A machine tool shank chip detection system and detection method

Abstract

This invention relates to a chip detection system and method for machine tool holders. The detection system includes a data acquisition module, a transmission module, and a processing module. The data acquisition module is connected to the transmission module and is used to acquire the rotational speed of the machine tool holder during machining and the distance between the acquisition module and the machine tool holder. The transmission module is connected to the data acquisition module and the processing module and is used to acquire real-time data, including the rotational speed of the machine tool holder during machining, the acquisition frequency of the acquisition module, and the distance between the acquisition module and the machine tool holder. The real-time data is then transmitted to the processing module. The processing module is connected to the machine tool and is used to process and analyze the real-time data to determine the actual runout of the machine tool holder during machining. Based on the actual runout, it determines whether chip jamming exists. If chip jamming is present, the processing module issues an alarm and controls the machine tool holder to stop machining. The beneficial effects are that it can not only handle various machining situations but also significantly improve detection accuracy.

Description

Technical Field

[0001] This invention relates to the field of machining technology, and in particular to a chip detection system and method for machine tool holders. Background Technology

[0002] In the field of machining, especially in aluminum alloy machining, the machine tool holder, as the core component connecting the spindle and the cutting tool, directly determines machining accuracy, tool life, and workpiece quality. Aluminum chips generated during aluminum alloy machining easily adhere to the tool holder surface. Even with the spindle typically equipped with an air-blowing cleaning device during tool changes, some aluminum chips inevitably get stuck in the clearance between the tool holder and the spindle—a problem particularly prominent with tool holders using standard interfaces such as BT, HSK, and CAT. These tool holders achieve a high-rigidity fit between a tapered surface and the spindle's inner bore. Internal pull studs or clamping mechanisms ensure clamping stability during high-speed rotation. However, aluminum chips stuck in the tapered surface, pull stud grooves, or mating interface disrupt the precise fit, leading to tool length deviations or abnormal runout values, directly causing workpiece dimensional deviations and significant quality risks. With the development of high-speed cutting, precision machining, and intelligent manufacturing technologies, the requirements for tool holder operational stability are increasingly stringent. Real-time chip detection has become a critical requirement for ensuring machining safety and quality.

[0003] If chip jamming is not detected and addressed in a timely manner, it will trigger a series of chain risks: From the perspective of machining quality, chip jamming will cause tool holder rotational eccentricity, reduced system rigidity, increased machining vibration, deterioration of workpiece surface roughness, and even irreversible defects such as dimensional deviations; from the perspective of equipment safety, chip jamming may cause the tool holder to fail to fully insert into the spindle or insufficient clamping force, causing the tool to loosen or even fall off during high-speed rotation, seriously threatening spindle accuracy and operator safety; from the perspective of economic cost, long-term chip jamming will accelerate the wear of the mating surfaces of the spindle and tool holder, shorten the service life of key components, increase equipment maintenance costs and unplanned downtime, and significantly reduce production efficiency.

[0004] Currently, the industry's methods for detecting and preventing chipping in tool holders can be mainly categorized into four types, but all of them have significant technical shortcomings:

[0005] One method is manual visual inspection, where operators inspect the conical surface of the tool holder and the pull stud area with the naked eye or with the aid of magnifying glasses, flashlights, or other tools before and after tool changes. While simple and intuitive, this method is extremely inefficient and difficult to adapt to automated, high-speed modern production lines. Furthermore, the inspection results are greatly affected by the operator's experience, fatigue level, and lighting conditions, making it highly subjective and prone to missing tiny debris hidden inside the pull stud holes or in the minute gaps of the conical surface.

[0006] Secondly, there is air pressure / air tightness detection. Some high-end machine tools are equipped with this system, which blows air into the spindle before tool change and monitors changes in air pressure to determine if there is any obstruction. However, this technology is primarily used to assess the cleanliness of the spindle end face, and its ability to detect chip jamming on the tool holder body (especially when the tool holder is not installed) is limited. It cannot accurately locate the chip jamming position, nor can it assess the severity of the chip jamming, thus its application scenarios are clearly limited.

