Methods for fabricating components for bioprocessing systems and methods for determining the cleanliness of components for bioprocessing systems

Abstract

The present disclosure relates to a method 100 of fabricating a part for use in a bioprocessing system, comprising manufacturing 110 the part using an additive manufacturing process and post-treating 120 a surface of the part, the surface intended to be wetted during use, wherein the post-treated surface has a surface roughness parameter value measured 220 in accordance with ISO 25178-2:2022, i.e., greater than 50 μm. 2 to 600 μm 2 Average valley area Sda between 50 μm 2 to 500 μm 2 Average mountain area between Sha, 3000mm -2 From 10,000 mm -2 and a peak apex density Spd between 0.5 and 1.0, and a kurtosis Sku between 6 and 20, wherein the one or more surface roughness parameter values ​​are measured on the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

Description

[Technical Field]

[0001] The present disclosure relates to methods for fabricating components for use in bioprocessing systems and methods for determining the cleanliness of the surfaces of components for use in bioprocessing systems. [Background technology]

[0002] 3D printing technology (also known as additive manufacturing (AM)) has been around since the 1980s, when it was primarily used for rapid prototyping for product development in certain industries. The technological growth and ability to mass-produce different technologies within AM has demonstrated its potential to complement and even replace traditional manufacturing techniques. Some of the advantages AM offers to the bioprocessing industry are the potential for increased geometric complexity, reduced costs and material waste, while requiring less manufacturing technology.

[0003] Among existing AM technologies, powder bed fusion (PBF) is the most developed and mature platform, using powder-based materials to provide models with a variety of shapes and sizes. However, various technical and regulatory challenges hinder the implementation of PBF technology in the bioprocessing field. Specifically, technical aspects related to cleanliness, sterility, surface finish, and dimensions must be designed according to sound engineering principles to minimize bacterial adhesion on the surface of components.

[0004] Despite continuous improvements in regulations, standards, and quality controls for the fabrication of bioprocessing equipment, bacterial adhesion leading to biofilm formation remains a serious threat to human health and is responsible for 80% of bacterial infections occurring in the human body. Apart from its health impact, biofilm formation has enormous economic impacts in different sectors. For example, in the field of biopharmaceuticals, bacterial adhesion leading to biofilm formation inside bioprocessing equipment results in huge economic losses of 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 of components for use in biological systems.

[0005] Among the surface properties of materials, surface roughness is considered to be the most important parameter affecting biofilm formation. Some studies have shown that rougher surfaces increase bacterial adhesion, thereby increasing irreversible biofilm formation proportionally. However, other studies have reported that smoother surfaces do not significantly affect bacterial adhesion.

[0006] In addition to surface roughness, the second important aspect affecting bacterial adhesion is surface wettability, which is generally reported in terms of apparent contact angle. The value of the contact angle regulates the interaction between the solid and liquid phases. It has been demonstrated that surfaces with very high or low wettability can reduce biofilm formation, but no regular pattern was found for surfaces with moderate wettability. Summary of the Invention [Problem to be solved by the invention]

[0007] Therefore, there is a need for improved methods of certifying the suitability of 3D printed parts for use in bioprocessing systems. Specifically, there is a need for methods that provide more complete certification of whether a 3D printed part has the potential to result in biofilm formation and the cleanliness of the 3D printed part. There is also a need for improved methods of manufacturing 3D printed parts to ensure their suitability for use in bioprocessing systems. [Means for solving the problem]

[0008] Accordingly, there is provided the invention as defined in the appended claims.

[0009] This Summary introduces concepts that are more fully described in the Detailed Description. This Summary is not intended 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 fabricating a part for use in a bioprocessing system, comprising the steps of manufacturing the part using an additive manufacturing process and post-treating a surface of the part, the surface being intended to be wetted during use, wherein the post-treated surface has the following surface roughness parameter values ​​measured in accordance with ISO 25178-2:2022: 2 to 600 μm 2 Average valley area Sda between 50 μm 2 to 500 μm 2 Average mountain area between Sha, 3000mm -2 From 10,000 mm -2 and a peak apex density Spd between 0.01 and 0.5; and a kurtosis Sku between 6 and 20, wherein the one or more surface roughness parameter values ​​are measured on the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

[0011] The method of the first aspect enables the production of additively manufactured parts with clean surfaces. Specifically, by adjusting post-processing to include one or more of the surface roughness parameter values ​​listed in the method of the first aspect, the additively manufactured part can be certified as clean without the need for inefficient and time-consuming certification methods. The current process for certifying the cleanliness of additively manufactured parts involves destructive testing of a certain number of prototypes, resulting in additional waste and reduced manufacturing efficiency. Specifically, an alternative method for certifying the cleanliness of additively manufactured parts involves contaminating a certain number of prototypes to promote biofilm growth before attempting to clean them and determining the effectiveness of such cleaning. Such methods are time-consuming due to the need for biofilm growth. Such methods also involve manual labor, potentially introducing additional contamination as a result of manual handling of the selected prototypes being tested. Such methods require the production of additional parts to be tested, which may require many parts to be tested to provide satisfactory effectiveness. Each surface must also be measured using a profilometer or microscope before a determination can be made that the part is clean.

[0012] In contrast, the first aspect allows for certification of the cleanliness of additively manufactured parts produced using the present method by tailoring the post-treatment of the surface of the additively manufactured part to provide one or more areal surface roughness values ​​that correspond to the surface roughness values ​​of clean surfaces currently used in the bioprocessing industry.

[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 that is intended to be wetted during use, the surface having the following surface roughness parameter values ​​measured in accordance with ISO 25178-2:2022: 2 Average valley area Sda between 50 and 500 μm 2 Average mountain area between Sha, 3000mm -2 From 10,000 mm-2 and a peak density Spd between 0.01 and 0.5, and a sharpness Sku between 6 and 20, wherein the one or more surface roughness parameter values ​​are measured on the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

[0014] According to a third aspect of the present disclosure, there is provided a method for determining the cleanliness of a surface of a component for use in a bioprocessing system, comprising the steps of: manufacturing a component with a surface intended to be wetted during use; measuring a mean valley area Sda of the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm; and determining whether the mean valley area Sda of the surface is 50 μm or less. 2 to 600 μm 2 determining that the surface is clean if the average peak area Sha of the surface is between 50 μm and 11 μm; and measuring the average peak area Sha of 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 to 500 μm 2 determining that the surface is clean if the peak density (Spd) of the surface is between 3000 mm and 4000 mm; and determining that the peak density (Spd) of the surface is clean if the peak density (Spd) of the surface is between 3000 mm and 4000 mm after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm. -2 From 10,000 mm -2 determining that the surface is clean if the surface sharpness Sku is between 6 and 20; and measuring the surface sharpness Sku after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm, and determining that the surface is clean if the surface sharpness Sku is between 6 and 20.

[0015] The method of the third aspect allows for determining whether a surface of a part (specifically, a surface of an additively manufactured part) can be cleaned in an efficient manner. The current process for determining the cleanliness of an additively manufactured part, as previously explained, involves destructive testing of a certain number of prototypes, resulting in additional waste and reduced manufacturing efficiency.

[0016] In contrast, the method of the second aspect allows for the determination of whether the surface of a part (specifically, an additively manufactured part) is clean based on the measurement of one or more areal surface roughness values ​​that have the strongest correlation with clean surfaces currently used in the bioprocessing industry. This allows for the certification of part surfaces as clean in a more efficient manner.

