the Sdax value of the PP_CNC sample is 1977 μm 2 , and the Sdax value of the PP_LP sample is 2862 μm 2 Post-treatment can be applied to the surface of the parts produced by SLS to provide Sdax values consistent with the Sdax values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a surface roughness less than 7000 μm. 2 , preferably less than 6000 μm 2 , more preferably less than 5000 μm 2 , more preferably less than 4000 μm 2 , or most preferably less than 3000 μm 2 Sdax value.

[0189] exist Figure 20In the graph of Sdaq shown in FIG, the Sdaq value of the PP_CNC sample is 196.1 μm 2 , and the Sdaq value of the PP_LP sample is 226.0 μm 2 Post-treatment can be applied to the surface of parts made by SLS to provide Sdaq values consistent with the Sdaq values of PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide less than 600 μm 2 , preferably less than 500 μm 2 , more preferably less than 400 μm 2 , more preferably less than 300 μm 2 , and most preferably less than 250 μm 2 Sdaq value.

[0190] exist Figure 21 In the graph of Sha shown in FIG, the Sha value of the PP_CNC sample is 177.2 μm 2 , and the Sha value of the PP_LP sample is 185.4 μm 2 Post-treatment can be applied to the surface of the parts manufactured by SLS in order to provide a Sha value that is consistent with the Sha values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a 50 μm 2 and 500 μm 2 between, preferably 75μm 2 and 400 μm 2 More preferably 100 μm 2 and 300 μm 2 between, more preferably 125 μm 2 and 250 μm 2 between, or most preferably 150 μm 2 and 200 μm 2 The Sha value between.

[0191] exist Figure 22 In the Shax diagram shown in FIG, the Shax value of the PP_CNC sample is 1571 μm 2 , and the Shax value of the PP_LP sample is 1771 μm 2 Post-treatment can be applied to the surface of the parts produced by SLS to provide a Shax value that is consistent with the Shax values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a Shax value less than 6000 μm. 2 , preferably less than 5000 μm 2 , more preferably less than 4000 μm 2 , more preferably less than 3000 μm 2, or most preferably less than 2000 μm 2 Shax value.

[0192] exist Figure 23 In the Shaq graph shown in FIG, the Shaq value of the PP_CNC sample is 145.5 μm 2 , and the Shaq value of the PP_LP sample is 158.6 μm 2 Post-treatment can be applied to the surface of the parts produced by SLS to provide a Shaq value that is consistent with the Shaq values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a Shaq value of less than 500 μm. 2 , preferably less than 400 μm 2 , more preferably less than 300 μm 2 , more preferably less than 250 μm 2 , and most preferably less than 200 μm 2 Shaq value.

[0193] exist Figure 24 In the graph of Sdn shown in FIG, the Sdn value of the PP_CNC sample is 1621, and the Sdn value of the PP_LP sample is 1496. Post-treatment can be applied to the surface of the part produced by SLS to provide an Sdn value consistent with the Sdn values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide an Sdn value between 1000 and 2000, preferably between 1100 and 1900, more preferably between 1200 and 1800, more preferably between 1300 and 1700, and most preferably between 1400 and 1650.

[0194] exist Figure 25 In the Shn graph shown in FIG, the Shn value of the PP_CNC sample is 1925, and the Shn value of the PP_LP sample is 1850. Post-treatment can be applied to the surface of the part produced by SLS to provide Shn values consistent with the Shn values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a Shn value between 1000 and 3000, preferably between 1200 and 2800, more preferably between 1400 and 2600, more preferably between 1600 and 2400, and most preferably between 1800 and 2200.

[0195] exist Figure 26In the graph of Sded shown in Figure 1, the Sded value for the PP_CNC sample is 14.10 μm, and the Sdd value for the PP_LP sample is 14.86 μm. Post-treatment can be applied to the surface of the part produced by SLS to provide an Sded value consistent with the Sded values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide an Sded value between 6 μm and 24 μm, preferably between 8 μm and 22 μm, more preferably between 10 μm and 20 μm, more preferably between 12 μm and 18 μm, and most preferably between 14 μm and 16 μm.

[0196] exist Figure 27 In the graph of Sdedx shown in Figure 1 , the Sdedx value for the PP_CNC sample is 48.82 μm, and the Sdedx value for the PP_LP sample is 58.13 μm. Post-treatment can be applied to the surface of the part produced by SLS to provide an Sdedx value consistent with the Sdedx values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide an Sdedx value of less than 90 μm, preferably less than 80 μm, more preferably less than 70 μm, more preferably less than 65 μm, and most preferably less than 60 μm.

