A method for printhead wear detection based on machine learning

Abstract

The present application belongs to the technical field of 3D printing, and specifically relates to a printing head wear detection method based on machine learning, which solves the problems of relying on manual judgment, low detection accuracy, insufficient prediction ability and the like in the prior art. By collecting current, temperature and working time data, and extracting key features to construct a regression model, real-time evaluation and remaining life prediction of the printing head wear state are realized. Compared with the traditional method, this method fuses multi-source data, improves detection accuracy, and can identify wear trends earlier; a weighted calculation method is used to establish a quantitative relationship, enhancing the scientific nature of life prediction; combined with the control system, automatic warning is realized, reducing unnecessary replacement and sudden failure risk, thereby improving equipment operation efficiency, reducing maintenance cost, and improving the intelligent level and stability of the 3D printing system.

Description

Technical Field

[0001] This invention belongs to the field of 3D printing technology, specifically relating to a machine learning-based method for detecting printhead wear. Background Technology

[0002] In the 3D printing process, the print head, as the core execution component, undertakes the critical tasks of material heating and extrusion. With prolonged use, the internal heating elements, nozzles, and drive mechanisms of the print head will experience varying degrees of wear or aging due to factors such as high temperatures, mechanical friction, and material corrosion, thereby affecting print quality and equipment stability. Therefore, accurately detecting the wear condition of the print head and predicting its remaining lifespan has become an important issue for ensuring printing accuracy and improving production efficiency.

[0003] Currently, existing technologies primarily rely on manual periodic inspections or fixed-cycle replacement strategies to manage printhead lifespan. This approach not only depends on operator experience and judgment, leading to subjectivity and delayed response times, but also easily results in resource waste or equipment failure risks. For example, replacing the printhead before it is severely worn incurs unnecessary costs; while failing to replace it in time after performance degradation can lead to printing failures, product defects, or even equipment damage. Furthermore, some improvement solutions attempt to monitor printhead operating status using a single sensor (such as a temperature sensor), but due to a lack of comprehensive analysis of multi-dimensional data, they cannot effectively identify early wear characteristics, limiting detection accuracy and predictive capabilities. Summary of the Invention

[0004] The purpose of this invention is to provide a printhead wear detection method based on machine learning, which can more accurately reflect the real-time health status of the printhead, improve maintenance efficiency, reduce equipment downtime risk, and has a higher level of practicality and intelligence, thereby solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a printhead wear detection method based on machine learning, comprising the following steps:

[0006] The system collects current data, temperature data, and cumulative working time data of the printhead; performs noise reduction and normalization on the current data, temperature data, and cumulative working time data to generate preprocessed data; extracts current fluctuation features, temperature change trends, and cumulative working time features from the preprocessed data to form a feature dataset; trains a feature-weighted regression model using the feature dataset, and calculates the wear index of the printhead by weighted summation; predicts the remaining lifespan of the printhead based on the wear index and preset initial life parameters.

[0007] Preferably, the current fluctuation characteristics include at least one of the average current, standard deviation, peak current, and fluctuation frequency; the temperature change trend includes at least one of the average temperature, maximum temperature, minimum temperature, and temperature change rate.

[0008] Preferably, the normalization of the preprocessed data is achieved through linear scaling, which maps the current data and temperature data to the [0,1] interval to eliminate the influence of different dimensions.

[0009] Preferably, the feature dataset also includes the product feature of current fluctuation and temperature change, as well as the correlation feature of the rate of change of current and temperature.

[0010] Preferably, the calculation formula for the feature-weighted regression model is as follows:

[0011] ;in, The wear index is the value of the printhead. For the first The weights of each feature For the first The value of each feature, The total number of features.

[0012] Preferably, the weight The objective function, determined using the minimum mean square error optimization method, is: ;

[0013] in, For the first The actual wear index of each sample The wear index predicted by the model. This represents the number of training samples.

[0014] Preferably, the formula for predicting the remaining lifespan is: ;

[0015] in, For the remaining lifespan of the printhead, This is the initial lifespan of the printhead. This is the wear rate coefficient. This represents the current wear index.

[0016] Preferably, the remaining lifespan prediction result of the print head is displayed in real time through the control system of the 3D printing equipment, and an alarm is triggered when the wear index exceeds a preset threshold.

