A cleaning method and a cleaning apparatus for cleaning a filter housing

Abstract

The application provides a cleaning method and a cleaning device for cleaning a filter housing, the cleaning method comprising: obtaining processing process information of a part, and formulating a cleaning strategy based on the processing process information; performing a preliminary cleaning operation on the part according to the cleaning strategy; obtaining wall surface state data of the part after the preliminary cleaning operation; comparing the wall surface state data with an expected standard of the processing process information, and generating a cleaning effect judgment based on a comparison result; if the cleaning effect is not up to standard, analyzing a reason for not meeting the standard and identifying a non-compliance area according to the comparison result, and formulating an optimized cleaning operation; and performing a cleaning operation again on the part or the non-compliance area based on the optimized cleaning operation. The application solves the technical problem that related technologies usually use high-pressure water for indiscriminate flushing, and lack of a targeted cleaning strategy according to part processing characteristics, which may easily lead to insufficient cleaning in some areas and excessive cleaning in other areas.

Description

Technical Field

[0001] This invention relates to the field of equipment cleaning technology, and more specifically, to a cleaning method and equipment for cleaning filter housings. Background Technology

[0002] In industrial fields such as filter manufacturing and precision hydraulics, after undergoing multiple machining processes such as turning, milling, drilling, and tapping, housing parts inevitably retain contaminants such as cutting fluid, grease, and metal shavings on their complex external surfaces and internal cavities. If these residues are not thoroughly removed, they will seriously affect the cleanliness index of the product, leading to a series of serious quality and reliability problems such as hydraulic system blockage, abnormal bearing wear, decreased filter insulation performance, and loss of assembly accuracy.

[0003] However, the related technologies have at least one of the following problems: most of the related technologies use high-pressure water to rinse the parts indiscriminately, and lack a strategy for targeted cleaning based on the processing characteristics of the parts; this can easily lead to insufficient cleaning in some areas and over-cleaning in others. Summary of the Invention

[0004] The technical problem solved by this invention is that related technologies mostly use high-pressure water to wash parts indiscriminately, lacking a strategy for targeted cleaning based on the processing characteristics of the parts; this easily leads to insufficient cleaning in some areas and over-cleaning in others.

[0005] To address the aforementioned problems, this invention provides a cleaning method for cleaning a filter housing, comprising: acquiring the processing technology information of the parts and formulating a cleaning strategy based on the processing technology information; performing a preliminary cleaning operation on the parts according to the cleaning strategy; after the preliminary cleaning operation, acquiring wall surface condition data of the parts; the wall surface condition data includes the outer wall data and inner wall data of the parts; comparing the wall surface condition data with the expected standards of the processing technology information, and generating a cleaning effect judgment based on the comparison results; if the cleaning effect is not up to standard, analyzing the reasons for the non-compliance based on the comparison results and identifying the non-compliant areas, and formulating an optimized cleaning operation; and performing a cleaning operation again on the parts or non-compliant areas based on the optimized cleaning operation.

[0006] Compared with existing technologies, the technical effects achieved by this technical solution are as follows: Compared with related technologies, this invention constructs a closed-loop process based on processing technology information to formulate cleaning strategies, perform preliminary cleaning, detect wall condition, compare with expected standards to determine the effect, and optimize and re-clean if the standard is not met. This achieves the targeting and verifiability of the cleaning process. It can formulate initial strategies based on the processing characteristics of parts, and promptly identify cleaning deficiencies through wall condition detection and effect judgment. Secondary optimization cleaning avoids impurity residue, effectively solving the problems of inability to judge the cleaning effect and incomplete cleaning.

[0007] In one embodiment of the present invention, obtaining the processing technology information of the part and formulating a cleaning strategy based on the processing technology information includes: identifying the geometric features of the part according to the processing technology information; determining targeted cleaning parameters according to the geometric features; the cleaning parameters include at least: the type of cleaning medium, the spray trajectory, and the pressure.

[0008] Compared with existing technologies, the technical effects achieved by this solution are as follows: For parts with deep holes, threads, and complex flow channels, using uniform cleaning parameters can lead to insufficient cleaning force in some areas and over-cleaning in others. Therefore, by associating with processing information, the key geometric features of the parts can be intelligently identified, thereby tailoring cleaning parameters to different features. For example, for deep holes, pressure can be increased and a fixed-point spray trajectory can be used, while for precision mating surfaces, low-pressure, high-flow-rate rinsing can be used. This precise strategy based on geometric features achieves optimal allocation of cleaning resources, ensures cleaning effectiveness, and protects the surface of the parts, avoiding damage.

[0009] In one embodiment of the present invention, the wall condition data is compared with the expected standard of the processing technology information, and a cleaning effect judgment is generated based on the comparison result. This includes: comparing the wall condition data of the part with the standard cleaning effect data after cleaning to obtain a first comparison result, and comparing the wall condition data with the corresponding theoretical processing data in the processing technology information to obtain a second comparison result; and generating a cleaning effect judgment based on the first comparison result and the second comparison result.

[0010] Compared with existing technologies, the technical effects achieved by this technical solution are as follows: Through a dual judgment mechanism of comparing the first comparison result with standard cleaning effect data and the second comparison result with theoretical processing data, a comprehensive evaluation of the cleaning effect is achieved; the first comparison ensures that the surface cleanliness meets the standard, and the second comparison ensures that the surface state after cleaning meets the theoretical expectation of the processing technology, avoiding one-sided judgment under a single standard and improving the accuracy of the cleaning effect judgment.

[0011] In one embodiment of the present invention, after cleaning, the wall surface condition data of the part is compared with standard cleaning effect data to obtain a first comparison result, including: scanning along any direction of the part to obtain a continuous data sequence of the wall surface condition of the part; determining whether the overall fluctuation of the continuous data sequence is within a preset stable range; if not, indicating that the first comparison result is not up to standard.