[0007] Thirdly, there is the initial application of image recognition technology, which involves capturing images of the tool holder with an industrial camera and combining them with algorithms to identify foreign objects on the surface. However, the existing solutions have extremely poor adaptability: differences in tool holder materials (steel, cemented carbide, etc.), surface treatments (plating, oxidation, etc.), lighting conditions, and the diversity of chip morphology (oil-stained chips, fine filamentous aluminum chips, etc.) all seriously affect the recognition accuracy; at the same time, conventional two-dimensional imaging is difficult to capture chips stuck in deep holes, grooves, or shadowed areas, resulting in a large number of detection blind spots.

[0008] Fourthly, vibration or acoustic monitoring indirectly infers the chip jamming status by collecting vibration signals or acoustic emission characteristics during tool changing. This type of method is an indirect detection, lacks specificity, and is easily affected by irrelevant factors such as tool wear and spindle imbalance. It is difficult to achieve direct and reliable identification of chip jamming, and the risk of misjudgment and missed judgment is relatively high. Summary of the Invention

[0009] (a) Technical problems to be solved

[0010] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a machine tool holder chip jamming detection system, which solves the technical problems of poor adaptability and poor detection accuracy of the prior art.

[0011] (II) Technical Solution

[0012] To achieve the above objectives, the main technical solutions adopted by the present invention include:

[0013] On one hand, the present invention provides a machine tool holder chip jamming detection system, comprising a data acquisition module, a transmission module, and a processing module. The data acquisition module is connected to the transmission module and is used to acquire the rotational speed n and the distance x(i) between the data acquisition module and the machine tool holder during machining. The transmission module is connected to the data acquisition module and the processing module and is used to acquire real-time data, including the rotational speed n of the machine tool holder during machining acquired by the data acquisition module, the acquisition frequency f of the data acquisition module, and the distance x(i) between the data acquisition module and the machine tool holder. The real-time data is then transmitted to the processing module. The processing module is connected to the machine tool and is used to process and analyze the real-time data to determine the actual runout V1(m) of the machine tool holder during machining. Based on the actual runout, the processing module determines whether chip jamming exists. If chip jamming exists, the processing module issues an alarm and controls the machine tool holder to stop machining.

[0014] Optionally, the acquisition module includes an eddy current sensor and a bracket; the bracket is mounted on the machine tool and is located on one side of the machine tool shank; the eddy current sensor is mounted on the bracket; the eddy current sensor is connected to the transmission module; the acquisition frequency of the eddy current sensor is the sampling frequency f, and the detection distance of the eddy current sensor is x(i).

[0015] Optionally, the bracket includes a fixed base, a connecting base, and a mounting base; the fixed base is fixedly installed on the machine tool, the mounting base is set on the fixed base through the connecting base, and the eddy current sensor is set on the mounting base.

[0016] Optionally, the distance between the eddy current sensor and the machine tool holder is 0.3~3mm.

[0017] Optionally, the mounting base is shaped like a fan-shaped plate, with a fan-shaped through hole in the middle, the central angle of which is 60°~90°. Above the fan-shaped through hole, there are two elongated holes along the circumference, which are used to fine-tune the installation position of the mounting base; the length of the elongated holes is 25~30mm and the width is 6~8mm.

[0018] Optionally, the connector is L-shaped, having an integrally formed horizontal section and a vertical section; the horizontal section has a first connecting hole for connecting the fixing seat; the vertical section has a first mounting hole for mounting the mounting seat, the first mounting hole being an oblong hole extending vertically; the oblong hole has a length of 15~20mm and a width of 5~6mm.

[0019] Optionally, the vertical section includes a first step and a second step; the second step is connected to the horizontal section; the thickness of the first step is less than that of the second step; and the first mounting hole is located in the first step.