[0017] Preferably, the data acquisition is achieved through a sensor, which is installed on the power supply circuit and heat conduction path of the print head.

[0018] Preferably, the method further includes: dynamically adjusting the weight parameters of the feature-weighted regression model based on historical wear data to adapt to the differences in wear patterns of different printheads.

[0019] Technical effects and advantages of the present invention: The printhead wear detection method based on machine learning proposed in this invention has the following advantages compared with the prior art:

[0020] This invention addresses the problems of reliance on manual judgment, low detection accuracy, and insufficient predictive ability in existing technologies. By collecting data on current, temperature, and operating time, and extracting key features to construct a regression model, it achieves real-time assessment of printhead wear status and prediction of remaining lifespan. Compared to traditional methods, this approach integrates multi-source data, improving detection accuracy and enabling earlier identification of wear trends; it employs a weighted calculation method to establish quantitative relationships, enhancing the scientific rigor of lifespan prediction; and it combines with a control system to achieve automatic early warning, reducing unnecessary replacements and the risk of sudden failures, thereby improving equipment operating efficiency, reducing maintenance costs, and enhancing the intelligence and stability of the 3D printing system. Attached Figure Description

[0021] Figure 1 This is a flowchart of the machine learning-based printhead wear detection method of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention provides, for example Figure 1 The present invention illustrates a machine learning-based printhead wear detection method. This method is based on machine learning algorithms and constructs an analysis model by collecting key data such as the current, temperature and working time of the printhead to identify wear characteristics and predict the remaining life of the printhead.

[0024] In this embodiment, the machine learning-based printhead wear detection method includes the following steps:

[0025] Step 1: Collect the current data, temperature data, and cumulative working time data of the printhead; data acquisition is achieved through sensors installed on the power supply circuit and heat conduction path of the printhead.

[0026] Step 2: Denoise and normalize the current data, temperature data, and cumulative working time data to generate preprocessed data; specifically, the normalization of the preprocessed data is achieved by linear scaling, mapping the current data and temperature data to the [0,1] interval to eliminate the influence of different dimensions.

[0027] Step 3: Extract current fluctuation characteristics, temperature change trends, and cumulative working time characteristics from the preprocessed data to form a feature dataset; the feature dataset also includes the product characteristics of current fluctuation and temperature change, as well as the correlation characteristics of the rate of change of current and temperature.

[0028] The current fluctuation characteristics include at least one of the following: average current, standard deviation, peak current, and fluctuation frequency; the temperature change trend includes at least one of the following: average temperature, maximum temperature, minimum temperature, and temperature change rate.

[0029] Step 4: Train a feature-weighted regression model using the aforementioned feature dataset, and calculate the printhead wear index through weighted summation; the calculation formula for the feature-weighted regression model is: ;in, The wear index is the value of the printhead. For the first The weights of each feature For the first The value of each feature, The total number of features.

[0030] Among them, weight The objective function, determined using the minimum mean square error optimization method, is: ;in, For the first The actual wear index of each sample The wear index predicted by the model. This represents the number of training samples.

[0031] Step 5: Based on the wear index and the preset initial lifespan parameters, predict the remaining lifespan of the printhead; the formula for predicting the remaining lifespan is: ;in, For the remaining lifespan of the printhead, This is the initial lifespan of the printhead. This is the wear rate coefficient. This represents the current wear index. The remaining lifespan prediction of the printhead is displayed in real time through the control system of the 3D printing equipment, and an alarm is triggered when the wear index exceeds a preset threshold.

[0032] Further measures include dynamically adjusting the weight parameters of the feature-weighted regression model based on historical wear data to adapt to the differences in wear patterns of different printheads.

[0033] The core of this method lies in modeling and analyzing the multidimensional data generated during printhead operation. Current data reflects the load on the printhead during material extrusion, temperature data reflects changes in internal heat conduction, and operating time is a key indicator of the cumulative usage time of the printhead. By inputting this data into a machine learning model, wear-related features can be extracted, and a predictive model can be built, thereby achieving dynamic monitoring of printhead wear. This method not only improves detection accuracy but also reduces manual intervention, enhancing overall production efficiency.