[0012] Compared with existing technologies, the technical effects achieved by this solution are as follows: By scanning and acquiring a continuous data sequence of the part's wall surface, and determining whether its overall fluctuation is within a preset stable range, a quantitative assessment of the surface condition is realized. Stable fluctuation indicates that the surface has no obvious impurities or unevenness, providing a reliable premise for subsequent comparison with standard data; if the fluctuation exceeds the range, it can be directly determined that it does not meet the standard, improving the objectivity and operability of the first comparison.

[0013] In one embodiment of the present invention, before determining whether the overall fluctuation of the continuous data sequence is within a preset stable range, the cleaning method includes: dividing the wall surface of the part into multiple process regions according to the processing technology information, wherein each process region corresponds to one or a type of processing procedure; setting a dedicated stable range for each process region; determining whether the overall fluctuation of the continuous data sequence is within the preset stable range, specifically including: dividing the continuous data sequence according to the process regions to obtain data sub-sequences corresponding to each region; determining whether each data sub-sequence is within its corresponding dedicated stable range; if not, indicating that the first comparison result is substandard, and marking the process region corresponding to the data sub-sequence as a candidate substandard region.

[0014] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: By dividing the process area and setting a dedicated stable interval, accurate regional judgment is achieved; Different process areas (such as cutting area and grinding area) have different residual impurity types and allowable fluctuation ranges due to different processing steps. The dedicated interval can specifically identify whether each area meets the standard, avoiding missed detection caused by a one-size-fits-all judgment, accurately marking candidate non-compliant areas, and providing accurate targets for subsequent optimization.

[0015] In one embodiment of the present invention, the theoretical processing data includes theoretical processing texture features formed on the surface of a qualified part by different processing steps; comparing the wall state data with the corresponding theoretical processing data in the processing technology information includes: parsing the final processing step type experienced by different regions of the part from the processing technology information; obtaining the theoretical processing texture features in the final processing step type; detecting the optical response characteristic data of each wall surface of the part after cleaning; calculating the feature matching degree between the optical response characteristic data and the theoretical processing texture features, and generating a second comparison result.

[0016] Compared with existing technologies, the technical effects achieved by this solution are as follows: it clarifies the theoretical processing data, including the theoretical processing texture features, and achieves accurate comparison between the surface state after cleaning and the essential processing features by analyzing the final processing steps, obtaining the theoretical texture, detecting the optical response characteristics, and calculating the matching degree. For example, if the matching degree between the optical response after cleaning and the theoretical texture of the cutting process is low, it indicates that there may be residual cutting impurities, ensuring that the second comparison can effectively identify the problem of processing feature masking caused by incomplete cleaning.

[0017] In one embodiment of the present invention, calculating the feature matching degree between optical response characteristic data and theoretically processed texture features, and generating a second comparison result, includes: calculating the similarity between optical response characteristic data and theoretically processed texture features to obtain a matching degree value; comparing the matching degree value with a preset matching threshold; if the matching degree value is greater than or equal to the preset matching threshold, generating a second comparison result compliance prompt; if the matching degree value is less than the preset matching threshold, generating a second comparison result non-compliance prompt, and identifying the reason for the non-compliance result based on the degree of deviation between the matching degree value and the preset matching threshold.

[0018] Compared with existing technologies, the technical effects achieved by this solution are as follows: By calculating the similarity score and comparing it with a preset threshold, a quantitative assessment of the matching degree is achieved. If the matching degree is lower than the threshold, the reason for non-compliance can be directly identified by the degree of deviation. For example, a slight deviation may indicate a small amount of residue, while a severe deviation may indicate a large amount of impurities covering the surface. This provides a clear basis for subsequent optimization of cleaning operations and improves the accuracy of problem localization.

[0019] In one embodiment of the present invention, generating a cleaning effect judgment based on a first comparison result and a second comparison result includes: if both the first comparison result and the second comparison result meet the standard, then generating a cleaning effect judgment that the overall part meets the standard; if at least one of the first comparison result and the second comparison result does not meet the standard, then generating a cleaning effect judgment that the part does not meet the standard.

[0020] Compared with existing technologies, the technical effects achieved by this solution are as follows: It clarifies the rule that overall compliance is determined only when both the first and second comparisons meet the standards, ensuring that the cleaning effect of parts simultaneously meets the dual requirements of surface cleanliness and conformity to the essential characteristics of the processing technology. This avoids misjudgments caused by meeting only one standard, such as a clean surface but damaged processing texture, thus strictly guaranteeing the quality of the cleaned parts.

[0021] In one embodiment of the present invention, the determination of the overall non-compliance of the cleaning effect of the generated parts includes: confirming the cause of non-compliance and the non-compliance area according to the non-compliance prompt; formulating optimized cleaning operations according to the cause of non-compliance; wherein, if the cause of non-compliance is cleaning residue, the corresponding cleaning liquid is selected according to the cleaning residue, and the solubility, pressure and spray angle of the cleaning liquid are adjusted.

[0022] Compared with existing technologies, the technical effects achieved by this solution are as follows: when standards are not met, the cause and area must first be identified, and precise optimization measures must be developed for cleaning residues. For example, a special degreasing solution is used for oil residues, and the spray pressure is increased for metal shavings, ensuring that secondary cleaning can efficiently solve specific problems and avoid repeated ineffective cleaning.