[0017] Specific embodiments are now described, by way of example only, and with reference to the accompanying drawings. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a flow chart of a method of fabricating a component for use in a bioprocessing system. [Figure 2] 1 is a flow chart of a method for determining the surface cleanliness of a component for use in a bioprocessing system. [Figure 3] FIG. 1 shows the weight of artificial test soil (ATS) placed on sample surfaces comprising surfaces fabricated by CNC milling and surfaces fabricated by SLS, with different degrees of post-treatment, according to a first example. [Figure 4] FIG. 10 shows the percentage of ATS removed from the sample surface after cleaning and drying according to the first example. [Figure 5] FIG. 2 shows relative light unit (RLU) values ​​for a contaminated sample surface according to a first example. [Figure 6] FIG. 6 shows the RLU values ​​of FIG. 5 along with the RLU values ​​for the positive and negative controls for each sample surface. [Figure 7] 1 is a plot of the relative area of ​​the sample surface according to the first example. [Figure 8] 1 is a plot of the complexity of the sample surface according to the first example. [Figure 9] FIG. 10 illustrates the degree of difference in roughness parameters between a surface produced by CNC milling and a surface produced by SLS and post-treated according to the first example. [Figure 10] FIG. 10 is a diagram showing the value of the maximum valley area aspect ratio Sdarx of the sample surface according to the first example. [Figure 11] FIG. 10 is a diagram showing values ​​of the peak apex density Spd per unit area of ​​the sample surface according to the first example. [Figure 12] FIG. 10 is a diagram showing values ​​of the arithmetic mean height Sa of the sample surface according to the first example. [Figure 13] FIG. 11 illustrates the correlation between the percentage of ATS shown in FIG. 4 and the value of Sdarx shown in FIG. 10. [Figure 14] FIG. 12 illustrates the correlation between the percentage of ATS shown in FIG. 4 and the value of Spd shown in FIG. 11. [Figure 15] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the autocorrelation length Sal of the sample surface according to a first example. [Figure 16] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the sharpness Sku of the sample surface according to a first example. [Figure 17] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the pit density Svd on the sample surface according to a first example. [Figure 18] FIG. 5 illustrates the correlation between the percentage of ATS shown in FIG. 4 and the value of the average valley region area Sda of the sample surface according to a first example. [Figure 19] FIG. 5 is a diagram showing the correlation between the percentage of ATS shown in FIG. 4 and the value of the maximum valley region area Sdax of the sample surface according to a first example. [Figure 20]FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the standard deviation of the valley region area Sdaq of the sample surface according to a first example. [Figure 21] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the average peak area Sha of the sample surface according to a first example. [Figure 22] FIG. 5 is a diagram showing the correlation between the percentage of ATS shown in FIG. 4 and the value of the maximum peak area Shax of the sample surface according to a first example. [Figure 23] FIG. 5 is a diagram showing the correlation between the percentage of ATS shown in FIG. 4 and the value of the standard deviation of the peak area Shaq of the sample surface according to a first example. [Figure 24] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the valley area count Sdn on the sample surface according to a first example. [Figure 25] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the peak area count Shn of the sample surface according to a first example. [Figure 26] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the mean valley area equivalent diameter Sded of the sample surface according to a first example. [Figure 27] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the maximum valley area equivalent diameter Sdedx of the sample surface according to a first example. [Figure 28] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the standard deviation of the valley area equivalent diameter Sdedq of the sample surface according to a first example. [Figure 29] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the average peak area equivalent diameter Shed of the sample surface according to a first example. [Figure 30] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the maximum peak area equivalent diameter Shedx of the sample surface according to a first example. [Figure 31]FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the standard deviation of the peak area equivalent diameter Shedq of the sample surface according to a first example. [Figure 32] FIG. 5 illustrates the correlation between the percentage of ATS shown in FIG. 4 and the value of the average valley area shape factor Sdff of the sample surface according to a first example. [Figure 33] FIG. 5 illustrates the correlation between the percentage of ATS shown in FIG. 4 and the value of the maximum valley area shape factor Sdffx of the sample surface according to a first example. [Figure 34] FIG. 5 illustrates the correlation between the percentage of ATS shown in FIG. 4 and the value of the average peak area shape factor Shff of the sample surface according to a first example. [Figure 35] FIG. 5 shows the correlation between the percentage of ATS shown in FIG. 4 and the value of the maximum peak area shape factor Shffx of the sample surface according to a first example. [Figure 36] FIG. 5 illustrates the correlation between the percentage of ATS shown in FIG. 4 and the value of the maximum valley area circularity Sdrnx of the sample surface according to a first example. [Figure 37] FIG. 5 is a diagram showing the correlation between the percentage of ATS shown in FIG. 4 and the value of the maximum peak area roundness Shrnx of the sample surface according to a first example. [Figure 38] 1 is a plot of the advancing and receding contact angles of the sample surface according to the first example. DETAILED DESCRIPTION OF THE INVENTION

[0019] Implementations of the present disclosure are described below with particular reference to manufacturing parts for use in bioprocessing systems and determining the cleanliness of those parts. However, it is understood that the methods described herein may also be used to manufacture parts for use in other settings and to determine the cleanliness of those parts. Furthermore, implementations of the present disclosure are described below with particular reference to determining the cleanliness of additively manufactured parts. However, it is further understood that the methods described herein may also be used to determine the cleanliness of parts manufactured using other manufacturing techniques.

[0020] FIG. 1 is a flow chart of a method 100 for fabricating a part.

[0021] At 110, a part is manufactured using an additive manufacturing process. By way of example, the part may be manufactured using an additive manufacturing process such as powder bed fusion (PBF), laser powder bed fusion (LPBF), or electron beam melting (EBM). Specifically, the PBF process may include selective laser 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.

[0022] At 120, the surface of the part is post-processed to provide one or more surface roughness parameter values ​​within a specified range. In one example, post-processing the surface includes laser polishing the surface.

[0023] In one example, one or more surface roughness parameters can be measured by analyzing images of a surface obtained using a confocal laser scanning microscope (CLSM), such as the VK-X1000 confocal laser scanning microscope available from Keyence Corporation, Osaka, Japan. Surface roughness parameters relate to surfaces of limited scale, and therefore, measuring one or more surface roughness parameters involves first filtering one or more CLSM images of the surface of the part according to an appropriate scale range. In the example described herein, surface feature wavelengths below 2.5 μm are filtered from the CLSM image, and surface feature wavelengths above 11 μm are filtered from the CLSM image. In alternative examples, a different filtering range (e.g., from 2 μm to 25 μm) may be used. However, it is understood that the selection of the filtering range affects the value of the surface roughness parameter. Therefore, implementing a different filtering range may change the values ​​of the surface roughness parameters described below.

[0024] Post-treatment of the part at 120 may include post-treating the surface to provide one or more surface wettability values ​​(e.g., advancing contact angle values ​​and / or receding contact angle values) within a particular range. Dynamic contact angles, including advancing and receding contact angles, can be measured using an optical tensiometer, such as a Theta Lite optical tensiometer available from Biolin Scientific of Gothenburg, Sweden. In one example, a needle method can be used to measure dynamic contact angles, and advancing contact angles are measured using a needle with a viscosity of 0.4 μL / s. -1 The contact angle was measured for an advancing time of 100 seconds at an advancing speed of 0.4 μL / s. -1 The contact angle is measured for a 100-second retraction time with a retraction speed of 0.4 μL / s. Prior to analysis, the samples are disinfected with 70% isopropyl alcohol (IPA) and dried with compressed nitrogen to remove contaminants and dirt particles. To measure the dynamic contact angle, the advancing contact angle is first measured by dispensing an oversized drop of 5 μL onto the sample surface. The needle is then brought close to the surface and the drop volume is reduced to 0.4 μL / s. -1The filling rate is gradually increased at a rate of 0.4 μL s-1. The receding contact angle is then measured by decreasing the drop volume at a dispensing rate of 0.4 μL s-1. Calculation of the advancing and receding contact angles can be performed using software such as OneAttension software available from Biolin Scientific, Gothenburg, Sweden.

[0025] The surface roughness parameter values ​​provided by the surface post-treatment at 120 relate to surface roughness parameters that have been identified to provide a correlation with surface cleanliness. As described in more detail in the examples below, these surface roughness parameters were determined by comparing the post-treated surface of a part manufactured using SLS with the surface of a part manufactured using computer numerical control (CNC) milling. The surface of a part manufactured using CNC milling is representative of surfaces used today in the bioprocessing industry, which have been proven not to support bacterial adhesion and biofilm formation. Determining the relevant roughness parameter(s) does not form part of method 100. Instead, it will be understood based on the discussion below that comparison of the SLS surface with a reference CNC-milled surface will yield the most relevant roughness parameter. The surface post-treatment at 120 is performed to provide the most relevant roughness parameter(s) with a surface roughness parameter(s).

[0026] 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. Specifically, relevant surface roughness parameters are identified based on a comparison of a reference CNC-milled surface with a post-processed SLS surface. In the examples described below, the post-processed SLS surface selected for comparison with the CNC-milled surface was a post-processed SLS surface with a high degree of cleanliness (i.e., a post-processed surface with the same degree of cleanliness as CNC milling). Therefore, the examples below involve determining the cleanliness of various post-processed SLS surfaces and a reference CNC-milled surface to determine the post-processed SLS surface with the highest degree of cleanliness. As previously mentioned, surfaces produced by CNC milling have a high degree of cleanliness and are therefore currently used in bioprocessing equipment. Therefore, in the examples, the surfaces selected for comparison both have a high degree of cleanliness, and the two surfaces were compared to determine the surface roughness parameter with the smallest degree of difference between the surfaces.

[0027] Normalized surface roughness parameters are defined in ISO 25178. Specifically, roughness parameters used to describe surface topography are defined in ISO 25178-2:2022. Most existing studies on the effect of surface roughness on biofilm formation focus solely on the arithmetic mean deviations Ra and Sa, which represent the average height from the two-dimensional contour and three-dimensional surface, respectively. In contrast, the present disclosure involves the examination of several different surface roughness parameters that have been found to be of greater relevance to surface cleanliness than the arithmetic mean deviations Ra and Sa.

[0028] Specifically, the surface of the part is measured according to ISO 25178-2:2022 to determine the following 25 surface roughness parameter values: sharpness (Sku), peak density (Spd) (mm -2 ), dent density Svd (mm -2 ), average valley area area Sda(mm -2 ), maximum valley area Sdax(μm 2), valley area area Sdaq(μm 2 ) standard deviation, average peak area Sha (μm 2 ), maximum peak area Shax(μm 2 ), mountain area area Shaq(μm 2 ), standard deviation of valley area equivalent diameter Sdedq (μm), valley area count Sdn, peak area count Shn, mean valley area equivalent diameter Sded (μm), maximum valley area equivalent diameter Sdedx (μm), standard deviation of valley area equivalent diameter Sdedq (μm), mean peak area equivalent diameter Shed (μm), maximum peak area equivalent diameter Shedx (μm), standard deviation of peak area equivalent diameter Shedq (μm), mean valley area shape factor Sdff, maximum valley area shape factor Sdffx, mean peak area shape factor Shff, maximum peak area shape factor Shffx, maximum valley area circularity Sdrnx, maximum peak area circularity Shrnx, maximum valley area aspect ratio Sdarx, and autocorrelation length Sal. These surface roughness parameters are identified in the examples below as being highly similar based on a comparison of surfaces found to have high degrees of cleanliness and as being parameters that have a strong correlation with surface cleanliness.

[0029] ISO 25178-2:2022 includes several parameters in the "feature" parameter category. Many of the parameters listed above fall within this category. According to this classification, there are three types of features: areal features (peaks and valleys), line features (paths and ridges), and point features (vertices, depressions, and saddle points). Surface roughness parameters related to areal surface features include parameters related to the height, area, volume, and count (quantity) of areal surface features (peaks and valleys). The related surface roughness parameters listed above include all surface roughness parameters in ISO 25178-2:2022 that relate to the area of ​​areal surface features.