[0020] Optionally, the mounting base is a block structure with a second connecting hole and a second mounting hole; the mounting base is connected to the connecting base through the second connecting hole; the second mounting hole is used to install the eddy current sensor.

[0021] On the other hand, the present invention provides a detection method for the above-mentioned machine tool holder chip jamming monitoring system, comprising the following steps:

[0022] S1. The processing module obtains the real-time data transmitted by the transmission module;

[0023] S2. The processing module processes and periodically segments the real-time data to obtain a highly consistent set of effective periods.

[0024] S3. The processing module fits a smooth profile to each effective period using the RANSAC algorithm and calculates the detection residual to obtain the detection jump curve for each effective period.

[0025] S4. The processing module calculates the actual oscillation amount V1 (m) based on the detection bounce curve of each effective cycle; when V1 (m) > Vt When V1(m) ≤ V, it is determined that there is debris. t The time was determined to be without debris; among them, V t This is the detection threshold.

[0026] Optionally, step S2 includes:

[0027] S21. The processing module calculates the number of sampling points N per cycle based on real-time data using Formula 1; where Formula 1 is:

[0028] ;

[0029] S22. The processing module performs standardization processing on the real-time data to obtain a standardized data set of all sampling points;

[0030] S23. The processing module extracts 2N sampling points from the beginning of the standardized data set, removes 50 transient interference points at the beginning and end, and obtains the initial reference period.

[0031] S24. The processing module performs cross-correlation calculations on the initial reference period to obtain the cross-correlation coefficient R(τ) when the delay step size is τ; and determines that when R(τ) ≥ 0.9, it records τ as the starting point of the period, obtaining the set of starting points {τ1, τ2, ..., τ...}. M}, where M is the number of candidate periods;

[0032] S25. The processing module takes each located starting point as a reference and extracts a fixed length N of sampling points to make the data length of each candidate period consistent.

[0033] S26. The processing module filters out the valid periods from the intercepted candidate periods to obtain a set of valid periods.

[0034] Optionally, step S22 includes:

[0035] S221. Collect real-time data within a fixed time window to obtain the original data set {x(1), x(2), ..., x(L)}; where L is the total number of sampling points within the time window;

[0036] S222. Transform the original dataset into a standardized dataset {x1(1), x1(2), ..., x1(L)}.

[0037] Optionally, in step S222,

[0038] The original dataset is transformed into a standardized dataset {x1(1), x1(2), ..., x1(L)} using Formula 2; Formula 2 is:

[0039] ;

[0040] Where x1(i) is the standardized data of the i-th sampling point, μ0 is the mean of all raw data within the fixed time window, and σ0 is the standard deviation of all raw data within the fixed time window.

[0041] Optionally, step S26 includes:

[0042] S261. Select the first candidate period as the reference period;

[0043] S262. The Pearson correlation coefficient r of the reference period is calculated using the coefficient formula;

[0044] S263. Retain candidate periods with r ≥ 0.85 and remove distorted periods with r < 0.85 to obtain the effective period set.

[0045] (III) Beneficial Effects

[0046] The beneficial effects of this invention are:

[0047] This invention provides a machine tool holder chip jamming detection system. The acquisition module can simultaneously capture the real-time rotational speed n of the tool holder and the distance x(i) between the acquisition module and the tool holder during the dynamic process of machine tool holder machining. The rotational speed n can accurately match the spindle speed drift that may occur during machining. Whether it is adapting to tool holders with different standard interfaces such as BT and HSK, or dealing with the special scenario of aluminum chips easily getting stuck in the fit gap in aluminum alloy machining, it can provide basic parameters that match the current machining state for subsequent data processing. It effectively solves the limitations of existing image recognition due to the influence of tool holder material and surface condition, as well as the problem that static detection cannot cope with dynamic machining scenarios. The distance x(i), as the core data that directly reflects the operating state of the tool holder, can capture the subtle positional changes caused by chip jamming, providing raw data support for detection accuracy. The transmission module efficiently acquires real-time data such as rotational speed n, acquisition frequency f, and distance x(i) and transmits them to the processing module in real time. This ensures that the processing module can process and analyze these parameters that are matched with the machining conditions in real time to obtain the actual runout amount V1(m) that directly represents the impact of chip jamming. This runout amount is determined by quantifying the abnormal jumping caused by chip jamming, replacing the inference method that relies on indirect signals in existing vibration and acoustic monitoring. This reduces the risk of misjudgment caused by interference factors such as tool wear and spindle imbalance, and also overcomes the accuracy defects of manual inspection in missing small chips and the inability of air pressure detection to quantify the impact of chip jamming. Once the processing module detects chip jamming, it immediately issues an alarm and controls the machine tool holder to stop processing, promptly preventing quality problems such as tool length deviation and workpiece deviation caused by aluminum chips getting stuck. At the same time, it prevents equipment damage and safety accidents, and the entire process requires no manual intervention, making it suitable for automated production needs. Compared with existing technologies, its technical path of dynamically collecting core parameters adapted to the working conditions, analyzing and quantifying characteristics in real time, and automatically linking control solves the problems of poor adaptability and inability to cope with complex processing scenarios of existing detection methods, and overcomes the pain points of poor detection accuracy and easy misjudgment and missed detection. It achieves reliable monitoring of chip jamming during processing and improves the stability of production processing quality.

[0048] This invention provides a detection method for a machine tool tool holder chip detection system. Through a coherent technical path—real-time data acquisition, periodic segmentation and purification, RANSAC fitting residual extraction, and oscillation threshold determination—it achieves high precision, strong adaptability, and high reliability in chip detection. The method first acquires real-time data such as rotational speed n, distance x(i), and acquisition frequency f. Combined with dynamic calculation of the number of sampling points per period, standardization processing, cross-correlation period positioning, and Pearson correlation coefficient screening, it accurately segments a highly consistent set of effective periods. This not only adapts to the spindle speed drift during machining by dynamically calculating the number of sampling points, but also eliminates cutting fluid and temperature fluctuations through standardization. To address the challenges of existing detection methods, which are prone to poor adaptability to dynamic machining scenarios and susceptible to interference, a dual correlation screening process is used to eliminate distorted cycles. Subsequently, the RANSAC algorithm is employed to fit a smooth contour to each effective cycle. By calculating the detection residual, a detection runout curve accurately reflecting chip jamming anomalies is obtained. This effectively separates the normal structural contour of the tool holder from the local fluctuations caused by chip jamming, avoiding misjudgments and missed detections caused by the inability of traditional indirect detection methods (such as vibration and acoustic monitoring) to distinguish between normal fluctuations and chip jamming anomalies. This overcomes the core pain point of poor detection accuracy. Finally, the actual runout amount V1 (m) is calculated from the detection runout curve and compared with the detection threshold V. t The chip jamming detection is completed by direct comparison, and the entire process does not require manual intervention. It achieves real-time response and accurate detection of chip jamming during processing, which can not only prevent risks such as workpiece deviation and equipment damage caused by chip jamming in a timely manner, but also adapt to different standard tool holders such as BT and HSK, as well as scenarios that are prone to generating sticky chips, such as aluminum alloy processing, which significantly improves the quality stability of precision machining. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the installation of an eddy current sensor in a machine tool holder chip detection system according to Embodiment 1 of the present invention;

[0050] Figure 2 This is a schematic diagram of the structure of the fixing base in Embodiment 1 of the present invention;

[0051] Figure 3 This is a front view of the fixing base of Embodiment 1 of the present invention;

[0052] Figure 4 This is a schematic diagram of the structure of the connector in Embodiment 1 of the present invention;

[0053] Figure 5 This is a front view of the connector of Embodiment 1 of the present invention;

[0054] Figure 6 This is a schematic diagram of the mounting base according to Embodiment 1 of the present invention;

[0055] Figure 7 This is a front view of the mounting base of Embodiment 1 of the present invention;

[0056] Figure 8 This is a flowchart illustrating the detection stage of the detection method of the machine tool holder chip detection system in Embodiment 2 of the present invention.