[0023] In one embodiment of the present invention, a cleaning device for cleaning a filter housing is provided, capable of applying any of the cleaning methods described above. The cleaning device includes: a control unit, and a strategy formulation module communicatively connected to the control unit, for acquiring and parsing processing information to formulate a cleaning strategy; a cleaning execution mechanism for executing cleaning operations according to the cleaning strategy; and a state detection sensor for acquiring wall surface state data of the parts after cleaning. The control unit is configured to: compare the wall surface state data with expected standards and generate a cleaning effect judgment; when the judgment fails to meet the standards, analyze the reasons for the failure, identify the non-compliant areas, formulate optimized cleaning operations, and control the cleaning execution mechanism to execute the optimized operations.

[0024] By adopting the technical solution of the present invention, the following technical effects can be achieved:

[0025] (1) This invention constructs a closed-loop process based on processing technology information to formulate cleaning strategies, perform preliminary cleaning, detect surface condition, compare with expected standards to determine the effect, and optimize and re-clean if the standard is not met. This realizes the pertinence and verifiability of the cleaning process. It can formulate initial strategies based on the processing characteristics of parts, and can promptly detect cleaning deficiencies through surface condition detection and effect determination. It can also avoid impurity residue through secondary optimization cleaning, effectively solving the problems of inability to judge the cleaning effect and incomplete cleaning.

[0026] (2) For some parts with deep holes, threads, and complex flow channels, using uniform cleaning parameters may result in insufficient cleaning force in some areas and over-cleaning in others. Therefore, by associating with the processing technology information, the key geometric features of the parts can be intelligently identified, thereby tailoring cleaning parameters for different features. For example, for deep holes, pressure can be increased and a fixed-point spray trajectory can be used, while for precision mating surfaces, low-pressure high-flow rinsing can be used. This precise strategy based on geometric features achieves optimal allocation of cleaning resources, ensures cleaning effect, and protects the surface of the parts, avoiding damage.

[0027] (3) The rule that the overall standard must be met only if both the first and second comparisons are satisfactory is clearly defined, ensuring that the cleaning effect of the parts simultaneously meets the dual requirements of surface cleanliness and conformity to the essential characteristics of the processing technology. This avoids misjudgment caused by a single standard being met, such as a clean surface but damage to the processing texture, and strictly guarantees the quality of the cleaned parts. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A flowchart illustrating a cleaning method for cleaning a filter housing, provided in an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of a cleaning device for cleaning a filter housing according to an embodiment of the present invention;

[0031] Figure 3 for Figure 2 A diagram from a first-person perspective;

[0032] Figure 4 for Figure 3 A cross-sectional view along the AA direction;

[0033] Figure 5 for Figure 3 A cross-sectional view along the BB direction;

[0034] Figure 6 This is a schematic diagram of the structure of the part shown.

[0035] Explanation of reference numerals in the attached figures:

[0036] 100. Cleaning equipment; 10. Housing; 101. Cleaning space; 102. Cleaning inlet; 20. Control panel; 30. Cleaning actuator; 311. Upper spray plate; 312. Front spray plate; 313. Left spray plate; 314. Right spray plate; 321. Lower spray plate; 33. Spray head; 331. First spray baseline; 332. Second spray baseline; 40. Conveying mechanism; 41. Roller; 50. Water tank; 60. Water mist treatment device; 70. Fan; 80. Water supply tank; 90. Parts; 91. Hole. Detailed Implementation

[0037] Embodiments of the present invention will now be described in detail. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0038] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a link, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0040] See Figure 1 , Figure 1 A flowchart illustrating a cleaning method for cleaning a filter housing, provided as an embodiment of the present invention; specifically, a cleaning method for cleaning a filter housing includes:

[0041] S1: Obtain the machining process information of the parts and formulate a cleaning strategy based on the machining process information;

[0042] S2: Perform preliminary cleaning operations on the parts according to the cleaning strategy;

[0043] S3: After the initial cleaning operation, acquire the wall condition data of the part; the wall condition data includes the outer wall data and the inner wall data of the part;

[0044] S4: Compare the wall condition data with the expected standards of the processing technology information, and generate a cleaning effect judgment based on the comparison results;

[0045] S41: If the cleaning effect is not up to standard, analyze the reasons for the failure based on the comparison results, identify the areas that are not up to standard, and formulate optimized cleaning operations;

[0046] S5: Based on the optimized cleaning operation, perform the cleaning operation again on the parts or substandard areas.

[0047] The machining process information records all the processes, such as rough turning, finish turning, and finish milling, from the blank to the finished product, as well as their corresponding machining areas.

[0048] The wall condition data includes at least the surface cleanliness of the outer wall of the part and the surface cleanliness of the inner wall of the part.

[0049] Based on the specific work situation, by acquiring the processing technology information of the parts to be cleaned, a preliminary cleaning strategy is initially formulated. Upon receiving the preliminary cleaning strategy, the parts are initially cleaned. After the preliminary cleaning is completed, an integrated array of detection sensors immediately scans each surface of the part to obtain surface condition data. This surface condition data is compared with the expected standards determined based on the processing technology information to generate a cleaning effect judgment. If the judgment is unsatisfactory, the reasons for the unsatisfactory results are analyzed, and the specific unsatisfactory areas of the part are identified based on the surface condition data and processing technology information. Subsequently, based on the analysis results, targeted optimized cleaning operations are formulated, and the unsatisfactory areas are cleaned again.

[0050] Furthermore, obtain the machining process information of the parts, and formulate a cleaning strategy based on the machining process information, including:

[0051] Identify the geometric features of the part based on the processing technology information;

[0052] Based on geometric characteristics, determine the appropriate cleaning parameters; the cleaning parameters should include at least the type of cleaning medium, the spray trajectory, and the pressure.