[0197] exist Figure 28 In the graph of Sdedq shown in Figure 1, the Sdedq value for the PP_CNC sample is 6.30 μm, and the Sdedq value for the PP_LP sample is 6.51 μm. Post-treatment can be applied to the surface of the part produced by SLS to provide an Sdedq value consistent with the Sdedq values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide an Sdedq value of less than 14 μm, preferably less than 11 μm, more preferably less than 9 μm, more preferably less than 8 μm, and most preferably less than 7 μm.

[0198] exist Figure 29 In the Shed graph shown in Figure 1, the Shed value for the PP_CNC sample is 13.15 μm, and the Shed value for the PP_LP sample is 13.48 μm. Post-treatment can be applied to the surface of the part produced by SLS to provide a Shed value consistent with the Shed value of the PP_CNC sample. Specifically, post-treatment can be applied to the SLS surface to provide a Shed value between 5 μm and 23 μm, preferably between 7 μm and 21 μm, more preferably between 9 μm and 19 μm, more preferably between 11 μm and 17 μm, and most preferably between 13 μm and 15 μm.

[0199] exist Figure 30In the Shedx graph shown in Figure 1, the Shedx value for the PP_CNC sample is 43.21 μm, and the Shedx value for the PP_LP sample is 45.91 μm. Post-treatment can be applied to the surface of the part produced by SLS to provide a Shedx value consistent with the Shedx values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a Shedx value of less than 70 μm, preferably less than 65 μm, more preferably less than 60 μm, more preferably less than 55 μm, and most preferably less than 50 μm.

[0200] exist Figure 31 In the Shedq graph shown in Figure 1, the Shedq value for the PP_CNC sample is 5.32 μm, and the Shedq value for the PP_LP sample is 5.43 μm. Post-treatment can be applied to the surface of the part produced by SLS to provide a Shedq value consistent with the Shedq values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a Shedq value of less than 14 μm, preferably less than 11 μm, more preferably less than 9 μm, more preferably less than 8 μm, and most preferably less than 7 μm.

[0201] exist Figure 32 In the graph of Sdff shown in Figure 1, the Sdff value for the PP_CNC sample is 0.486, and the Sdff value for the PP_LP sample is 0.491. Post-treatment can be applied to the surface of the part produced by SLS to provide an Sdff value consistent with the Sdff values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide an Sdff value between 0.44 and 0.54, preferably between 0.45 and 0.53, more preferably between 0.46 and 0.52, more preferably between 0.47 and 0.51, or most preferably between 0.48 and 0.50.

[0202] exist Figure 33 In the graph of Sdffx shown in Figure 1 , the Sdffx value for the PP_CNC sample is 0.847, and the Sdffx value for the PP_LP sample is 0.839. Post-treatment can be applied to the surface of the part produced by SLS to provide an Sdffx value consistent with the Sdffx values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide an Sdffx value between 0.815 and 0.875, preferably between 0.825 and 0.865, or more preferably between 0.835 and 0.855.

[0203] exist Figure 34In the Shff graph shown in Figure 1, the Shff value of the PP_CNC sample is 0.492, and the Shff value of the PP_LP sample is 0.503. Post-treatment can be applied to the surface of the part produced by SLS to provide a Shff value consistent with the Shff values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a Shff value between 0.45 and 0.55, preferably between 0.46 and 0.54, more preferably between 0.47 and 0.53, more preferably between 0.48 and 0.52, or most preferably between 0.49 and 0.51.

[0204] exist Figure 35 In the Shffx graph shown in FIG, the Shffx value of the PP_CNC sample is 0.864, and the Shffx value of the PP_LP sample is 0.871. Post-treatment can be applied to the surface of the part produced by SLS to provide Shffx values consistent with the Shffx values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide Shffx values between 0.80 and 0.94, preferably between 0.82 and 0.92, more preferably between 0.84 and 0.90, or most preferably between 0.86 and 0.88.

[0205] exist Figure 36 In the graph of Sdrnx shown in Figure 1, the Sdrnx value for the PP_CNC sample is 0.837, and the Sdrnx value for the PP_LP sample is 0.850. Post-treatment can be applied to the surface of the part produced by SLS to provide an Sdrnx value consistent with the Sdrnx values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide an Sdrnx value between 0.815 and 0.875, preferably between 0.825 and 0.865, or more preferably between 0.835 and 0.855.