[0030] To identify peak and valley regions in a surface, a watershed algorithm is typically applied to divide the surface into regions. Smaller sections are then pruned using a Wolf pruning method, which removes regions below a certain height / depth threshold (e.g., 5% of the maximum surface height Sz). Surface segmentation can be performed using surface analysis software, such as the MountainsLab (RTM) software defined in ISO 25178-2:2022 and available from Digital Surf, Besancon, France.

[0031] Additional surface roughness parameters related to areal surface features include parameters related to the circularity, shape factor, equivalent diameter, and aspect ratio of the areal surface feature. Circularity is the ratio of the horizontal area of ​​a motif (peak or valley area) to the area of ​​a circle with a diameter equal to the maximum diameter. A round object will yield a value of 1, while an elliptical object will yield a value less than 1. The shape factor is a measure of the compactness of the shape (i.e., the fraction of the volume that is filled). An elliptical object will yield a value close to zero, while a compact object will yield a value close to 1. The equivalent diameter is the diameter of a circle with the same area as the motif (peak or valley area). The aspect ratio is the ratio of the maximum diameter to the minimum diameter and distinguishes between compact and elliptical motifs (a disk would have a value of 1, while an elliptical motif would have a value greater than 1).

[0032] As explained above, the values ​​of the surface roughness parameters listed above depend on the scale to which the surface is limited. The surface of the part can be post-processed at 120 to provide one or more surface roughness parameter values ​​within the ranges set forth in the following paragraphs. The surface 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.

[0033] The post-treatment of the surface at 120 may provide one or more of the following:

[0034] (i) A limited surface sharpness Sku on the scale of 2.5 μm to 11 μm, between 6 and 20, preferably between 7 and 18, more preferably between 8 and 16, or most preferably between 9 and 14. Sharpness (Sku) is a measure of surface sharpness, with larger Sku values ​​indicating sharper peaks and valleys (as opposed to rounder features).

[0035] (ii) 3000 mm -2 From 10,000 mm -2 Between, preferably 3500 mm -2 to 9000mm -2 Between 4000mm and 5000mm, more preferably 4000mm -2 to 8000mm -2 Between 4500mm and 5000mm, more preferably 4500mm -2 From 7500mm -2 Between 5000mm and 6000mm, more preferably 5000mm -2 to 7000mm -2 Between or most preferably 5500mm -2 From 6500mm -2 The surface peak density Spd is limited to a scale between 2.5 μm and 11 μm.

[0036] (iii) 3000mm -2 From 10,000 mm -2 Between, preferably 3500 mm -2 to 8000mm -2 Between 4000mm and 5000mm, more preferably 4000mm -2 to 7000mm -2 Between 4500mm and 5000mm, more preferably 4500mm -2 to 6000mm -2 or, most preferably, 4000 mm -2 to 5000mm -2 The limited surface pit density Svd is between 2.5 μm and 11 μm in scale.

[0037] (iv) 50 μm 2 to 600 μm 2 Between 75 μm and 85 μm, preferably 75 μm 2 to 500 μm2 Between 100 μm and 100 μm, more preferably 2 to 400 μm 2 Between 125 μm and 150 μm, more preferably 125 μm 2 to 300 μm 2 or, most preferably, 150 μm 2 to 250 μm 2 The mean valley area Sda of the limited surface on the scale between 2.5 μm and 11 μm.

[0038] (v) 7000 μm 2 Less than 6000 μm, preferably 2 Less than 5000 μm, more preferably 2 Less than 4000 μm, more preferably 2 Less than, or most preferably, 3000 μm 2 The maximum valley area Sdax of a limited surface on a scale of less than 2.5 μm to 11 μm.

[0039] (vi) 600 μm 2 Less than 500 μm, preferably 2 less than 400 μm, more preferably 2 less than 300 μm, more preferably 2 Less than, and most preferably 250 μm 2 The standard deviation of the confined surface valley area Sdaq on the scale from 2.5 μm to 11 μm is less than

[0040] (vii) 50 μm 2 to 500 μm 2 Between 75 μm and 85 μm, preferably 75 μm 2 to 400 μm 2 Between 100 μm and 100 μm, more preferably 2 to 300 μm 2 Between 125 μm and 150 μm, more preferably 125 μm 2 to 250 μm 2 or, most preferably, 150 μm 2 to 200 μm 2 The average peak area Sha of the limited surface on a scale between 2.5 μm and 11 μm.

[0041] (viii) 6000 μm 2 Less than 5000 μm, preferably 2 Less than 4000 μm, more preferably 2 Less than 3000 μm, more preferably 2 Less than, or most preferably, 2000 μm 2 The maximum peak area Shax of a limited surface on a scale of less than 2.5 μm to 11 μm.

[0042] (ix) 500 μm 2 Less than 400 μm, preferably 2 less than 300 μm, more preferably 2 less than 250 μm, more preferably 2 less than, and most preferably 200 μm 2 The standard deviation of the area Shaq for limited surface peaks on the scale of 2.5 μm to 11 μm is less than 0.01 μm.

[0043] (x) a confined surface valley area count Sdn on the scale 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.

[0044] (xi) A confined surface peak area count Shn on the scale 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.

[0045] (xii) Mean valley area equivalent diameter Sded of the confined surface on a scale of 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, up to 2.5 μm and 11 μm.

[0046] (xiii) A maximum valley area equivalent diameter Sdedx of the limited surface on a scale of 2.5 μm to 11 μm, 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.

[0047] (xiv) A standard deviation of the confined surface valley area equivalent diameter Sdedq on the scale 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.

[0048] (xv) The average peak area equivalent diameter (Shed) of the confined surface on a scale of 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, up to 2.5 μm and 11 μm.

[0049] (xvi) A maximum peak area equivalent diameter Shedx of a limited surface on a scale of 2.5 μm to 11 μm, 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.

[0050] (xvii) A standard deviation of the confined surface peak area equivalent diameter Shedq on the scale 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.

[0051] (xviii) An average valley area shape factor Sdff for confined surfaces on the scale from 2.5 μm to 11 μm, 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.

[0052] (xix) A maximum valley area shape factor Sdffx for confined surfaces on the scale from 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.

[0053] (xx) an average peak area shape factor Shff of the confined surface on the scale 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;

[0054] (xxi) A maximum peak area shape factor Shffx of a limited surface on a scale from 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.

[0055] (xxii) A maximum valley area roundness Sdrnx of the confined surface on the scale from 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.

[0056] (xxiii) A maximum peak area roundness Shrnx of the limited surface on the scale 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.

[0057] (xxiv) A maximum valley area aspect ratio Sdarx of the confined surface on the scale from 2.5 μm to 11 μm, between 11 and 18, preferably between 12 and 17, more preferably between 13 and 16, and most preferably between 14 and 15.

[0058] (xxv) A surface autocorrelation length Sal 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, limited to a scale of 2.5 μm to 11 μm.

[0059] The post-processed surface may have two or more of the surface roughness parameter values ​​listed in (i) through (xxv). In one example, the post-processed surface may have all of the surface roughness parameter values ​​listed in (i) through (xxv).

[0060] In one particular example, the post-processed surface may have one or more of the surface roughness parameter values ​​listed in (i), (ii), (iv), and (vii) (and, for example, may have all of the surface roughness parameter values ​​listed in (i), (ii), (iv), and (vii)). In this example, the post-processed surface may have one or more of the surface roughness parameter values ​​listed in (iii), (v), (viii), (xviii), (xx), and (xxv).

[0061] Given the strong correlation of these parameters with surface cleanliness, the surface of a manufactured part is likely to be clean if the value of one or more of the above roughness parameters is similar to the corresponding value for a surface produced by CNC milling and / or a clean, post-processed surface of a part produced by additive manufacturing. Thus, post-processing the surface to include one or more of these values ​​is likely to result in a surface that is clean.

[0062] As previously explained, the post-treatment of the part at 120 may include post-treating the surface to provide one or more surface wettability values ​​(e.g., advancing contact angle values ​​and / or receding contact angle values) within a particular range. Specifically, the post-treatment of the surface at 120 may provide one or more of the following:

[0063] (xxvi) an advancing contact angle of the post-treated surface 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.

[0064] (xxvii) A receding contact angle of the post-treated surface between 55 degrees and 95 degrees, preferably between 57.5 degrees and 92.5 degrees, more preferably between 60 degrees and 90 degrees, more preferably between 62.5 degrees and 87.5 degrees, or most preferably between 65 degrees and 85 degrees.

[0065] Once a part has been manufactured using method 100, depending on the post-processing performed at 120, the surface of the part will have one or more of the surface roughness parameter values ​​listed in (i) through (xxv), and optionally one or more of the surface wettability values ​​listed in (xxvi) and (xxvii).

[0066] A part produced using method 100 is an additively manufactured part. Additively manufactured parts can be formed from materials such as polymers. Additionally, additively manufactured parts can be formed using layers of powder material bonded together using heat using processes such as PBF (e.g., SLS). This means that an additively manufactured part can include a first region (or first regions) where the material is not melted and a second region (or second regions) where the material has been melted and re-solidified.