[0053] Specifically, by analyzing the processing information, the geometric features formed by these processes that have a significant impact on cleaning are identified; for example, drilling deep holes, blind holes, threaded holes, and complex cavities. For each identified key geometric feature, a set of cleaning parameters is specifically developed; for example, for deep holes or blind holes, high jet pressure and a vertical jet trajectory pointing towards the bottom of the hole are set to ensure that debris at the bottom of the hole is flushed away; for large-area flat walls, a high-flow fan-shaped nozzle is set to perform a reciprocating jet trajectory to achieve efficient coverage; for threads, medium and low pressure jets can be used to damage the thread profile on the wall.

[0054] Furthermore, based on the machining process information, the geometric features of the part are identified, including:

[0055] Based on geometric features and corresponding processing steps, difficult cleaning areas of parts are identified, and corresponding powerful cleaning strategies are developed.

[0056] The intensive cleaning strategy includes assigning higher cleaning priority, longer cleaning time and / or stronger cleaning parameters to areas that are difficult to clean.

[0057] For example, depending on the part's machining process, such as a part having a combination of deep holes and tapping, it is very easy for high-viscosity tapping oil to remain. Therefore, in the first cleaning, this area should be treated more thoroughly to improve the pass rate of the first cleaning.

[0058] Furthermore, the wall condition data is compared with the expected standards of the processing technology information, and a cleaning effect judgment is generated based on the comparison results, including:

[0059] After cleaning, the wall surface condition data of the parts is compared with the standard cleaning effect data to obtain the first comparison result, and the wall surface condition data is compared with the corresponding theoretical processing data in the processing technology information to obtain the second comparison result.

[0060] A cleaning effect determination is generated based on the first comparison result and the second comparison result.

[0061] The standard cleaning effect data represents the acceptable data for this part, which can be understood as a passing grade for the cleaning standard of this part.

[0062] Specifically, the cleaning effect is determined by two parallel and complementary comparison result judgment paths. The purpose of the first comparison result is to evaluate the consistency between the cleaning effect and the standard cleaning effect. That is, after cleaning is completed, the wall surface status data of the part after cleaning is obtained. The wall surface status data of the part obtained this time is compared with the standard cleaning effect data constructed based on the qualified part data set for the part, and then the first comparison result is output. The first comparison result includes qualified or unqualified.

[0063] The purpose of the second comparison result is to evaluate whether the current state of the part meets the theoretical quality specified by its processing technology from a physical perspective. That is, after cleaning, the same wall state data is compared with the theoretical processing data that the corresponding area should have after being parsed from the processing technology information, or it can be understood as comparing the surface characteristics under the ideal processing state, and then outputting the second comparison result, which includes qualified or unqualified.

[0064] Finally, by combining the results of the first and second comparisons, a final cleaning effect judgment is generated according to preset regulations.

[0065] Furthermore, after cleaning, the surface condition data of the parts is compared with the standard cleaning effect data to obtain the first comparison results, including:

[0066] Scan along any direction of the part to obtain a continuous data sequence of the part's wall surface condition;

[0067] Determine whether the overall fluctuation of a continuous data sequence is within a preset stable range;

[0068] If so, compare the continuous data sequence with the standard cleaning effect data;

[0069] If not, a message will be displayed indicating that the first comparison result does not meet the standard.

[0070] Specifically, the analysis of the first comparison result involves uniformly scanning along the length of the part or along any preset path to obtain a continuous data sequence of wall feature states. The overall volatility of this data sequence, such as by calculating variance or standard deviation, is used to determine whether the continuous data sequence obtained after cleaning is within a preset stable range. If so, it indicates that the data of the part's wall surface is stable after cleaning and can be used as a subsequent reference. This high-quality data sequence is then further compared with standard cleaning effect data. If not, it indicates that the data of the part's wall surface is unstable after cleaning, meaning that at least one wall surface state of the part is seriously inconsistent or the detection process is interfered with. In this case, a "first comparison result not meeting the standard" message is directly output.

[0071] Furthermore, the continuous data sequence was compared with standard cleaning effect data, specifically including:

[0072] Each data subsequence obtained by dividing the process area is compared with the corresponding standard cleaning effect to obtain the comparison results of each sub-area;

[0073] Based on the comparison results of each sub-region, a message indicating whether the first comparison result meets the standard or not is generated.

[0074] Furthermore, based on the comparison results of each sub-region, a comprehensive notification is generated indicating whether the first comparison result meets the standard or not, including:

[0075] If the comparison results of all sub-regions are satisfactory, then a satisfactory message for the first comparison result will be generated.

[0076] If the comparison result of any sub-region is unsatisfactory, a message indicating that the first comparison result is unsatisfactory will be generated.

[0077] Specifically, when all data subsequences are determined to be stable, the continuous data sequence is compared with standard cleaning effect data: First, based on the processing procedure type of each process area, the corresponding dedicated standard cleaning effect data is selected; then, each data subsequence is independently compared with its dedicated standard data, the feature matching degree is calculated, and it is determined whether the regional qualification threshold is reached, thereby generating an independent sub-region comparison result for each region, i.e., each independent sub-region is "compliant" or "non-compliant". Next, a comprehensive judgment is made: if all sub-region results are "compliant", a "first comparison result compliance prompt" is generated; if any sub-region result is "non-compliant", a "first comparison result non-compliant prompt" is generated.

[0078] Furthermore, before determining whether the overall fluctuation of the continuous data sequence is within a preset stable range, the cleaning method includes:

[0079] Based on the processing technology information, the wall surface of the part is divided into multiple processing areas, each of which corresponds to one or a type of processing procedure.

[0080] Set a dedicated stability range for each process region;

[0081] Determining whether the overall fluctuation of a continuous data series is within a preset stability range specifically includes:

[0082] The continuous data sequence is divided according to the process region to obtain the data subsequence corresponding to each region;

[0083] Determine whether each data subsequence is within its corresponding dedicated stable interval;

[0084] If not, the system will indicate that the first comparison result is not up to standard and mark the process area corresponding to the data subsequence as a candidate non-compliant area.