[0034] To more clearly explain the operating principle of the machine learning-based printhead wear detection method described above, the following will provide further explanation with reference to specific embodiments:

[0035] For data acquisition, this method uses sensors to acquire real-time current and temperature data of the printhead and records the cumulative working time of the printhead using timestamps. The data acquisition system is integrated with the printing equipment to ensure data continuity and integrity. The acquired data undergoes preprocessing to remove noise and outliers to improve the reliability of subsequent analysis. The preprocessed data is then input into a machine learning model for training and validating the prediction model. The model construction process includes feature extraction, model selection, and parameter tuning to ensure that the model can accurately capture the key characteristics of printhead wear.

[0036] The advantage of this method lies in its ability to monitor and predict printhead wear in real time. By deeply analyzing printhead operating data, it can accurately identify early signs of wear and issue timely warnings before printhead performance significantly declines. Furthermore, this method can accurately predict the remaining lifespan of the printhead based on historical data, providing a scientific and reliable basis for developing maintenance plans. This predictive capability not only effectively reduces equipment maintenance costs but also significantly improves the stability of the production system and reduces downtime caused by printhead failures.

[0037] In the 3D printing process, the current, temperature, and operating time of the print head are important indicators for measuring its operational status. Current data reflects the load on the print head when extruding material and is typically collected at the start of operation. Since the load on the print head is closely related to factors such as material extrusion resistance and nozzle clogging, changes in current directly reflect the print head's working condition. Temperature data is used to monitor heat conduction within the print head. High temperatures can lead to thermal fatigue and aging of internal components, thus accelerating wear.

[0038] Operating time is a key indicator for measuring the cumulative usage time of the printhead. By recording the duration of each print job, the total operating time of the printhead can be calculated and correlated with the degree of wear. To ensure the accuracy and reliability of the data, this method uses sensors to collect the current and temperature of the printhead in real time.

[0039] A current sensor is installed in the power supply circuit of the printhead to measure current fluctuations as the printhead extrudes material. A temperature sensor is embedded in critical parts of the printhead, such as near the nozzle, to monitor temperature changes.

[0040] The data acquisition system is integrated with the printing equipment to ensure data continuity and integrity. Acquired data is recorded with timestamps for subsequent analysis. The raw data typically contains noise and outliers, requiring preprocessing to improve the accuracy of subsequent analysis. Preprocessing includes data cleaning, normalization, and feature extraction. Data cleaning removes invalid data and outliers, such as sudden current spikes or abnormal temperature fluctuations. Normalization unifies data with different dimensions to a common scale, allowing the model to better process the data.

[0041] Feature extraction involves extracting features related to printhead wear from the raw data, such as the frequency distribution of current fluctuations and the trend of temperature changes. These features will serve as input to the subsequent machine learning model for training and predicting printhead wear conditions. After data preprocessing, the data is divided into training and test sets for training and validating the machine learning model. The training set is used to build the model, while the test set is used to evaluate the model's generalization ability.

[0042] The model's input includes preprocessed current, temperature, and operating time data, while the output is the printhead's wear status and predicted remaining lifespan. By appropriately partitioning the dataset, the model can maintain high prediction accuracy even when faced with new data. Feature extraction is a crucial step in building a machine learning model. This method extracts multiple features from the preprocessed current, temperature, and operating time data to capture key information about printhead wear.

[0043] First, the fluctuation characteristics of the current data were extracted, including the average current, standard deviation, peak value, and fluctuation frequency. These characteristics reflect the load changes of the printhead during material extrusion, and abnormal fluctuations in current may indicate wear or blockage inside the printhead.

[0044] Secondly, the temperature data's changing trends were also extracted as features, including the average, maximum, and minimum temperatures, as well as the rate of temperature change. High temperatures can lead to thermal fatigue of internal printhead components, while rapid temperature changes may indicate problems with the printhead's cooling system. Besides current and temperature characteristics, runtime is also an important feature. The degree of printhead wear is usually proportional to its cumulative runtime, therefore, the length of runtime directly affects the printhead's remaining lifespan. By combining runtime with current and temperature characteristics, a more comprehensive assessment of the printhead's wear condition can be achieved.

[0045] Furthermore, this method also extracts the interaction features between current and temperature, such as the product of current fluctuations and temperature changes, and the correlation between the rates of change of current and temperature. These interaction features can reveal the wear patterns of the printhead under different operating conditions, thereby improving the predictive ability of the model.