[0067] Method 100 enables the production of additively manufactured parts with clean surfaces. Specifically, by adjusting the post-processing applied at 120 to include one or more of the surface roughness parameter values ​​listed above, the additively manufactured part can be certified as clean without the need for inefficient and time-consuming certification methods. The current process for certifying the cleanliness of additively manufactured parts involves destructive testing of a certain number of prototypes, resulting in additional waste and reduced manufacturing efficiency. Specifically, an alternative method for certifying the cleanliness of additively manufactured parts involves contaminating a certain number of prototypes to promote biofilm growth before attempting to clean them and determining the effectiveness of such cleaning. Such methods are time-consuming due to the need for biofilm growth. Such methods also involve manual labor, potentially introducing additional contamination as a result of manual handling of the selected prototypes being tested. Such methods require the production of additional parts to be tested, which may require many parts to be tested to provide satisfactory effectiveness. Each surface must also be measured using a profilometer or microscope before a determination can be made that the part is clean.

[0068] In contrast, method 100 enables certification of the cleanliness of an additively manufactured part produced using method 100 by adjusting the post-processing of the surface of the additively manufactured part to provide one or more areal surface roughness values ​​that correspond to the surface roughness values ​​of a clean surface produced using CNC milling.

[0069] FIG. 2 is a flow chart of a method 200 for determining the cleanliness of a surface of a component.

[0070] At 210, a part is manufactured. The part includes a surface that is 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.

[0071] At 220, one or more surface roughness parameters of the surface are measured (e.g., from a CLSM image as described with reference to method 100). The method may include filtering the CLSM image using an appropriate scale range (e.g., from 2.5 μm to 11 μm).

[0072] At 230, a determination is made as to whether the surface is clean based on the one or more surface roughness parameters measured at 220.

[0073] If the surface is determined to be clean at 230, the method 200 may include classifying the part as clean at 240.

[0074] If the surface is determined to be not clean at 230, the method 200 may include modifying a design process associated with the design of the surface at 250. Alternatively or additionally, if the surface is determined to be not clean at 230, 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.

[0075] The one or more surface roughness parameters measured at 220 are the sharpness Sku, peak density Spd (mm -2 ), dent density Svd (mm -2 ), average valley area area Sda(mm -2 ), maximum valley area Sdax(μm 2 ), valley area area Sdaq(μm 2 ) standard deviation, average peak area Sha (μm 2 ), maximum peak area Shax(μm 2 ), mountain area area Shaq(μm 2), standard deviation of valley region equivalent diameter Sdedq (μm), valley region count Sdn, peak region count Shn, mean valley region equivalent diameter Sded (μm), maximum valley region equivalent diameter Sdedx (μm), standard deviation of valley region equivalent diameter Sdedq (μm), mean peak region equivalent diameter Shed (μm), maximum peak region equivalent diameter Shedx (μm), standard deviation of peak region equivalent diameter Shedq (μm), mean valley region shape factor Sdff, maximum valley region shape factor Sdffx, mean peak region shape factor Shff, maximum peak region shape factor Shffx, maximum valley region circularity Sdrnx, maximum peak region circularity Shrnx, maximum valley region aspect ratio Sdarx, and autocorrelation length Sal.

[0076] With the exception of kurtosis (Sku) and autocorrelation length (Sal), each of the above parameters is within the family of "feature" parameters defined in ISO 25178-2:2022. This family defines three different types of features: area features (peak and valley areas), line features (paths and ridges), and point features (peaks and depressions). Thus, the one or more roughness parameters measured at 220 may include one or more surface roughness parameters related to surface features (as defined in ISO 25178-2:2022).

[0077] Many of the above parameters in the family of "feature" parameters in ISO 25178-2:2022 relate to areal features (i.e., peak and valley areas). Thus, the one or more roughness parameters measured at 220 may include one or more surface roughness parameters related to areal surface features.

[0078] The surface roughness parameters listed above include all characteristic parameters in ISO 25178-2:2022 related to the area of ​​peaks in a surface (i.e., Sha, Shax, Shaq) and all characteristic parameters in ISO 25178-2:2022 related to the area of ​​valleys in a surface (i.e., Sda, Sdax, Sdaq). Thus, the one or more roughness parameters measured at 220 may include one or more surface roughness parameters related to the area of ​​areal surface features (i.e., the area of ​​surface peaks and / or the area of ​​surface valleys as defined in ISO 25178-2:2022).

[0079] The surface roughness parameters listed above include all characteristic parameters in ISO 25178-2:2022 related to the equivalent diameter of a peak area in a surface (i.e., Shed, Shedx, Shedq) and all characteristic parameters in ISO 25178-2:2022 related to the equivalent diameter of a valley area in a surface (i.e., Sded, Sdedx, Sdedq). Thus, the one or more roughness parameters measured at 220 may include one or more surface roughness parameters related to the equivalent diameter of areal surface features (i.e., the equivalent diameter of a surface peak area and / or the equivalent diameter of a surface valley area as defined in ISO 25178-2:2022).

[0080] After filtering the surface features to remove surface feature wavelengths below 2.5 μm and to remove surface feature wavelengths above 11 μm, the surface may be determined to be clean at 230 if one or more of the following conditions are met:

[0081] (i) The surface sharpness Sku, limited to the scale of 2.5 μm to 11 μm, is between 6 and 20, preferably between 7 and 18, more preferably between 8 and 16, or most preferably between 9 and 14. The sharpness (Sku) is a measure of the sharpness of a surface, with larger Sku values ​​indicating sharper peaks and valleys (as opposed to rounder features).

[0082] (ii) The surface peak density Spd of the limited scale from 2.5 μm to 11 μm is 3000 mm -2 From 10,000 mm -2 Between, preferably 3500 mm -2 to 9000mm -2 Between 4000mm and 5000mm, more preferably 4000mm -2 to 8000mm -2 Between 4500mm and 5000mm, more preferably 4500mm -2 From 7500mm -2 Between 5000mm and 6000mm, more preferably 5000mm -2 to 7000mm -2 Between or most preferably 5500mm -2 From 6500mm -2 It is between.

[0083] (iii) The density of limited surface depressions Svd on the scale of 2.5 μm to 11 μm is 3000 mm -2 From 10,000 mm -2 Between, preferably 3500 mm -2 to 8000mm -2 Between 4000mm and 5000mm, more preferably 4000mm -2 to 7000mm -2 Between 4500mm and 5000mm, more preferably 4500mm -2 to 6000mm -2 or, most preferably, 4000 mm -2 to 5000mm -2 It is between.

[0084] (iv) The average valley area Sda of the limited surface of the scale from 2.5 μm to 11 μm is 50 μm 2 to 600 μm 2 Between 75 μm and 85 μm, preferably 75 μm 2 to 500 μm 2 Between 100 μm and 100 μm, more preferably 2 to 400 μm 2 Between 125 μm and 150 μm, more preferably 125 μm 2 to 300 μm 2 or, most preferably, 150 μm 2 to 250 μm2 It is between.

[0085] (v) The maximum valley area Sdax of the limited surface of the scale from 2.5 μm to 11 μm is 7000 μm 2 Less than 6000 μm, preferably 2 Less than 5000 μm, more preferably 2 Less than 4000 μm, more preferably 2 Less than, or most preferably, 3000 μm 2 is less than.

[0086] (vi) The standard deviation of the valley area Sdaq of the limited surface on the scale from 2.5 μm to 11 μm is 600 μm 2 Less than 500 μm, preferably 2 less than 400 μm, more preferably 2 less than 300 μm, more preferably 2 Less than, and most preferably 250 μm 2 is less than.

[0087] (vii) The average peak area Sha of the limited surface of the scale from 2.5 μm to 11 μm is 50 μm 2 to 500 μm 2 Between 75 μm and 85 μm, preferably 75 μm 2 to 400 μm 2 Between 100 μm and 100 μm, more preferably 2 to 300 μm 2 Between 125 μm and 150 μm, more preferably 125 μm 2 to 250 μm 2 or, most preferably, 150 μm 2 to 200 μm 2 It is between.

[0088] (viii) The maximum peak area Shax of the limited surface of the scale from 2.5 μm to 11 μm is 6000 μm 2 Less than 5000 μm, preferably 2 Less than 4000 μm, more preferably 2 Less than 3000 μm, more preferably 2 Less than, or most preferably, 2000 μm 2is less than.

[0089] (ix) The standard deviation of the area of ​​the limited surface peaks on the scale of 2.5 μm to 11 μm is 500 μm. 2 Less than 400 μm, preferably 2 less than 300 μm, more preferably 2 less than 250 μm, more preferably 2 less than, and most preferably 200 μm 2 is less than.

[0090] (x) The confined surface valley region count Sdn on the scale from 2.5 μm to 11 μm is 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.

[0091] (xi) The confined surface peak area count Shn on the scale from 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.

[0092] (xii) The mean valley area equivalent diameter Sded of the confined surface on the scale from 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.

[0093] (xiii) The maximum valley area equivalent diameter Sdedx of the limited surface on the scale from 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.

[0094] (xiv) The standard deviation of the confined surface valley equivalent diameter Sdedq on the scale 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.

[0095] (xv) The average peak area equivalent diameter (Shed) of the confined surface on the scale from 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.

[0096] (xvi) The maximum peak area equivalent diameter Shedx of the limited surface on the scale from 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.

[0097] (xvii) The standard deviation of the peak area equivalent diameter Shedq of the limited surface on the scale 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.

[0098] (xviii) The average valley area shape factor Sdff of the confined surface on the scale 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.

[0099] (xix) The maximum valley area shape factor Sdffx of the confined surface on the scale from 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.

[0100] (xx) The average peak area shape factor Shff of the limited surface on the scale from 2.5 μm to 11 μm is 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.

[0101] (xxi) The maximum peak area shape factor Shffx of the limited surface on the scale from 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.

[0102] (xxii) The maximum valley area circularity Sdrnx of the limited surface on the scale from 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.

[0103] (xxiii) The maximum peak area circularity Shrnx of the limited surface on the scale from 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.