[0085] Specifically, before scanning, the process area is first divided. Based on the processing technology information, the wall surface of the part is divided into multiple process areas on the digital model. For example, there are "end face turning area", "bearing mounting hole area", "cooling water channel area", and "threaded hole area", each area corresponding to one or a similar processing procedure. Furthermore, since the surfaces formed by different processing methods have different inherent uniformity, for example, the data fluctuation in the fine grinding area should be smaller than that in the casting area, a dedicated stable interval is set for each process area. After obtaining the continuous data sequence of the entire part, it is divided into several data subsequences according to the divided areas. Then, it is determined whether each subsequence is within its corresponding dedicated stable space. If all data subsequences are stable, the global data sequence is compared with the standard cleaning effect data to generate a "first comparison result meets the standard prompt". If any data subsequence is unstable, a "first comparison result does not meet the standard prompt" is generated, and the physical process area corresponding to the unstable data subsequence is marked as a "candidate non-compliant area" in the digital model to achieve the initial location of the problem.

[0086] It should be noted that setting a dedicated stability range for each process region is necessary because the surfaces formed by different processing steps (such as fine grinding and casting) have drastically different inherent microstructures and statistical characteristics. A finely ground surface, in a clean state, should theoretically exhibit low variance and high stability in its optical response signal. However, due to its inherent roughness, a cast surface, in a clean state, naturally exhibits higher volatility in its signal. Using a uniform stability range threshold would lead to an excessively high false alarm rate for rough surfaces or an increased false negative rate for smooth surfaces. Therefore, setting a dedicated stability range essentially establishes an adaptive filter for each region that matches its specific process.

[0087] Only subsequences deemed unstable within a dedicated stable region are marked as "candidate non-compliant regions" in terms of their corresponding physical regions. The reliability of this judgment directly determines the accuracy and efficiency of subsequent optimization and cleaning operations.

[0088] Furthermore, the theoretical processing data includes the theoretical processing texture features formed on the surface of qualified parts by different processing steps;

[0089] The wall condition data is compared with the corresponding theoretical processing data in the processing technology information, including:

[0090] The final processing steps experienced by different areas of the part are analyzed from the processing technology information;

[0091] Obtain the theoretical processing texture features in the final processing step type;

[0092] The optical response characteristics of the parts were measured on each surface after cleaning.

[0093] The feature matching degree between the optical response characteristic data and the theoretical processed texture features is calculated, and a second comparison result is generated.

[0094] The theoretical processing data can be understood as a standard signal feature library, which stores the physical signal features formed on the surface of qualified parts by different processing steps that can be detected by sensors.

[0095] The optical response characteristics data include the intensity of light along the specular reflection direction after light enters the hole of the part at a specific angle; for example, the light intensity reflected by a smooth wall after fine grinding is high; the intensity of specular reflection is weakened due to scattering of oil or rough surfaces; and the intensity of diffuse reflection, for example, the sum of the light intensity scattered in all directions after the light is incident; rough, uncontaminated surfaces have stronger diffuse reflection; while smooth surfaces or surfaces with oil film have weaker diffuse reflection.

[0096] Specifically, the process begins by analyzing the final processing steps, which involves extracting the final processing steps that different areas of the part undergo from the processing technology information. For example, it determines whether the final processing type is fine grinding or fine turning. Based on the analyzed process type, the corresponding theoretical processing texture features are obtained from the standard signal feature library. These features can include the theoretical roughness, specific optical reflectivity, and other data that the qualified wall surface of the part should have under this process. The optical response characteristics of the corresponding area of ​​the part after cleaning are detected using an optical measurement device. The feature matching degree between the obtained optical response characteristics and the obtained theoretical processing texture features is calculated, and a second comparison result is generated accordingly.

[0097] Furthermore, the optical response characteristics of the parts on each wall surface after cleaning are measured, including:

[0098] Simultaneously acquire reflectivity data collected by an optical sensor and acoustic impedance data collected by an ultrasonic sensor;

[0099] The calculation of feature matching degree includes: weighted fusion calculation of reflectivity data and acoustic impedance data to obtain comprehensive matching degree.

[0100] Optical sensors are sensitive to surface oil stains, while ultrasonic waves are more effective at detecting internal pore residues and thick layers of contaminants. Therefore, combining the two can greatly reduce the false positive rate and distinguish the types of contaminants, such as oil film and water stains.

[0101] Furthermore, the feature matching degree between the optical response characteristic data and the theoretical processed texture features is calculated, and a second comparison result is generated, including:

[0102] The similarity between optical response characteristic data and theoretically processed texture features is calculated to obtain the matching degree value;

[0103] Compare the matching score with the preset matching threshold;

[0104] If the matching degree value is greater than or equal to the preset matching threshold, a second comparison result compliance prompt will be generated;

[0105] If the matching degree value is less than the preset matching threshold, a second comparison result failure prompt will be generated, and the reason for the failure will be identified according to the degree of deviation between the matching degree value and the preset matching threshold.

[0106] Specifically, a quantified matching degree value is obtained by calculating the similarity between optical response characteristic data and theoretical processed texture; this matching degree value is compared with a preset matching threshold; and then it is determined whether the second comparison result meets the standard; that is, if the matching degree value is greater than or equal to the preset threshold, a "second comparison result meets the standard prompt" is generated.

[0107] If the matching degree value is less than the preset threshold, a "Second comparison result does not meet the standard prompt" will be generated. In addition, the system will identify the primary cause of the non-compliant result based on the degree of deviation between the matching degree value and the threshold. For example, a slight deviation may be identified as "cleaning residue", while a serious deviation may be identified as "processing defect".