[0057] [Explanation of Labels in the Attached Image]

[0058] 1: Eddy current sensor; 2: Mounting base; 21: Fan-shaped through hole; 22: Elongated hole; 3: Connecting base; 31: Horizontal section; 32: Vertical section; 33: First connecting hole; 34: First mounting hole; 35: First stepped section; 36: Second stepped section; 4: Mounting base; 41: Second connecting hole; 42: Second mounting hole. Detailed Implementation

[0059] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present invention can be understood more clearly and thoroughly, and that the scope of the present invention can be fully conveyed to those skilled in the art.

[0060] Example 1:

[0061] This invention provides a machine tool holder chip jamming detection system, comprising a data acquisition module, a transmission module, and a processing module. The data acquisition module is connected to the transmission module and is used to acquire the rotational speed n and the distance x(i) between the data acquisition module and the machine tool holder during machining. The transmission module is connected to both the data acquisition module and the processing module and is used to acquire real-time data, including the rotational speed n acquired by the data acquisition module, the acquisition frequency f of the data acquisition module, and the distance x(i) between the data acquisition module and the machine tool holder; and transmits the real-time data to the processing module. The processing module is connected to the machine tool and is used to process and analyze the real-time data to determine the actual runout V1 (m) of the machine tool holder during machining, and to determine whether chip jamming exists based on the actual runout. If chip jamming is present, the processing module issues an alarm and controls the machine tool holder to stop machining.

[0062] Specifically, the acquisition module can simultaneously capture the real-time rotational speed n of the tool holder and the distance x(i) between the acquisition module and the tool holder during the dynamic process of machining the machine tool holder. The rotational speed n can accurately match the spindle speed drift that may occur during machining. Whether it is adapting to tool holders with different standard interfaces such as BT and HSK, or dealing with the special scenario of aluminum chips easily getting stuck in the fit gap in aluminum alloy machining, it can provide basic parameters that match the current machining state for subsequent data processing. It effectively solves the adaptation limitations of existing image recognition affected by the material and surface condition of the tool holder, as well as the problem that static detection cannot cope with dynamic machining scenarios. The distance x(i), as the core data that directly reflects the operating state of the tool holder, can capture the subtle positional changes caused by chip jamming, providing raw data support for detection accuracy. The transmission module efficiently acquires real-time data such as rotational speed n, acquisition frequency f, and distance x(i) and transmits them to the processing module in real time. This ensures that the processing module can process and analyze these parameters that are matched with the machining conditions in real time to obtain the actual runout amount V1(m) that directly represents the impact of chip jamming. This runout amount is determined by quantifying the abnormal jumping caused by chip jamming, replacing the inference method that relies on indirect signals in existing vibration and acoustic monitoring. This reduces the risk of misjudgment caused by interference factors such as tool wear and spindle imbalance, and also overcomes the accuracy defects of manual inspection in missing small chips and the inability of air pressure detection to quantify the impact of chip jamming. Once the processing module detects chip jamming, it immediately issues an alarm and controls the machine tool holder to stop processing, promptly preventing quality problems such as tool length deviation and workpiece deviation caused by aluminum chips getting stuck. At the same time, it prevents equipment damage and safety accidents, and the entire process requires no manual intervention, making it suitable for automated production needs. Compared with existing technologies, its technical path of dynamically collecting core parameters adapted to the working conditions, analyzing and quantifying characteristics in real time, and automatically linking control solves the problems of poor adaptability and inability to cope with complex processing scenarios of existing detection methods, and overcomes the pain points of poor detection accuracy and easy misjudgment and missed detection. It achieves reliable monitoring of chip jamming during processing and improves the stability of production processing quality.