[0104] (xxiv) The maximum valley area aspect ratio Sdarx of the confined surface on the scale from 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.

[0105] (xxv) The surface autocorrelation length Sal on the scale limited to 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.

[0106] After filtering the surface features to remove surface feature wavelengths below 2.5 μm and to remove surface feature wavelengths above 11 μm, if two or more of the conditions listed in (i) through (xxv) are met, the surface may be determined to be clean at 230. In one example, the surface may be determined to be clean at 230 if all of the conditions listed in (i) through (xxv) are met.

[0107] In one particular example, after filtering the surface features to remove surface feature wavelengths below 2.5 μm and to remove surface feature wavelengths above 11 μm, if one or more of the conditions listed in (i), (ii), (iv), and (vii) (e.g., all of the conditions listed in (i), (ii), (iv), and (vii)) are met, the surface may be determined to be clean at 230. After filtering the surface features to remove surface feature wavelengths below 2.5 μm and to remove surface feature wavelengths above 11 μm, the surface may be determined to be clean at 230 if one or more of the conditions listed in (iii), (v), (viii), (xviii), (xx), and (xxv) are met.

[0108] Method 200 enables determining whether a part surface (specifically, a surface of an additively manufactured part) can be cleaned in an efficient manner. Current processes for determining the cleanliness of additively manufactured parts involve destructive testing of a certain number of prototypes, resulting in additional waste and reduced manufacturing efficiency. Specifically, an alternative method for determining the cleanliness of an additively manufactured part involves contaminating a certain number of prototypes to promote biofilm growth before attempting to clean the prototypes and determining the effectiveness of such cleaning. Such methods are time-consuming due to the need for biofilm growth. Such methods also involve manual labor, potentially introducing additional contamination as a result of manual handling of the selected prototypes being tested. Such methods require the fabrication of additional parts to be tested, potentially requiring many parts to be tested to provide satisfactory effectiveness. Each surface also needs to be measured using a profilometer or microscope for a determination to be made that the part is clean.

[0109] In contrast, method 200 enables a determination of whether an additively manufactured part is clean based on measuring one or more areal surface roughness values ​​that have the strongest correlation with clean surfaces manufactured using CNC milling, which enables certification of additively manufactured parts as clean in a more efficient manner. [Example]

[0110] This example describes a comparison of the surface of a part manufactured using SLS with the surface of a part manufactured using CNC milling, both of which have high cleanliness (and are therefore similarly clean). This example involves determining the surface roughness parameter with the greatest similarity based on a comparison of these highly clean surfaces to establish a surface roughness parameter that correlates strongly with cleanliness.

[0111] In this example, two grades of polypropylene (PP), a medical-grade isotactic PP homopolymer and an industrial-grade isotactic PP, were used as accepted materials. The medical-grade isotactic PP homopolymer available from Nordbergs Tekniska AB of Vallentuna, Sweden, was used for conventional milling fabrication, while the industrial-grade isotactic PP available from Ricoh Co., Ltd. of Tokyo, Japan, was used for SLS.

[0112] A CNC reference specimen (identified here as PP_CNC) was fabricated using a VF-8 milling machine available from Haas Automation, Inc. of Oxnard, CA, USA. A circular plate specimen with a diameter of 25 mm and a height of 5 mm was selected as the reference specimen.

[0113] SLS disk samples (here identified using PP_SLS or PP_PBF) with a diameter of 25 mm and a height of 5 mm were fabricated by SLS. The layer thickness used in SLS was 0.1 mm with a printing tolerance of ±0.3%. The sample orientation during the printing process was horizontal (0°).

[0114] Different surface textures of the PP_SLS samples were obtained using six different types of post-treatments: (i) no post-treatment (i.e., as-printed SLS samples), identified here as PP_SLS_0h or PP_PBF_0h; (ii) SLS samples tumbled for 5 hours using intermediate abrasive media, identified here as PP_SLS_5h or PP_PBF_5h; and (iii) SLS samples tumbled for 10 hours using intermediate abrasive media, identified here as PP_SLS_10h or PP_PBF_10h. (iv) SLS samples tumbled for 15 hours using medium polishing media, identified here as PP_SLS_15h or PP_PBF_15h; (v) SLS samples tumbled for 13 hours using medium polishing media and polished for 3 hours using small polishing media, identified here as PP_SLS_13h_3P or PP_PBF_13h_3P; and (vi) SLS samples laser polished (LP) using a technique developed by the Fraunhofer Institute, identified here as PP_SLS_LP or PP_PBF_LP. Post-processing technique (vi) involves irradiating the surface of the SLS sample with laser radiation to melt material near the surface, which closes cracks and pores in the surface and reduces surface roughness, allowing the surface to resolidify in a smooth state.

[0115] For tumble surfacing, ceramic triangular abrasive media (CTAM) with a coarse grain, large grain size, matte finish, and medium media wear rate was used along with detergent. The detergent was used at a fixed concentration and dosage for each sample and did not contribute chemical energy to the surfacing process. Instead, the detergent was used to optimize the mechanical abrasive energy provided by the media. In the tumble process, the combination of a circular flow in the immersion tank and the application of detergent caused the sample to tumble in the detergent flow and remain below the detergent surface in the immersion tank.

[0116] Dynamic contact angles, including advancing and receding contact angles, were measured using a Theta Lite optical tensiometer available from Biolin Scientific, Gothenburg, Sweden. Advancing contact angles were measured at 0.4 μL s -1 The contact angle was measured for an advancing time of 100 seconds at an advancing speed of 0.4 μL / s. -1 The retraction time was measured for 100 seconds at a retraction speed of 100.

[0117] Prior to analysis, samples were disinfected with 70% isopropyl alcohol (IPA) and dried with compressed nitrogen to remove contaminants and dirt particles. The needle method was used to measure the dynamic contact angle. First, the advancing contact angle was measured by dispensing an oversized drop of 5 μL onto the sample surface. Then, the needle was brought close to the surface, and the drop volume was reduced to 0.4 μL. -1 The filling rate was gradually increased at 0.4 μL s-1. The receding contact angle was then measured by decreasing the drop volume at a dispensing rate of 0.4 μL s-1. Calculations of the advancing and receding contact angles were performed using OneAttension software available from Biolin Scientific, Gothenburg, Sweden. All three measurements were performed at room temperature for one sample.

[0118] Surface roughness was determined by a VK-X1000 confocal laser scanning microscope (CLSM) available from Keyence Corporation, Osaka, Japan. Each surface image was 700 × 525 μm. 2The surface had a surface of 100 mm x 100 mm, which was divided into the mentioned areas in order to calculate each roughness parameter in each of four identical areas. The following statistical data were reported: mean, standard deviation, maximum, and minimum. The analysis was performed using Multi-File Analyser software. Three images per sample were taken at 20x magnification. The following roughness parameters were calculated: 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, Shvq, Sdd, Sddx, Sddq, Sdax, Sdaq, Sdvx, Sdvq, Shn, Sdn, Shrn, Shrnx, Shrnq, Shff, Shffx, Shffq, Shed, Shedx, Shedq, Shar, Sharx, Sharq, Sdrn, Sdrnx, Sdrnq, Sdff, Sdffx, Sdffq, Sded, Sdedx, Sdedq, Sdar, Sdarx, and Sdarq were calculated according to ISO 25178-2:2022.

[0119] The top surfaces of the samples were visualized using a TM-1000 tabletop scanning electron microscope (SEM) available from Hitachi, Ltd., Tokyo, Japan, with an accelerating voltage of 15 kV. No conductive coating was used for the tabletop SEM evaluation. Images were acquired at magnifications of 40x, 120x, 150x, and 180x.

[0120] Cleanliness was initially determined using a contamination study with an artificial test soil (ATS). The ATS is a standardized test soil with proteins, hemoglobin, carbohydrates, cellulose, lipids, and mucins for simulated use testing. The ATS is typically used for soiling medical devices, such as flexible endoscopes, for the purpose of performing cleanliness certification. The ATS provides a conditioning membrane on the surface, modeled after conditioning membranes typically used in biopharmaceutical applications with proteins and carbohydrates.

[0121] 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 (identified as "A" and measured in mg) that was a sample recovered from a defined surface area on a test sample not soiled with ATS; (ii) a positive device control (identified as "B" and measured in mg) that was a sample recovered from a defined surface area on a test sample soiled with ATS and dried in an incubator at 37°C for 3 hours; and (iii) a test device (identified as "C" and measured in mg) that was a sample recovered from a defined surface area on a test sample soiled with ATS and dried in an incubator at 37°C for 3 hours, then cleaned as specified. Three specimens were analyzed. Cleanliness can be determined using either the residual level of test object (mg) after cleaning = CA or the percentage removal of test object = ((BA) - (CA)) x 100 / (BA).

[0122] The contamination study also involved measuring relative light units (RLU) based on the detection of adenosine 5'-triphosphate (ATP) bioluminescence, a method based on the detection of ATP using a fluorogenic enzyme and a luciferin cofactor.

[0123] The experimental procedure included three main steps: (i) purification, (ii) ATS preparation, and (iii) ATS application. In the purification step, the samples were washed with deionized water, immersed in 70% isopropyl alcohol (IPA), and dried with nitrogen. One specimen from each sample batch was then removed to measure the cleanliness (negative device control) and ensure the efficiency of the purification step. In the ATS preparation step, ATS was applied to sterile water at a concentration of 0.09 g / ml. The mixture was vortexed for 10 minutes and then allowed to rest for 20 minutes before application.