[0108] Furthermore, a cleaning effect judgment is generated based on the first comparison result and the second comparison result, including:

[0109] If both the first and second comparison results meet the standards, a cleaning effect judgment is generated to indicate that the overall parts meet the standards.

[0110] If at least one of the first comparison result and the second comparison result is substandard, a cleaning effect judgment for the part that does not meet the standard will be generated.

[0111] Specifically, after receiving the final signals from both paths, a final decision will be made; only if the first comparison result meets the standard and the second comparison result meets the standard will a cleaning effect judgment be generated indicating that the overall part meets the standard; if any comparison result or all comparison results are not met, an effect judgment indicating that the overall part does not meet the standard will be generated.

[0112] Furthermore, the determination of the overall cleaning effect of the generated parts is as follows:

[0113] Confirm the reason for non-compliance and the area that did not meet the standard based on the non-compliance notice;

[0114] Optimize cleaning operations based on the reasons for non-compliance; if the reason for non-compliance is cleaning residue, select the appropriate cleaning solution based on the cleaning residue, and adjust the cleaning solution concentration, pressure and spray angle.

[0115] Specifically, when a part is determined to be non-compliant, the area corresponding to the non-compliance warning signal is identified and ultimately confirmed as the non-compliant area requiring processing. This includes the candidate non-compliant areas described above and areas where optical abnormal responses are detected. Then, the cause of the non-compliance is determined. Based on the confirmed cause, an optimized cleaning operation is matched and formulated from a pre-configured cleaning strategy library. For example, if the cause is cleaning residue, the optimized operation includes: selecting a corresponding specialized cleaning fluid based on the chemical properties of the residue (e.g., oil-based or water-based), adjusting the concentration of the cleaning fluid, increasing the spray pressure, and optimizing the nozzle spray angle to ensure direct contact with the problem area. If the cause is a machining defect, a machining quality alarm signal is output.

[0116] Furthermore, the cleaning methods also include:

[0117] Establish and maintain a knowledge base to record key data in each cleaning task. Key data includes: part model, cleaning strategy, wall condition data, cleaning effect judgment and optimization operation.

[0118] Based on a knowledge base, parameters in the data model and / or cleaning strategy library are optimized through data analysis to improve cleaning effectiveness.

[0119] Combination Figures 2 to 6The present invention also provides a cleaning device 100 for cleaning a filter housing 10, capable of applying the cleaning method described in any example. The cleaning device 100 includes: a control unit, and a strategy formulation module communicatively connected to the control unit for acquiring and parsing processing information to formulate a cleaning strategy; a cleaning execution mechanism 30 for performing cleaning operations according to the cleaning strategy; and a status detection sensor for acquiring wall surface status data of the part 90 after cleaning. The control unit is configured to: compare the wall surface status data with expected standards and generate a cleaning effect judgment; when the judgment is unsatisfactory, analyze the reasons for the unsatisfactory performance, identify the unsatisfactory areas, formulate optimized cleaning operations, and control the cleaning execution mechanism 30 to perform the optimized operations.

[0120] Furthermore, the cleaning equipment 100 includes: a housing 10, a control panel 20 is provided on the outside of the housing 10, and the control unit and strategy formulation module are both located in the control panel 20; a cleaning space 101 is provided inside the housing 10, and the cleaning execution mechanism 30 and the status detection sensor are both located in the cleaning space 101; and a conveying mechanism 40 is located in the cleaning space 101 and is used to convey the parts 90 to be cleaned.

[0121] Furthermore, the housing 10 has a cleaning inlet 102 that communicates with the cleaning space 101, and the cleaning inlet 102 is located on the same side as the control panel 20; one end of the conveying mechanism 40 is located at the cleaning inlet 102; the conveying mechanism 40 includes a drive filter and a plurality of rollers 41, and the drive filter is connected to the rollers 41 in a driving manner; the plurality of rollers 41 are arranged along the length direction of the housing 10 and are used to transfer the part 90 from the cleaning inlet 102 to the cleaning space 101.

[0122] Alternatively, the conveyor 40 can drive other conveyor combinations such as filters and conveyor belts.

[0123] Furthermore, the cleaning actuator 30 includes a first cleaning section disposed within the cleaning space 101. The first cleaning section includes an upper spray plate 311, a front spray plate 312, a rear spray plate, a left spray plate 313, and a right spray plate 314. The upper spray plate 311, the front spray plate 312, the rear spray plate, the left spray plate 313, and the right spray plate 314 are disposed on the corresponding inner walls of the cleaning space 101. The upper spray plate 311, the front spray plate 312, the rear spray plate, the left spray plate 313, and the right spray plate 314 are each provided with a plurality of water spray heads 33.

[0124] Furthermore, a water tank 50 is provided below the transmission mechanism, and the cleaning execution mechanism 30 also includes a second cleaning section, which is located on the side of the water tank 50 near the transmission mechanism 40; the second cleaning section includes a lower spray plate 321 and a water spray head 33 provided on the lower spray plate 321.

[0125] During the targeted cleaning phase, each hole 91 of the part 90 is equipped with at least one water spray head 33 to perform targeted, high-pressure jet cleaning, effectively removing stubborn contaminants from key areas. After the targeted cleaning is completed, the water spray nozzles covering the entire part 90 are activated. During this phase, the part 90 reciprocates on the conveyor mechanism 40, ensuring that all holes 91 and other parts pass through the range of the water spray head 33 in sequence, ensuring no cleaning blind spots and achieving comprehensive cleaning coverage.