[0206] exist Figure 37 In the Shrnx graph shown in FIG, the Shrnx value of the PP_CNC sample is 0.862, and the Shrnx value of the PP_LP sample is 0.864. Post-treatment can be applied to the surface of the part produced by SLS to provide a Shrnx value consistent with the Shrnx values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide a Shrnx value between 0.83 and 0.90, preferably between 0.84 and 0.89, more preferably between 0.85 and 0.88, or most preferably between 0.86 and 0.87.

[0207] exist Figure 38In the graph of advancing and receding contact angles (ACA and RCA) shown in , the ACA value of the PP_CNC sample is 89 degrees, and the ACA value of the PP_LP sample is 100 degrees. Post-treatment can be applied to the surface of the part manufactured by SLS to provide an ACA value consistent with the ACA values of the PP_CNC and PP_LP samples. Specifically, post-treatment can be applied to the SLS surface to provide an ACA value between 80 degrees and 110 degrees, preferably between 82.5 degrees and 107.5 degrees, more preferably between 85 degrees and 105 degrees, or most preferably between 87.5 degrees and 102.5 degrees. The RCA value of the PP_CNC sample is 68 degrees, and the RCA value of the PP_LP sample is 83 degrees. Post-treatment can be applied to the surface of the part manufactured by SLS to provide an ACA value consistent with the ACA values of the PP_CNC and PP_LP samples. Specifically, post-treatment may be applied to the SLS surface to provide an ACA value between 55 and 95 degrees, preferably between 57.5 and 92.5 degrees, more preferably between 60 and 90 degrees, more preferably between 62.5 and 87.5 degrees, or most preferably between 65 and 85 degrees.

[0208] Variations or modifications of the systems and methods described herein are listed in the following paragraphs.

[0209] It will also be appreciated that the methods described herein are not limited to evaluating the surfaces of parts produced by PBF, and may also be applied to the surfaces of parts produced by other AM techniques. Furthermore, it will be appreciated that the methods described herein are not limited to evaluating the surfaces of parts produced by AM techniques, and may alternatively or additionally be applied to the surfaces of parts produced using other manufacturing techniques.

[0210] Additionally, the methods described herein are not limited to evaluating flat surfaces. In particular, Figure 2 The proposed method can be used to verify the cleanability of other features of manufactured parts, such as pockets, cavities, and corners. This can be done by identifying relevant roughness parameters based on comparison with corresponding features of parts produced using manufacturing methods (such as CNC milling) that are currently used to produce cleanable features on parts used in bioprocessing applications.

[0211] The described methods can be implemented using computer-executable instructions. A computer program product or computer-readable medium may include or store computer-executable instructions. A computer program product or computer-readable medium may include a hard drive, flash memory, read-only memory (ROM), CD, DVD, cache, random access memory (RAM), and / or any other storage medium in which information is stored for any duration (e.g., for an extended period of time, permanently, for a short instance, for temporary buffering, and / or for caching of information). A computer program may include computer-executable instructions. A computer-readable medium may be a tangible or non-transient computer-readable medium. The term "computer-readable" includes "machine-readable."

[0212] The singular terms "a" and "an" should not be used to mean "one and only one". Instead, unless otherwise stated, they should be used to mean "at least one" or "one or more". The word "comprise" and its derivatives (including "comprising" and "containing") include each of the stated features, but do not exclude the inclusion of one or more additional features.

[0213] The above embodiments have been described by way of example only, and the described embodiments are to be considered in all respects as illustrative and not restrictive. It will be appreciated that variations of the described embodiments may be made without departing from the scope of the present invention. It will also be apparent that there are numerous variations not yet described that fall within the scope of the appended claims.

Claims

1. A method (100) of producing a part for use in a biological treatment system, the method comprising: manufacturing (110) the part using an additive manufacturing process; as well as post-treating (120) a surface of the part, wherein the surface is intended to be wetted in use; The post-treated surface has one or more of the following areal roughness parameter values measured according to ISO 25178-2:2022, wherein the one or more areal roughness parameter values are measured (220) for the surface after filtering the surface to remove surface features having a wavelength below 2.5 μm and surface features having a wavelength above 11 μm: At 50 μm 2 and 600 μm 2 The average valley area between them is Sda; At 50 μm 2 and 500 μm 2 The average hill area between Sha; At 3000mm -2 and 10000mm -2 The peak density Spd between ; and Kurtosis Sku between 6 and 20.