[0046] After feature extraction, the next step is model construction. This method employs a feature-weighted regression model to predict the remaining lifespan of the printhead. The basic formula of the model is as follows: in, Indicates the remaining lifespan of the print head. It is the first The weights of each feature, It is the first The value of each feature, It's a bias term. Weight This represents the degree of influence of each feature on remaining lifetime, and the optimal weight values ​​are determined through the training process. Bias term. Used to adjust the model's predictions to make them closer to the actual values.

[0047] The model building process includes feature selection, weight optimization, and model validation. Feature selection identifies the features that have the greatest impact on printhead wear status to reduce model complexity and improve prediction accuracy. Weight optimization adjusts the weights of each feature using training data, enabling the model to more accurately predict remaining lifespan. Model validation evaluates the model's predictive performance using a test set, ensuring that the model maintains high accuracy when faced with new data.

[0048] During model training, an optimization method based on minimum mean squared error (MSE) was employed to minimize the difference between predicted and actual values. The optimization objective function is as follows: ;in, It is the first The actual remaining lifetime of each sample This represents the model's predicted remaining lifetime. Through iterative optimization, the model can continuously adjust the weights and biases to minimize the prediction error.

[0049] After model training is complete, its generalization ability needs to be evaluated. Evaluation methods include cross-validation and independent test set validation. Cross-validation evaluates the model's performance on different datasets by splitting the training data multiple times, thus preventing overfitting. Independent test set validation uses data not used in training to test the model, ensuring that it maintains high prediction accuracy when faced with new data.

[0050] Through the steps described above, this method constructs a model capable of accurately predicting the remaining lifespan of the printhead. This model can not only identify early signs of printhead wear but also predict future wear trends based on historical data, providing a scientific basis for printhead maintenance and replacement.

[0051] After constructing a feature-weighted regression model, this model can be used to predict the wear status and remaining life of the printhead in real time. The core principle of the model lies in using collected current, temperature, and operating time data, combined with extracted features, to calculate the printhead wear index and further predict its remaining life. The formula for calculating the wear index is as follows: ;in, This indicates the wear index of the printhead. It is the first The weights of each feature, It is the first The formula uses a weighted summation method to combine the effects of multiple features, forming a numerical value that reflects the degree of printhead wear. A higher wear index indicates more severe printhead wear, while a lower index indicates a newer printhead.

[0052] After calculating the wear index, the remaining life of the printhead can be further predicted. The prediction of remaining life is based on the trend of the wear index. Assuming that printhead wear is a cumulative process over time, its remaining life is expressed as: in, Indicates the remaining lifespan of the print head. This refers to the initial lifespan of the printhead. It is the wear rate coefficient. This is the current wear index. The formula indicates that as the wear index increases, the remaining life of the printhead gradually decreases. By periodically calculating the wear index and substituting it into the remaining life formula, the remaining life of the printhead can be dynamically assessed.

[0053] In practical applications, this model can be integrated with the control system of 3D printing equipment to achieve real-time monitoring of printhead wear. When the wear index exceeds a set threshold, the system will automatically issue a warning, prompting the operator to check or replace the printhead.

[0054] Furthermore, the system can develop maintenance plans based on the predicted remaining lifespan, ensuring timely replacement of the printhead before it fails, thereby reducing production interruptions caused by printhead malfunctions. After completing the construction of the printhead wear prediction model, the next step is to integrate the model with the control system of the 3D printing equipment to achieve real-time monitoring and prediction of printhead wear status. The key to system integration lies in three aspects: data acquisition, model deployment, and feedback control.

[0055] First, the data acquisition system needs to work closely with the hardware modules of the 3D printing equipment. Current and temperature sensors are installed on the power supply circuit and critical heat conduction path of the print head, respectively, to ensure accurate acquisition of the print head's operating status. The sensor data acquisition frequency is set to 10 times per second to capture subtle changes in the print head under different operating conditions. The acquired raw data is sent to the data processing unit via serial communication or wireless transmission. The data processing unit is responsible for preprocessing the data, including noise reduction, normalization, and feature extraction.