[0126] Furthermore, the front spray plate 312 and the rear spray plate have water nozzles 33 corresponding to parts 90 and holes 91 on parts 90, and are provided with a first water spray baseline 331; the lower spray plate 321 and the upper spray plate 311 have water nozzles 33 corresponding to parts 90 and holes 91 on parts 90, and are provided with a second water spray baseline 332; the left spray plate 313 and the right spray plate 314 have water nozzles 33 corresponding to parts 90 and holes 91 on parts 90, and are provided with a third water spray baseline.

[0127] Furthermore, the spray heads 33 on each spray plate consist of a straight spray head 33 and a omnidirectional spray head 33. The omnidirectional spray head 33 can effectively clean the non-spray plate opposite surfaces.

[0128] Furthermore, the spray plate is installed in multiple positions. The spray plate is customized according to the different holes 91 of the workpiece. Some spray heads 33 are installed corresponding to the holes 91 of the workpiece, while other spray heads 33 are installed with full coverage. With the universal elbow, the workpiece can be cleaned from all directions. The nozzles of the spray heads 33 are tapered wide-angle nozzles, which can cover the surface of the workpiece and the inner hole wall without gaps, thus improving the cleaning and drying effect.

[0129] Furthermore, the housing 10 is also equipped with a water mist treatment device 60, which is connected to the cleaning space 101. During the cleaning process, especially during the high-pressure spraying and drying stages, a large amount of suspended water mist and aerosols are generated inside the chamber. The water mist treatment device 60 actively extracts this humid air through a collection hood or vent at its inlet end. The extracted air passes through the device's condensation unit and high-efficiency air-water separator, where suspended water droplets are condensed and separated, collected, and can be discharged or recycled to a water tank for reuse. The dried and purified air can be directly discharged into the atmosphere through the device outlet or sent back into the cleaning chamber to maintain air pressure balance.

[0130] Furthermore, the housing 10 is also equipped with multiple fans 70, which are connected to the cleaning space 101. When high-pressure cleaning or air blowing is performed inside the chamber, a large amount of water mist will be generated. At this time, the system starts the fans 70 to force the air rich in water mist to be discharged and directed to the water mist treatment device 60 for condensation and separation, effectively preventing water mist from accumulating inside the housing 10 or affecting the operation of the sensor.

[0131] Furthermore, a water supply tank 80 is also provided on the housing 10, and the water supply tank 80 is connected to the cleaning actuator 30.

[0132] Furthermore, the condition detection sensors include an optical imaging sensor and an ultrasonic sensor. The optical imaging sensor is installed on the inner wall of the cleaning space 101 to acquire two-dimensional images or three-dimensional point cloud data of the surface of the part 90 after cleaning. By analyzing the images, contaminants such as oil film coverage, pigment residue, and particulate matter adhesion are identified, and a first comparison result judgment is performed. The ultrasonic sensor can be installed on any spray plate and emits ultrasonic waves to key areas of the workpiece (such as the bottom of blind holes or cross-hole structures) and receives the echoes. By analyzing the echoes, it is possible to determine whether there are hidden liquid residues, grease accumulations, or structural defects within the microstructure of the workpiece. A second comparison result judgment is then performed to achieve a deeper level of cleanliness verification.

[0133] In a specific example, part 90 is the filter housing 10. By reading the process document of the filter housing 10, its machining process information is obtained, including turning, drilling, tapping, and other processes and their corresponding machining areas. Then, based on the machining process information, the geometric features of part 90 are identified, such as deep holes and other complex cavities in the drilling process. Targeted cleaning parameters are then formulated based on the geometric features. The cleaning parameters include selecting a special cleaning fluid based on the type of residue that may be present, using vertical fixed-point spraying for geometric features such as deep holes, and using fan-shaped scanning for the planar area of ​​the filter housing 10. For deep hole areas in the machining process, the spray pressure is adjusted to use high-pressure spraying; and for areas requiring high precision, low-pressure spraying is used.

[0134] After the settings are completed, the filter housing 10 is sent into the cleaning space of the cleaning equipment 100 through the conveying mechanism 40, and the cleaning execution mechanism 30 is started to control the water spray heads 33 on the spray plates in six directions (up, down, left, right, front, and back) to spray and clean the filter housing 10 in all directions according to the preset cleaning strategy.

[0135] After cleaning, the optical imaging sensor and ultrasonic sensor are activated; the surface cleanliness of the filter housing 10 is obtained by scanning the wall surface of the housing 10; the ultrasonic sensor sends sound waves into deep holes and complex cavities, and judges whether there are other residues such as grease by the echo characteristics; the filter housing 10 is judged to be cleaned as qualified by comparing the data collected by the two sensors with qualified data in the standard cleaning effect database and theoretical processing texture characteristics; only when both data are qualified is the part 90 judged to be cleaned as qualified.

[0136] When the cleaned filter housing 10 fails to meet the standards, the location of the residue is determined based on the first and second comparison results. For example, if the reflectivity of the threaded hole of the filter housing 10 is abnormal and the ultrasonic data indicates that the acoustic impedance inside the hole deviates from the standard, it is determined that there is oil residue in the threaded hole area and it is marked as a non-compliant area. Based on this, the cleaning plan is adjusted according to the cause of non-compliance. For example, switching to a strong degreasing cleaning fluid, appropriately increasing the concentration and temperature of the cleaning fluid, performing a higher pressure fixed-point spray on the threaded hole, and adjusting the angle of the universal nozzle to ensure vertical coverage of the threaded tooth surface.

[0137] After the second cleaning, the non-compliant areas are inspected again; only after passing the inspection are the filter housing 10 deemed clean and sent out by the conveyor mechanism 40. All data from the entire cleaning process, including part 90 model, cleaning strategy, sensor data, judgment results, and optimization measures, are automatically recorded and stored in a knowledge base for continuous optimization of future cleaning models and strategies, enabling the cleaning device to have self-learning capabilities.