2. The method (100) according to claim 1, wherein: The parts were manufactured using powder bed fusion.

3. The method (100) according to claim 2, wherein: The part is manufactured using selective layer sintering.

4. The method (100) according to any one of claims 1 to 3, wherein: Post-processing the surface includes laser polishing the surface.

5. The method (100) according to any one of claims 1 to 4, wherein: The average valley area Sda of the post-treated surface is 75 μm 2 and 500 μm 2 between, preferably 100 μm 2 and 400 μm 2 between 1 and 25 μm, more preferably between 125 μm and 25 μm 2 and 300 μm 2 between, or most preferably between 150 μm 2 and 250 μm 2 between.

6. The method (100) according to any one of claims 1 to 5, wherein: The average hill area Sha of the post-treated surface is 75 μm 2 and 400 μm 2 between, preferably 100 μm 2 and 300 μm 2 between 1 and 25 μm, more preferably between 125 μm and 25 μm 2 and 250 μm 2 between, or most preferably between 150 μm 2 and 200 μm 2 between.

7. The method (100) according to any one of claims 1 to 6, wherein: The peak density Spd of the post-treated surface is at 3500 mm -2 and 9000mm -2 Between, preferably 4000mm -2 and 8000mm -2 between 4500mm and 5000mm, more preferably between 4500mm and 5000mm -2 and 7500mm -2 between, more preferably between 5000mm -2 and 7000mm -2 between, or most preferably at 5500mm -2 and 6500mm -2 between.

8. The method (100) according to any one of claims 1 to 7, wherein: The kurtosis Sku of the post-treated surface is between 7 and 18, more preferably between 8 and 16, or most preferably between 9 and 14.

9. The method (100) according to any one of claims 1 to 8, wherein: The post-treated surface has an autocorrelation length Sal measured according to ISO 25178-2:2022, which is between 2 μm and 4.5 μm, preferably between 2.25 μm and 4.25 μm, more preferably between 2.5 μm and 4 μm, or most preferably between 2.75 μm and 3.75 μm, wherein the autocorrelation length Sal is measured for the surface after filtering the surface to remove surface features with wavelengths below 2.5 μm and surface features with wavelengths above 11 μm.

10. The method (100) according to any one of claims 1 to 9, wherein: The post-treated surface has a maximum valley area Sdax measured according to ISO 25178-2:2022, the maximum valley area Sdax being less than 7000 μm 2 , preferably less than 6000 μm 2 , more preferably less than 5000 μm 2 , more preferably less than 4000 μm 2 , or most preferably less than 3000 μm 2 , wherein the maximum valley area Sdax is measured for the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

11. The method (100) according to any one of claims 1 to 10, wherein: The post-treated surface has a maximum hill area Shax measured according to ISO 25178-2:2022, the maximum hill area Shax being less than 6000 μm 2 , preferably less than 5000 μm 2 , more preferably less than 4000 μm 2 , more preferably less than 3000 μm 2 , or most preferably less than 2000 μm 2 , wherein the maximum hill area Shax is measured for the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

12. The method (100) according to any one of claims 1 to 11, wherein: The post-treated surface has a pit density Svd measured according to ISO 25178-2:2022, the pit density Svd being 0.040 at 3000 mm -2 and 10000mm -2 Between, preferably 3500mm -2 and 8000mm -2 between, more preferably 4000mm -2 and 7000mm -2 between 4500mm and 5000mm, more preferably between 4500mm and 5000mm -2 and 6000mm -2 between, or most preferably 4000mm -2 and 5000mm -2 Between, wherein the pit density Svd is measured for the surface after filtering the surface to remove surface features with wavelengths below 2.5 μm and surface features with wavelengths above 11 μm.

13. The method (100) according to any one of claims 1 to 12, wherein: The post-treated surface has an average hill shape factor Shff measured according to ISO 25178-2:2022, the average hill shape factor Shff being between 0.45 and 0.55, preferably between 0.46 and 0.54, more preferably between 0.47 and 0.53, more preferably between 0.48 and 0.52, or most preferably between 0.49 and 0.51, wherein the average hill shape factor Shff is measured on the surface after filtering the surface to remove surface features with wavelengths below 2.5 μm and surface features with wavelengths above 11 μm.