[0124] In the ATS application step, each sample (maintained in a sterilized Petri dish) was first weighed. Next, ATS was applied to each sample using 1 ml of contaminant, covering the entire sample surface. The samples were then dried for 3 hours at 37°C in a shaker-incubator, which provided consistent temperature and humidity during the drying process. The samples (in the sterilized Petri dish) were then reweighed. The samples were then washed in an autoclaved beaker at 121°C for 30 minutes. The beaker contained 30 ml of 70% IPA for 20 minutes at a shaking speed of 100 rpm. The samples were then placed in a sterilized Petri dish and allowed to dry for 2 hours at 37°C before being weighed again. ATP measurements were performed by wiping each surface for 30 seconds and immediately measuring the RLU.

[0125] Figure 3 shows the weight of ATS deposited on the top surface of the samples. Specifically, an average of 0.102±0.011 g of ATS was deposited on each surface after drying for 3 hours, thereby ensuring consistent reproducibility of the method for depositing ATS onto the sample surface.

[0126] Figure 4 shows the percentage of ATS removed after washing and drying in the incubator. For the PP_CNC and PP_SLS_LP samples, the entire ATS layer is peeled off from the surface (i.e., 100% of the ATS is removed).

[0127] Figure 5 shows the ATP results obtained for the experiments performed after washing and drying. As shown, the least contamination was obtained for the PP_CNC and PP_SLS_LP samples. 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 processing. Conversely, high RLU values ​​were found for all tumbled SLS samples, regardless of the post-treatment time applied. At the same time, a higher standard deviation in RLU was also found for the tumbled SLS samples due to the inconsistent surface texture of the samples.

[0128] Figure 6 shows the RLU values ​​of the negative and positive controls to verify the reproducibility of the experimental procedure. As shown in Figure 6, the values ​​for the test samples produced by CNC and SLS followed by LP are within the values ​​of the negative and positive controls, while the values ​​for the rest of the AM samples are close to or exceed the values ​​of their respective positive controls.

[0129] It can be concluded that LP and CNC are two examples of manufacturing processes that reduce the degree of contamination as measured by both gravimetric measurements (FIGS. 13 and 14) and ATP bioluminescence (FIGS. 15 and 16). On the other hand, the SLS-fabricated, mechanically tumbled surfaces show no evidence of improved cleanliness.

[0130] In this example, the filter applied to the CLSM image is determined by identifying the most significant scales for surface characteristics, as identified by scale-sensitive fractal analysis. Scale-sensitive fractal analysis is a multiscale method that includes area-scale analysis to calculate the area of ​​a surface as a function of scale. Using this method, the relative area and complexity of a surface are calculated using a virtual tiling algorithm in which the surface topography is covered with triangular tiles. Each tile has the same area and represents a scale of measurement. The relative area at a particular 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 a plot of the relative area at each scale multiplied by the orders of magnitude.

[0131] Figure 7 shows a plot of the relative surface area of ​​the different samples as a function of scale. In Figure 7, PP_SLS_LP is 2 PP_CNC is the data series with the smallest value on the scale of 1 μm 2 PP_SLS_13h_3P is the data series with the second smallest value on the scale of 1 μm 2 PP_SLS_0h is the data series with the largest value on the scale of 1 μm 2 The data series with the second largest value on the 1 μm scale is the tumbled SLS sample, which is time-matched to the post-treatment. 2 The graph shows gradually decreasing values ​​on a scale of

[0132] For PP_CNC, Figure 7 shows the small scale (1 μm 2 The relative area reaches a maximum of 1.4 at less than 60 μm 2 The relevant scale is therefore 1 μm 2to 60 μm 2 For PP_SLS_LP, the surface features are up to 20 μm 2 The relative area is the smallest of all samples (1.12). The trend for PP_SLS_LP is similar to that for PP_CNC.

[0133] Figure 7 shows that PP_SLS_0h has a large relative area (1.9) at small scales, up to 30,000 μm 2 The results also show that the roughness is relatively high up to the scale of 60 μm, which means that the roughness is accounted for at all scales. The effect of tumbling reduces the relative area, but the roughness is not significant at the scale of large roughness (60 μm). 2 It can still be observed in (greater than 100%). There is a negligible effect of tumbling after 10 hours of post-treatment, as shown by the similarity of the trends for PP_SLS_10h and PP_SLS_15h. These tumbling times resulted in a maximum relative area of ​​1.45. Use of abrasive media for 3 hours did not affect the surface texture.

[0134] Figure 8 shows a plot of the surface complexity of different samples as a function of scale. 2 The data series in increasing order of complexity on this scale are PP_SLS_0h, PP_SLS_LP, PP_SLS_10h, PP_SLS_15h (very close in value to PP_SLS_10h), PP_SLS_13h_3P, PP_SLS_5h, and PP_CNC.

[0135] As shown in Figure 8, PP_CNC has a 2 μm 2 For PP_SLS_0h, the maximum complexity (100) is observed at the large scale (approximately 2000 μm) with the largest complexity at the medium and large scales. 2For 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 similar trend to the as-printed samples. PP_SLS_LP exhibits a complexity of 100 μm. 2 It has a gradient from to identity and has minimal complexity at small scales.

[0136] From Figure 8, the scale range used to filter the raw CLSM images and calculate the surface roughness parameters can be obtained. The PP_CNC (reference) sample has a roughness of 3 μm. 2 to 60 μm 2 This scale range is used to determine the area range, which is up to 3 μm. Within this range, surface features of increasing complexity are fabricated during machining. The end point of the area range multiplied by 2 (i.e., 3 μm) is used to calculate the filter length. 2 and 60 μm 2 ) is taken, resulting in a length range from 2.5 μm to 11 μm. This scale is used to apply a band-pass filter (i.e., between 2.5 μm and 11 μm) based on a robust Gaussian filter.

[0137] As explained previously, the PP_CNC and PP_SLS_LP samples were found to have the highest degree of cleanliness. Therefore, the PP_CNC and PP_SLS_LP samples were compared to identify the most relevant roughness parameters by using the smallest degree of difference in the roughness parameters between the samples. This was done using the following equation: 72 surface roughness parameters described in ISO 25178-2:2022 were considered.

[0138]

number

[0139] Figure 9 shows the degree of difference for the 72 evaluated surface roughness parameters. As shown in Figure 9, Sdarx is the roughness parameter that correlates best to explain the similarities when PP_CNC and PP_SLS_LP samples are compared. This parameter is newly included in ISO 25178-2:2022 and is expressed by the formula Sdarx=max(D max / D min ) This parameter distinguishes between compact and elliptical motifs (e.g., Sdarx = 1 for a disk, and Sdarx > 1 for an elliptical motif). Specifically, Sdarx is defined as the maximum aspect ratio of the valley region.

[0140] Figure 10 shows the maximum valley area aspect ratios for the reference (PP_CNC) and AM samples. As shown in Figure 10, the same maximum aspect ratios are found for the PP_CNC and PP_SLS_LP samples, while more rounded valley areas are obtained for the SLS samples post-treated by tumbling. Regardless of the post-treatment time, Sdarx remains roughly constant for each tumbled SLS sample.

[0141] Figure 11 shows the peak density per unit area (Spd) for the reference (PP_CNC) and AM samples. Spd correlates well with ATS removal, with a small degree of difference (close to zero) resulting from a comparison of PP_CNC and PP_SLS_LP, as shown in Figure 9. Figure 11 shows that a larger peak density exists for the PP_CNC and PP_SLS_LP samples (both between 5000 and 6000 mm). -2 (within the range of

[0142] To demonstrate the need to use roughness parameters other than the average roughness, Sa, a plot of Sa is shown in Figure 12. The similarity of the Sa values ​​in Figure 12 demonstrates the limitations of using this single parameter to describe the surface features of components fabricated by CNC machining and AM techniques. Specifically, Sa cannot represent the observed differences between different manufacturing processes.

[0143] Figure 13 shows the correlation between ATS removal and Sdarx, and Figure 14 shows the correlation between ATS removal and Spd. The autocorrelation length (Sal) correlates well with ATS removal, with small degrees of difference (less than 2) resulting from the comparison of PP_CNC and PP_SLS_LP, as shown in Figure 9. The autocorrelation length defines the spatial meridian between each surface feature on the surface. Figure 15 shows the correlation between ATS removal and Sal for each sample. From Figure 15, it can be seen that the reference (PP_CNC) sample has short distances between features, which may hinder the ability of ATS to adhere and bind to the surface, while the AM surface is porous and has larger distances between surface features. As shown in Figures 13-15, the tumbling effect does not improve the efficiency of ATS removal from the surface and, in fact, results in the opposite effect. That is, the as-printed sample (PP_SLS_0h) with greater porosity and average roughness detached a greater percentage of ATS when compared to SLS samples post-treated by tumbling for 5, 10, and 15 hours. Conversely, the SLS samples post-treated by laser abrasion show a similar effect to the CNC samples, as can be seen in Figures 13-15.

[0144] In the Sdarx plot shown in Figure 10, the PP_CNC sample has an Sdarx value of 14.16, and the PP_LP sample has an Sdarx value of 14.23. Post-treatments can be applied to the surfaces of SLS-produced parts to provide Sdarx values ​​consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Sdarx values ​​between 11 and 18, preferably between 12 and 17, more preferably between 13 and 16, and most preferably between 14 and 15.