[0138] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A cleaning method for cleaning a filter housing, characterized in that, include: The machining process information of the part is obtained, and a cleaning strategy is formulated based on the machining process information; wherein, the machining process information records all processes of the part from blank to finished product and their corresponding processing areas; According to the cleaning strategy, a preliminary cleaning operation is performed on the parts; After the initial cleaning operation, the surface condition data of the part is acquired by a condition detection sensor, which includes an optical imaging sensor and an ultrasonic sensor. The optical imaging sensor is used to acquire two-dimensional images or three-dimensional point cloud data of the part surface after cleaning. By analyzing the images, oil film coverage, pigment residue, and particulate matter adhesion can be identified. The ultrasonic sensor emits ultrasonic waves to the bottom of blind holes and intersecting hole structures of the workpiece and receives the echoes. By analyzing the echoes, it is possible to determine whether there are hidden liquid residues, grease accumulations, or structural defects inside the microstructure of the workpiece. The surface condition data includes the outer wall data and inner wall data of the part. The wall condition data is compared with the expected standard of the processing technology information, and a cleaning effect judgment is generated based on the comparison results. The step of comparing the wall surface condition data with the expected standard of the processing technology information and generating a cleaning effect judgment based on the comparison result includes: After cleaning, the wall surface condition data of the part is compared with the standard cleaning effect data to obtain a first comparison result, and the wall surface condition data is compared with the corresponding theoretical processing data in the processing technology information to obtain a second comparison result. The cleaning effect determination is generated based on the first comparison result and the second comparison result; The theoretical processing data includes the theoretical processing texture features formed on the surface of qualified parts by different processing procedures; The step of comparing the wall surface condition data with the corresponding theoretical processing data in the processing technology information includes: The final processing steps experienced by different areas of the part are extracted from the processing technology information; Obtain the theoretical processing texture features in the final processing step type; The optical response characteristics of the parts were measured on each surface after cleaning. Calculate the feature matching degree between the optical response characteristic data and the theoretical processed texture features, and generate the second comparison result; If the cleaning effect is not up to standard, analyze the reasons for the failure based on the comparison results, identify the areas that are not up to standard, and formulate optimized cleaning operations. Based on the optimized cleaning operation, the cleaning operation is performed again on the part or the substandard area; The step of comparing the wall surface condition data of the part with standard cleaning effect data after cleaning to obtain a first comparison result includes: Scan along any direction of the part to obtain a continuous data sequence of the wall surface state of the part; Determine whether the overall fluctuation of the continuous data sequence is within a preset stable range; If not, a message will be displayed indicating that the first comparison result does not meet the standard; Before determining whether the overall fluctuation of the continuous data sequence is within a preset stable range, the cleaning method includes: Based on the processing technology information, the wall surface of the part is divided into multiple processing areas, wherein each processing area corresponds to one or a type of processing procedure. Each of the aforementioned process regions is assigned a dedicated stability range; The determination of whether the overall fluctuation of the continuous data sequence is within a preset stable range specifically includes: The continuous data sequence is divided according to the process region to obtain data subsequences corresponding to each region; Determine whether each of the data subsequences is within its corresponding dedicated stable interval; If not, the system will indicate that the first comparison result is not up to standard and mark the process area corresponding to the data subsequence as a candidate unsatisfactory area. The calculation of the feature matching degree between the optical response characteristic data and the theoretical processed texture features, and the generation of the second comparison result, includes: Calculate the similarity between the optical response characteristic data and the theoretical processed texture features to obtain a matching degree value; The matching score value is compared with a preset matching threshold. If the matching degree value is greater than or equal to the preset matching threshold, a second comparison result compliance prompt is generated; If the matching degree value is less than the preset matching threshold, a second comparison result failure prompt is generated, and the reason for failure is identified based on the degree of deviation between the matching degree value and the preset matching threshold.

2. The cleaning method according to claim 1, characterized in that, The process of acquiring the machining process information of the parts and formulating a cleaning strategy based on the machining process information includes: Based on the processing technology information, identify the geometric features of the part; Based on the geometric features, specific cleaning parameters are determined; the cleaning parameters include at least the type of cleaning medium, the spray trajectory, and the pressure.

3. The cleaning method according to claim 1, characterized in that, The step of generating the cleaning effect determination based on the first comparison result and the second comparison result includes: If both the first comparison result and the second comparison result meet the standard, then the cleaning effect judgment of the part as a whole is generated; If at least one of the first comparison result and the second comparison result is substandard, a cleaning effect judgment is generated indicating that the part is substandard.

4. The cleaning method according to claim 3, characterized in that, The determination of the cleaning effect when the generated part fails to meet the standard includes: Based on the non-compliance prompt, confirm the reason for non-compliance and the non-compliant area; The optimized cleaning operation is formulated based on the reasons for non-compliance; wherein, if the reasons for non-compliance are cleaning residue, the corresponding cleaning solution is selected according to the cleaning residue, and the solubility, pressure and spray angle of the cleaning solution are adjusted.

5. A cleaning device for cleaning a filter housing, characterized in that, The cleaning equipment, capable of applying the cleaning method as described in any one of claims 1 to 4, comprises: The control unit, and the following components that are communicatively connected to the control unit: The strategy formulation module is used to acquire and parse the processing technology information to formulate the cleaning strategy; A cleaning actuator, used to perform the cleaning operation according to the cleaning strategy; A condition detection sensor is used to acquire the wall surface condition data of the part after cleaning; The control unit is configured to perform: The wall condition data is compared with the expected standard, and the cleaning effect judgment is generated. When a non-compliance is determined, the reasons for the non-compliance are analyzed, the non-compliance areas are identified, and the optimized cleaning operation is formulated. The cleaning execution mechanism is then controlled to perform the optimized cleaning operation.

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