[0063] Furthermore, such as Figure 1As shown, the acquisition module includes an eddy current sensor 1 and a bracket. The bracket is mounted on the machine tool and is located on one side of the machine tool shank; the eddy current sensor 1 is mounted on the bracket; the eddy current sensor 1 is connected to the transmission module, the acquisition frequency of the eddy current sensor 1 is the sampling frequency f, and the distance detected by the eddy current sensor 1 is x(i). In this embodiment, the distance between the eddy current sensor 1 and the machine tool shank is 0.3~3mm. The acquisition module significantly improves the adaptability and accuracy of the detection system through the configuration of the eddy current sensor 1 and the bracket: the bracket provides a stable mounting foundation for the eddy current sensor 1, and the installation position can be flexibly adjusted according to different machine tool layouts and the structural characteristics of various standard tool holders such as BT / HSK, ensuring that the distance between the eddy current sensor 1 and the tool holder is kept within the optimal detection range. This effectively adapts to the diverse equipment and tool holder types in automated production lines, while avoiding the stringent requirements of existing image recognition technologies on installation space and lighting conditions, thus solving the problem of poor adaptability of traditional detection solutions. The machine tool's nc acquires the real-time rotational speed n of the tool holder during machining, while the eddy current sensor 1 non-contactly detects the distance x(i) between itself and the tool holder at a fixed sampling frequency f. The two work together—the machine tool's nc captures the rotational speed drift under dynamic working conditions, providing subsequent data processing with information about the current machining status. For the matching basic parameters, the eddy current sensor 1, with its non-contact detection characteristics, avoids physical interference with the high-speed rotating tool holder. At the same time, it can penetrate interference such as cutting fluid and dust, and directly capture the micron-level distance changes caused by chip jamming. Compared with the shortcomings of manual visual inspection being unable to identify tiny chips and vibration monitoring relying on indirect signals, it ensures detection accuracy from the data acquisition level. The rotational speed n and distance x(i) collected by the two types of sensors are core parameters that directly reflect the operating state of the tool holder. They are transmitted to the processing module in real time through the transmission module, providing reliable data support for the accurate calculation of the actual runout V1(m). This further overcomes the pain points of poor detection accuracy and easy misjudgment and omission in the existing technology, ensuring that the system can stably and accurately realize chip jamming detection and early warning in scenarios such as aluminum alloy processing where sticky chips are easily generated, providing dual protection for processing quality and production safety.

[0064] Furthermore, such as Figures 2-7As shown, the bracket includes a fixed base 2, a connecting base 3, and a mounting base 4; the fixed base 2 is fixedly installed on the machine tool, the mounting base 4 is set on the fixed base 2 through the connecting base 3, and the eddy current sensor 1 is set on the mounting base 4. Stable installation and flexible adaptation of the eddy current sensor 1 were achieved, significantly improving the structural reliability and scene adaptability of the detection system: the fixed base 2 is directly fixed to the machine tool, providing a solid installation foundation for the entire bracket, effectively resisting external interference such as machine tool vibration and cutting fluid impact during processing, avoiding sensor displacement due to loose installation, and ensuring the stability and consistency of distance data x(i) acquisition; the connecting base 3, as the intermediate connection structure between the fixed base 2 and the mounting base 4, can flexibly adjust the height, angle and horizontal position of the mounting base 4 according to different machine tool layouts, tool holder types (such as BT, HSK, CAT, etc.) and installation space requirements, so that the eddy current sensor 1 can be accurately aligned with the tool holder detection area, always maintaining an optimal detection distance of 0.3~3mm; the mounting base 4 is specifically designed to support the eddy current sensor 1, and its structural design fits the shape of the sensor, ensuring the coaxiality and perpendicularity of the sensor after installation, and avoiding distance detection errors caused by sensor tilt. This modular support structure not only solves the problems of poor adaptability and incompatibility with different machine tools and tool holders in existing sensor installation methods, but also reduces the impact of external interference on detection accuracy through structural stability. It provides reliable mechanical support for the eddy current sensor 1 to accurately collect distance data x(i), further ensuring the accuracy of subsequent vibration calculation and chip jamming judgment, and improving the industrial practicality and long-term operational stability of the entire detection system.