[0056] Secondly, model deployment is a core component of system integration. Preprocessed data is input into a pre-trained machine learning model, which calculates the wear index of the print head based on input features and further predicts its remaining lifespan. Model deployment can be achieved through embedded systems or cloud servers, the specific choice depending on the hardware configuration and network environment of the 3D printing equipment. In embedded systems, the model is compiled into an executable file and runs directly on the main control unit of the 3D printing equipment, reducing data transmission latency and improving real-time performance. In cloud-based deployment, the model runs on a remote server and communicates with the 3D printing equipment via an API interface, suitable for scenarios requiring multiple devices to work collaboratively.

[0057] Finally, the feedback control system is responsible for taking appropriate measures based on the model's predictions. When the wear index of the printhead exceeds a set threshold, the system automatically triggers an alarm and displays a warning message on the user interface. Furthermore, the system can generate a maintenance plan based on the predicted remaining lifespan, reminding operators to replace the printhead at the appropriate time to avoid production interruptions due to printhead failure. For highly automated 3D printing equipment, the system can also be integrated with maintenance robots to automatically initiate the replacement process when the printhead is detected to be nearing failure, thereby improving maintenance efficiency.

[0058] Through the aforementioned system integration solution, the printhead wear prediction model can be seamlessly integrated with 3D printing equipment, enabling real-time monitoring and intelligent management of printhead wear status. This solution not only extends the lifespan of the printhead but also reduces equipment maintenance costs, providing strong support for the intelligent development of the 3D printing industry.

[0059] This method has demonstrated significant practical value in multiple 3D printing applications. Firstly, in the industrial manufacturing sector, 3D printing is widely used to produce parts, such as in the aerospace, automotive, and medical device industries. In these industries, printhead wear can lead to dimensional deviations and increased surface roughness in printed parts, affecting product quality. This method allows for real-time monitoring of printhead wear and provides early warnings before printhead performance deteriorates, thereby reducing scrap rates due to printhead failure and improving production efficiency. Furthermore, this method can also optimize equipment maintenance plans based on the remaining lifespan of the printhead, reducing unnecessary downtime and lowering maintenance costs.

[0060] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A machine learning-based method for detecting printhead wear, characterized in that, Includes the following steps: Collect the current data, temperature data, and cumulative working time data of the print head; The current data, temperature data, and cumulative working time data are denoised and normalized to generate preprocessed data; Current fluctuation characteristics, temperature change trends, and cumulative working time characteristics are extracted from the preprocessed data to form a feature dataset. The feature dataset is used to train a feature-weighted regression model, and the wear index of the printhead is calculated by weighted summation. Based on the wear index and the preset initial life parameters, the remaining life of the printhead is predicted; The calculation formula for the feature-weighted regression model is as follows: ;in, The wear index is for the printhead. For the first The weights of each feature For the first The value of each feature, The total number of features; The weight The objective function, determined using the minimum mean square error optimization method, is: ; in, For the first The actual wear index of each sample The wear index predicted by the model. This represents the number of training samples; The formula for predicting the remaining lifespan is: ; in, For the remaining lifespan of the printhead, This is the initial lifespan of the printhead. This is the wear rate coefficient. This represents the current wear index.

2. The printhead wear detection method based on machine learning according to claim 1, characterized in that, The current fluctuation characteristics include at least one of the following: average current, standard deviation, peak current, and fluctuation frequency; the temperature change trend includes at least one of the following: average temperature, maximum temperature, minimum temperature, and temperature change rate.

3. The printhead wear detection method based on machine learning according to claim 1, characterized in that, The normalization of the preprocessed data is achieved through linear scaling, which maps the current data and temperature data to the [0,1] interval to eliminate the influence of different dimensions.

4. The printhead wear detection method based on machine learning according to claim 1, characterized in that, The feature dataset also includes the product feature of current fluctuation and temperature change, as well as the correlation feature between the rate of change of current and temperature.

5. The printhead wear detection method based on machine learning according to claim 1, characterized in that, The remaining life prediction result of the print head is displayed in real time through the control system of the 3D printing equipment, and an alarm is triggered when the wear index exceeds a preset threshold.

6. The printhead wear detection method based on machine learning according to claim 1, characterized in that, Data acquisition is achieved through sensors, which are installed on the power supply circuit and heat conduction path of the printhead.

7. The printhead wear detection method based on machine learning according to claim 1, characterized in that, The method also includes dynamically adjusting the weight parameters of the feature-weighted regression model based on historical wear data to adapt to the differences in wear patterns of different printheads.

Images