[0145] In the plot of Spd shown in Fig. 11, the Spd value of the PP_CNC specimen was 5793 mm -2 The Spd value of the PP_LP sample was 5563 mm -2 A post-treatment can be applied to the surface of the SLS-produced part to provide an Spd value that is consistent with the Spd values ​​of the PP_CNC and PP_LP samples. Specifically, the post-treatment can be applied to the surface of the 3000mm -2 From 10,000 mm -2 Between, preferably 3500 mm -2 to 9000mm -2 Between 4000mm and 5000mm, more preferably 4000mm -2 to 8000mm -2 Between 4500mm and 5000mm, more preferably 4500mm -2 From 7500mm -2 Between 5000mm and 6000mm, more preferably 5000mm -2 to 7000mm -2 Between or most preferably 5500mm -2 From 6500mm -2 can be applied to the SLS surface to provide an Spd value between

[0146] In the Sal plot shown in Figure 15, the PP_CNC sample has a Sal value of 2.97 μm, and the PP_LP sample has a Sal value of 3.34 μm. Post-treatments can be applied to the surfaces of SLS-produced parts to provide Sal values ​​consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Sal values ​​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.

[0147] In the Sku plot shown in Figure 16, the PP_CNC sample has an Sku value of 13.73, and the PP_LP sample has an Sku value of 11.26. A post-treatment can be applied to the surface of a part produced by SLS to provide an Sku value consistent with the Sku values ​​of the PP_CNC and PP_LP samples. Specifically, a post-treatment can be applied to the SLS surface to provide an Sku value between 6 and 20, preferably between 7 and 18, more preferably between 8 and 16, or most preferably between 9 and 14.

[0148] In the plot of Svd shown in Figure 17, the Svd value of the PP_CNC specimen was 4944 mm -2 The Svd value of the PP_LP sample was 4570 mm -2 A post-treatment can be applied to the surface of the SLS-produced part to provide an Svd value that is consistent with the Svd values ​​of the PP_CNC and PP_LP samples. Specifically, the post-treatment can be applied to the surface of the Svd value of the PP_CNC and PP_LP samples. -2 From 10,000 mm -2 Between, preferably 3500 mm -2 to 8000mm -2 Between 4000mm and 5000mm, more preferably 4000mm -2 to 7000mm -2 Between 4500mm and 5000mm, more preferably 4500mm -2 to 6000mm -2 or, most preferably, 4000 mm -2 to 5000mm -2 can be applied to the SLS surface to provide an Svd value between

[0149] In the plot of Sda shown in Fig. 18, the Sda value of the PP_CNC sample was 208.4 μm 2 The Sda value of the PP_LP sample was 229.6 μm 2 A post-treatment can be applied to the surface of the SLS-produced part to provide an Sda value that is consistent with the Sda values ​​of the PP_CNC and PP_LP samples. Specifically, the post-treatment can be applied to the surface of the SLS-produced part to provide an Sda value that is consistent with the Sda values ​​of the PP_CNC and PP_LP samples. 2 to 600 μm 2 Between 75 μm and 85 μm, preferably 75 μm 2 to 500 μm 2 Between 100 μm and 100 μm, more preferably 2 to 400 μm 2 Between 125 μm and 150 μm, more preferably 125 μm 2 to 300 μm 2 or, most preferably, 150 μm 2 to 250 μm 2 can be applied to the SLS surface to provide an Sda value between

[0150] In the plot of Sdax shown in Figure 19, the Sdax value of the PP_CNC sample was 1977 μm 2 The Sdax value of the PP_LP sample was 2862 μm 2 A post-treatment can be applied to the surface of the SLS-produced part to provide an Sdax value that is consistent with the Sdax values ​​of the PP_CNC and PP_LP samples. Specifically, the post-treatment can be applied to the surface of the Sdax value of the PP_CNC and PP_LP samples to provide an Sdax value that is consistent with the Sdax values ​​of the PP_LP samples. 2 Less than 6000 μm, preferably 2 Less than 5000 μm, more preferably 2 Less than 4000 μm, more preferably 2 Less than, or most preferably, 3000 μm 2 may be applied to the SLS surface to provide an Sdax value of less than

[0151] In the plot of Sdaq shown in Figure 20, the Sdaq value of the PP_CNC sample was 196.1 μm 2 The Sdaq value of the PP_LP sample was 226.0 μm 2A post-treatment can be applied to the surface of the SLS-produced part to provide an Sdaq value that is consistent with the Sdaq values ​​of the PP_CNC and PP_LP samples. Specifically, the post-treatment can be applied to the surface of the SLS-produced part to provide an Sdaq value that is consistent with the Sdaq values ​​of the PP_CNC and PP_LP samples. 2 Less than 500 μm, preferably 2 less than 400 μm, more preferably 2 less than 300 μm, more preferably 2 Less than, and most preferably 250 μm 2 can be applied to the SLS surface to provide an Sdaq value of less than

[0152] In the plot of Sha shown in Fig. 21, the Sha value of the PP_CNC sample was 177.2 μm 2 The Sha value of the PP_LP sample is 185.4 μm 2 A post-treatment can be applied to the surface of the SLS-produced parts to provide Sha values ​​that are consistent with those of the PP_CNC and PP_LP samples. Specifically, the post-treatment can be applied to the surface of the SLS-produced parts to provide Sha values ​​that are consistent with those of the PP_CNC and PP_LP samples. 2 to 500 μm 2 Between 75 μm and 85 μm, preferably 75 μm 2 to 400 μm 2 Between 100 μm and 100 μm, more preferably 2 to 300 μm 2 Between 125 μm and 150 μm, more preferably 125 μm 2 to 250 μm 2 or, most preferably, 150 μm 2 to 200 μm 2 can be applied to the SLS surface to provide a Sha value between

[0153] In the Shax plot shown in Figure 22, the Shax value of the PP_CNC sample was 1571 μm 2 The Shax value of the PP_LP sample was 1771 μm 2 A post-treatment can be applied to the surface of the SLS-produced part to provide a Shax value that is consistent with the Shax values ​​of the PP_CNC and PP_LP samples. Specifically, the post-treatment can be applied to the surface of the SLS-produced part to provide a Shax value that is consistent with the Shax values ​​of the PP_CNC and PP_LP samples. 2 Less than 5000 μm, preferably 2Less than 4000 μm, more preferably 2 Less than 3000 μm, more preferably 2 Less than, or most preferably, 2000 μm 2 It can be applied to an SLS surface to provide a Shax value of less than

[0154] In the Shaq plot shown in Figure 23, the Shaq value of the PP_CNC sample was 145.5 μm 2 The Shaq value of the PP_LP sample is 158.6 μm 2 A post-treatment can be applied to the surface of the SLS-produced part to provide a Shaq value that is consistent with that of the PP_CNC and PP_LP samples. Specifically, the post-treatment can be applied to the surface of the SLS-produced part to provide a Shaq value that is consistent with that of the PP_CNC and PP_LP samples. 2 Less than 400 μm, preferably 2 less than 300 μm, more preferably 2 less than 250 μm, more preferably 2 less than, and most preferably 200 μm 2 can be applied to the SLS surface to provide a Shaq value of less than

[0155] In the Sdn plot shown in Figure 24, the PP_CNC sample has an Sdn value of 1621 and the PP_LP sample has an Sdn value of 1496. Post-treatments can be applied to the surfaces of parts produced by SLS to provide Sdn values ​​that are consistent with the Sdn values ​​of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Sdn values ​​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.

[0156] In the Shn plot shown in Figure 25, the PP_CNC sample has a Shn value of 1925 and the PP_LP sample has a Shn value of 1850. A post-treatment can be applied to the surface of a part produced by SLS to provide a Shn value that is consistent with the Shn values ​​of the PP_CNC and PP_LP samples. Specifically, a 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.

[0157] In the Sded plot shown in Figure 26, the PP_CNC sample has an Sded value of 14.10 μm, and the PP_LP sample has an Sded value of 14.86 μm. Post-treatments can be applied to the surface of parts produced by SLS to provide Sded values ​​that are consistent with the Sded values ​​of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Sded values ​​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.

[0158] In the plot of Sdedx shown in Figure 27, the PP_CNC sample has an Sdedx value of 48.82 μm, and the PP_LP sample has an Sdedx value of 58.13 μm. Post-treatments can be applied to the surface of parts produced by SLS to provide Sdedx values ​​that are consistent with the Sdedx values ​​of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Sdedx values ​​that are 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.

[0159] In the plot of Sdedq shown in Figure 28, the PP_CNC sample has an Sdedq value of 6.30 μm, and the PP_LP sample has an Sdedq value of 6.51 μm. Post-treatments can be applied to the surface of parts produced by SLS to provide Sdedq values ​​consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Sdedq values ​​that are 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.

[0160] In the Shed plot shown in Figure 29, the PP_CNC sample has a Shed value of 13.15 μm, and the PP_LP sample has a Shed value of 13.48 μm. Post-treatments can be applied to the surface of SLS-produced parts to provide Shed values ​​consistent with those of the PP_CNC samples. Specifically, post-treatments can be applied to SLS surfaces to provide Shed values ​​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.

[0161] In the Shedx plot shown in Figure 30, the PP_CNC sample has a Shedx value of 43.21 μm, and the PP_LP sample has a Shedx value of 45.91 μm. Post-treatments can be applied to the surface of parts produced by SLS to provide Shedx values ​​that are consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Shedx values ​​that are 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.

[0162] In the Shedq plot shown in Figure 31, the PP_CNC sample has a Shedq value of 5.32 μm and the PP_LP sample has a Shedq value of 5.43 μm. Post-treatments can be applied to the surface of parts produced by SLS to provide Shedq values ​​consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Shedq values ​​that are 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.

[0163] In the Sdff plot shown in Figure 32, the PP_CNC sample has an Sdff value of 0.486, and the PP_LP sample has an Sdff value of 0.491. Post-treatments can be applied to the surfaces of SLS-produced parts to provide Sdff values ​​consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Sdff values ​​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.