[0065] Specifically, such as Figure 2 and Figure 3As shown, the fixing base 2 is shaped like a fan-shaped plate, with a fan-shaped through hole 21 in the middle. The central angle of the fan-shaped through hole 21 is 60°~90°. Two elongated holes 22 are opened circumferentially above the fan-shaped through hole 21, which are used to make fine adjustments to the installation position of the fixing base 2; the length of the elongated holes 22 is 25~30mm and the width is 6~8mm. The fan-shaped plate shape can fit the arc-shaped installation space next to the machine tool tool holder, avoiding interference with the machine tool spindle, tool holder, and other components. Simultaneously, the fan-shaped structure provides a more uniform stress distribution, effectively resisting high-frequency vibrations during machine tool processing, preventing the bracket from becoming loose, and ensuring the stability of the sensor's detection position. The 60°~90° central angle fan-shaped through-hole 21 in the center significantly reduces the weight of the fixed base 2 and the load pressure at the machine tool installation point, while preventing the accumulation of machining dust and cutting fluid on the surface of the fixed base 2, reducing corrosion of the connection parts by dirt, and extending the service life of the bracket. Furthermore, this through-hole does not damage the core stress area of ​​the fan-shaped plate, ensuring that the fixed base 2 still has sufficient structural rigidity to support the connecting seat 3 and the mounting plate. The weight of mounting base 4; two elongated holes 22, 25-30mm long and 6-8mm wide, arranged circumferentially above the fan-shaped through hole 21, can not only adapt to the differences in the mounting screw hole spacing of different machine tool models, realizing fine adjustment of the horizontal position of the fixed base 2, but also assist in adjusting the installation angle of the fixed base 2 through the angular adjustment margin of the circumferential elongated holes 22, so that the subsequent connecting base 3 and mounting base 4 can drive the eddy current sensor to accurately align with the tool holder detection area, ensuring the optimal detection distance between the sensor and the tool holder, avoiding the distortion of detection data due to installation position deviation. At the same time, the size design of the elongated holes 22 takes into account both installation tightness and adjustment flexibility, and can adapt to various machine tools without the need for custom multi-specification fixed bases 2, greatly improving the scene adaptability and installation convenience of the detection system.

[0066] Specifically, such as Figure 4 and Figure 5As shown, the connecting seat 3 is L-shaped, with an integrally formed horizontal section 31 and a vertical section 32; the horizontal section 31 has a first connecting hole 33 for connecting the fixing seat 2; the vertical section 32 has a first mounting hole 34 for setting the mounting seat 4, the first mounting hole 34 is an oblong hole extending vertically; the oblong hole has a length of 15~20mm and a width of 5~6mm. The L-shaped structure, formed by the integrally molded horizontal section 31 and vertical section 32, possesses excellent overall structural rigidity. It effectively distributes the load transmitted between the fixed base 2 and the mounting base 4, resists high-frequency vibrations during machine tool processing, prevents loosening or deformation of the connection points, and ensures the long-term installation stability of the support system. Simultaneously, the L-shaped spatial form can adapt to the compact installation area next to the machine tool tool holder, avoiding interference with components such as the spindle and cutting tools, thus improving the spatial adaptability of the support. The first connecting hole 33 of the horizontal section 31 enables precise fastening to the fixed base 2, providing a stable mounting base for the entire connecting base 3 and ensuring that its relative position to the fixed base 2 does not shift. The vertical section 32 extends vertically for 15-20mm in length and 5-6mm in width... The waist-shaped first mounting hole 34 provides a vertical height adjustment margin of 0~15mm for the mounting base 4. It allows for flexible adjustment of the vertical position of the mounting base 4 and the eddy current sensor according to the size of different tool holders and the layout of the machine tool, ensuring that the sensor and the tool holder maintain the optimal detection distance and avoiding data distortion due to height deviation. Furthermore, because the width of the waist-shaped hole is only 5~6mm, it can limit the lateral displacement of the mounting base 4, ensuring that the detection direction of the sensor does not deviate during the adjustment process. It balances adjustment flexibility and installation stability, and can adapt to various detection scenarios without changing the connecting base 3 of different specifications. This greatly improves the scenario adaptability and installation convenience of the entire acquisition module, laying a solid mechanical installation foundation for the subsequent high-precision chip detection.