14. The method (100) according to any one of claims 1 to 13, wherein: The post-treated surface has an average valley shape factor Sdff measured according to ISO 25178-2:2022, the average valley shape factor Sdff being between 0.44 and 0.54, preferably between 0.45 and 0.53, more preferably between 0.46 and 0.52, more preferably between 0.47 and 0.51, or most preferably between 0.48 and 0.50, wherein the average valley shape factor Sdff is measured on the surface after filtering the surface to remove surface features with a wavelength below 2.5 μm and surface features with a wavelength above 11 μm.

15. The method (100) according to any one of claims 1 to 14, wherein: The advancing contact angle of the post-treated surface is between 80 and 110 degrees, preferably between 82.5 and 107.5 degrees, more preferably between 85 and 105 degrees, or most preferably between 87.5 and 102.5 degrees.

16. The method (100) according to any one of claims 1 to 15, wherein: The receding contact angle of the post-treated surface is between 55 and 95 degrees, preferably between 57.5 and 92.5 degrees, more preferably between 60 and 90 degrees, more preferably between 62.5 and 87.5 degrees, or most preferably between 65 and 85 degrees.

17. An additively manufactured part for use in a bioprocessing system, wherein: The additively manufactured part comprises a surface intended to be wetted in use, wherein the surface has one or more of the following areal roughness parameter values measured according to ISO 25178-2:2022, wherein the one or more areal roughness parameter values are measured for the surface after filtering the surface to remove surface features having a wavelength below 2.5 μm and surface features having a wavelength above 11 μm: At 50 μm 2 and 600 μm 2 The average valley area between them is Sda; At 50 μm 2 and 500 μm 2 The average hill area between Sha; At 3000mm -2 and 10000mm -2 The peak density Spd between ; and Kurtosis Sku between 6 and 20.

18. The additively manufactured part according to claim 17, wherein: The average valley area Sda of the post-treated surface is 75 μm 2 and 500 μm 2 between, preferably 100 μm 2 and 400 μm 2 between 1 and 25 μm, more preferably between 125 μm and 25 μm 2 and 300 μm 2 between, or most preferably between 150 μm 2 and 250 μm 2 between.

19. The additively manufactured part according to claim 17 or claim 18, wherein The average hill area Sha of the post-treated surface is 75 μm 2 and 400 μm 2 between, preferably 100 μm 2 and 300 μm 2 between 1 and 25 μm, more preferably between 125 μm and 25 μm 2 and 250 μm 2 between, or most preferably between 150 μm 2 and 200 μm 2 between.

20. The additively manufactured part according to any one of claims 17 to 19, wherein The peak density Spd of the post-treated surface is at 3500 mm -2 and 9000mm -2 Between, preferably 4000mm -2 and 8000mm -2 between 4500mm and 5000mm, more preferably between 4500mm and 5000mm -2 and 7500mm -2 between, more preferably between 5000mm -2 and 7000mm -2 between, or most preferably at 5500mm -2 and 6500mm -2 between.

21. The additively manufactured part according to any one of claims 17 to 20, wherein The kurtosis Sku of the post-treated surface is between 7 and 18, more preferably between 8 and 16, or most preferably between 9 and 14.

22. A method (230) of determining the cleanability of a surface of a part for use in a biological processing system, the method comprising: manufacturing (210) the part, wherein the part includes a surface intended to be wetted in use; and One or more of the following steps: After filtering the surface to remove surface features with wavelengths below 2.5 μm and surface features with wavelengths above 11 μm, the average valley area Sda of the surface is measured, and if the average valley area Sda of the surface is within 50 μm 2 and 600 μm 2 If the surface is cleanable, After filtering the surface to remove surface features with wavelengths below 2.5 μm and surface features with wavelengths above 11 μm, the average hill area Sha of the surface is measured, and if the average valley area Sda of the surface is within 50 μm 2 and 500 μm 2 If the surface is cleanable, After filtering the surface to remove surface features with wavelengths below 2.5 μm and surface features with wavelengths above 11 μm, the peak density Spd of the surface is measured, and if the peak density Spd of the surface is within 3000 mm -2 and 10000mm -2 , then determining that the surface is cleanable; and After filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm, the kurtosis Sku of the surface is measured and if the kurtosis Sku of the surface is between 6 and 20, the surface is determined to be cleanable.

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