[0164] In the Sdffx plot shown in Figure 33, the PP_CNC sample has an Sdffx value of 0.847, and the PP_LP sample has an Sdffx value of 0.839. Post-treatments can be applied to the surfaces of SLS-produced parts to provide Sdffx values ​​that are consistent with the Sdffx values ​​of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Sdffx values ​​between 0.815 and 0.875, preferably between 0.825 and 0.865, or more preferably between 0.835 and 0.855.

[0165] In the Shff plot shown in Figure 34, the PP_CNC sample has a Shff value of 0.492, and the PP_LP sample has a Shff value of 0.503. Post-treatments can be applied to the surfaces of SLS-produced parts to provide Shff values ​​consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Shff values ​​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.

[0166] In the Shffx plot shown in Figure 35, the PP_CNC sample has a Shffx value of 0.864, and the PP_LP sample has a Shffx value of 0.871. Post-treatments can be applied to the surfaces of SLS-produced parts to provide Shffx values ​​consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces 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.

[0167] In the Sdrnx plot shown in Figure 36, the PP_CNC sample has an Sdrnx value of 0.837, and the PP_LP sample has an Sdrnx value of 0.850. Post-treatments can be applied to the surfaces of SLS-produced parts to provide Sdrnx values ​​consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Sdrnx values ​​between 0.815 and 0.875, preferably between 0.825 and 0.865, or more preferably between 0.835 and 0.855.

[0168] In the Shrnx plot shown in Figure 37, the PP_CNC sample has a Shrnx value of 0.862, and the PP_LP sample has a Shrnx value of 0.864. Post-treatments can be applied to the surfaces of SLS-produced parts to provide Shrnx values ​​consistent with those of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide Shrnx values ​​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.

[0169] In the advancing and receding contact angle (ACA and RCA) plot shown in FIG. 38, the ACA value for the PP_CNC sample is 89 degrees, and the ACA value for the PP_LP sample is 100 degrees. A post-treatment can be applied to the surface of a part fabricated by SLS to provide an ACA value consistent with the ACA values ​​of the PP_CNC and PP_LP samples. Specifically, a post-treatment can be applied to the SLS surface to provide an ACA value 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. The RCA value for the PP_CNC sample is 68 degrees, and the RCA value for the PP_LP sample is 83 degrees. A post-treatment can be applied to the surface of a part fabricated by SLS to provide an RCA value consistent with the RCA values ​​of the PP_CNC and PP_LP samples. Specifically, post-treatments can be applied to SLS surfaces to provide RCA values ​​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.

[0170] Variations or modifications to the systems and methods described herein are set forth in the following paragraphs.

[0171] It is also understood that the methods described herein are not limited to evaluating surfaces of parts produced by PBF, but may also be applied to surfaces of parts produced by other AM techniques. Furthermore, it is understood that the methods described herein are in fact not limited to evaluating surfaces of parts produced by AM techniques, but may alternatively or additionally be applied to surfaces of parts produced using other manufacturing techniques.

[0172] Additionally, the methods described herein are not limited to evaluating flat surfaces. Specifically, the method of FIG. 2 can be used to certify the cleanliness of other features of manufactured parts, such as pockets, cavities, and corners. This can be done by identifying relevant roughness parameters based on a comparison of the clean features of parts used in bioprocessing applications (e.g., CNC milling) with corresponding features of parts manufactured using manufacturing methods currently used to produce such features.

[0173] The described methods can be implemented using computer-executable instructions. A computer program product or computer-readable medium may contain or store the computer-executable instructions. The computer program product or computer-readable medium may comprise a hard disk drive, flash memory, read-only memory (ROM), CD, DVD, cache, random access memory (RAM), and / or any other storage medium on which information is stored for any period of time (e.g., an extended period of time, a permanent period of time, a short moment, while temporarily buffering, and / or while caching information). A computer program may comprise computer-executable instructions. A computer-readable medium may be tangible or may be a non-transitory computer-readable medium. The term "computer-readable" encompasses "machine-readable."

[0174] The singular terms "a" and "an" should not be construed to mean "only one." Rather, unless otherwise stated, they should be construed to mean "at least one" or "one or more." The word "comprising" and its derivatives including "comprises" include each of the stated features but do not exclude the inclusion of one or more additional features.

[0175] The above-described implementations have been described by way of example only, and the described implementations are to be considered in all respects as illustrative and not restrictive. It will be understood that variations of the described implementations may be made without departing from the scope of the invention. It will also be apparent that there are many variations not described, but which are within the scope of the appended claims.

Claims

1. 1. A method (100) of fabricating a component for use in a bioprocessing system, comprising: manufacturing the part using an additive manufacturing process (110); a step (120) of post-treating the surface of the part, the surface being intended to be wetted during use; Including, The post-treated surface has a surface roughness parameter value measured according to ISO 25178-2:2022, namely: 50 μm ２ to 600 μm ２ Average valley area Sda between 50 μm ２ to 500 μm ２ Average mountain area Sha between 3000mm －２ From 10,000 mm －２ The peak density Spd between Kurtosis Sku between 6 and 20 wherein the one or more surface 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 (220).

2. The method (100) of claim 1, wherein the part is manufactured using powder bed fusion bonding.

3. The method (100) of claim 2, wherein the part is manufactured using selective laser sintering.

4. The method (100) of any one of claims 1 to 3, wherein post-treating the surface comprises laser ablation of the surface.

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

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

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

8. 8. The method (100) according to any one of claims 1 to 7, wherein the sharpness 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. 9. The method (100) of any one of claims 1 to 8, wherein the post-treated surface has an autocorrelation length Sal, measured according to ISO 25178-2:2022, of 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 on the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

10. The post-treated surface is 7000 μm ２ Less than 6000 μm, preferably ２ less than 5000 μm, more preferably ２ less than 4000 μm, more preferably ２ Less than, or most preferably, 3000 μm ２ 10. The method (100) of claim 1, wherein the surface has a maximum valley region area Sdax measured in accordance with ISO 25178-2:2022 of less than 1.0 μm, wherein the maximum valley region area Sdax is measured on 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 post-treated surface is 6000 μm ２ Less than 5000 μm, preferably ２ less than 4000 μm, more preferably ２ less than 3000 μm, more preferably ２ Less than, or most preferably, 2000 μm ２ 11. The method (100) of claim 1, wherein the surface has a maximum peak area Shax measured in accordance with ISO 25178-2:2022 of less than 1.0 μm, wherein the maximum peak area Shax is measured on 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 post-treated surface is 3000 mm －２ From 10,000 mm －２ Between, preferably 3500 mm －２ From 8000mm －２ Between 4000 mm and 5000 mm, more preferably 4000 mm －２ From 7000mm －２ Between 4500 mm and 5000 mm, more preferably 4500 mm －２ From 6000mm －２ or most preferably between 4000 mm －２ From 5000mm －２ 12. The method (100) of any one of claims 1 to 11, wherein the surface has a dent density Svd measured according to ISO 25178-2:2022 between 1.0 and 1.5 μm, wherein the dent density Svd is measured on the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

13. 13. The method (100) of any one of claims 1 to 12, wherein the post-treated surface has an average peak area shape factor Shff, measured according to ISO 25178-2:2022, of 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 peak area shape factor Shff is measured on the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

14. 14. The method (100) of any one of claims 1 to 13, wherein the post-treated surface has a mean valley area shape factor Sdff, measured according to ISO 25178-2:2022, 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, wherein the mean valley area shape factor Sdff is measured on the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

15. 15. The method (100) of any one of claims 1 to 14, wherein the post-treated surface has an advancing contact angle 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. 16. The method (100) of any one of claims 1 to 15, wherein the post-treated surface has a receding contact angle 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. 1. An additively manufactured part for use in a bioprocessing system, said additively manufactured part comprising a surface that is intended to be wetted during use, said surface having a surface roughness parameter value measured according to ISO 25178-2:2022, namely: 50 to 600 μm ２ Average valley area Sda between 50 to 500 μm ２ Average mountain area Sha between 3000mm －２ From 10,000 mm －２ The peak density Spd between Kurtosis Sku between 6 and 20 wherein the one or more surface roughness parameter values ​​are measured on the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm.

18. The average valley area Sda of the post-treated surface is 75 to 500 μm ２ Preferably, the range is between 100 and 400 μm. ２ More preferably, it is between 125 and 300 μm. ２ or most preferably between 150 and 250 μm ２ 20. The additively manufactured part of claim 17, wherein:

19. The average peak area Sha of the post-treated surface is 75 to 400 μm ２ Preferably, the range is between 100 and 300 μm. ２ More preferably, it is between 125 and 250 μm. ２ or most preferably between 150 and 200 μm ２ 19. The additively manufactured part of claim 17 or 18, wherein:

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

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

22. 1. A method (230) for determining the surface cleanliness of a component for use in a bioprocessing system, comprising: manufacturing (210) said part with a surface intended to be wetted during use; Measure the average valley area Sda of the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm, and determine whether the average valley area Sda of the surface is 50 μm or less. ２ to 600 μm ２ determining that the surface is clean if Measure an average peak area Sha of the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm, and determine whether the average peak area Sha of the surface is 50 μm or less. ２ to 500 μm ２ determining that the surface is clean if Measure the peak density (Spd) of the surface after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm, and determine whether the peak density (Spd) of the surface is 3000 mm －２ From 10,000 mm －２ determining that the surface is clean if measuring the surface sharpness Sku after filtering the surface to remove surface features having wavelengths below 2.5 μm and surface features having wavelengths above 11 μm, and determining that the surface is clean if the surface sharpness Sku is between 6 and 20. and one or more of The method (230) includes:

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