[0067] Furthermore, such as Figure 4 As shown, the vertical section 32 includes a first stepped portion 35 and a second stepped portion 36; the second stepped portion 36 is connected to the horizontal section 31; the thickness of the first stepped portion 35 is less than that of the second stepped portion 36; the first mounting hole 34 is located in the first stepped portion 35. The stepped structure of the vertical section 32 of the L-shaped connecting seat 3 ensures the structural rigidity at the connection with the horizontal section 31 through the second stepped portion 36 to resist machine tool vibration, and achieves local weight reduction through the thinner first stepped portion 35 and provides suitable installation space for the oblong hole. At the same time, the vertical adjustment margin of the oblong hole can accurately adjust the height of the mounting seat 4 and the sensor, ensuring stable detection spacing and avoiding component interference.

[0068] Specifically, the mounting base 4 is a block structure with a second connecting hole 41 and a second mounting hole 42. The mounting base 4 is connected to the connecting base 3 through the second connecting hole 41. The second mounting hole 42 is used to mount the eddy current sensor. The block mounting base 4 can achieve a stable connection with the connecting base 3 through the second connecting hole 41, while the second mounting hole 42 provides accurate mounting positioning for the eddy current sensor, ensuring stable sensor detection posture and providing a reliable carrier for distance data acquisition for chip detection.

[0069] Specifically, the modular support system consisting of the fixed base 2, connecting base 3, and mounting base 4 achieves a synergistic unity of installation adaptability, structural stability, and detection accuracy, providing comprehensive mechanical support for the reliable operation of the eddy current sensor. The fan-shaped plate-like fixed base 2 allows for initial fine-tuning of the installation position and angle through circumferential elongated holes 22, laying the foundation for the entire support to adapt to different machine tools. Its fan-shaped through holes 21 also achieve structural lightweighting and prevent dirt accumulation, ensuring long-term installation reliability. The L-shaped stepped connecting base 3, on the one hand, is firmly connected to the fixed base 2 through the horizontal section 31, and on the other hand, strengthens the overall rigidity to resist machine tool vibration through the integrated molding structure and the thick-walled design of the second step 36. On the one hand, the vertical waist-shaped hole of the first step 35 provides a precise height adjustment margin for the mounting base 4, making up for the adjustment limitations of the fixed base 2; the block mounting base 4 precisely connects with the connecting base 3 through the second connecting hole 41, and at the same time uses the second mounting hole 42 to provide a stable installation position for the eddy current sensor. The three form a hierarchical adjustment and stable connection system of basic positioning-height fine adjustment-sensor fixing, which not only realizes multi-scenario adaptation to different machine tools and different tool holder models, but also ensures that the eddy current sensor always maintains the optimal detection distance of 0.3~3mm, effectively avoiding the distortion of detection data caused by installation deviation, and laying a solid mechanical structure foundation for the high-precision realization of subsequent chip jam detection.

[0070] Example 2:

[0071] This embodiment provides a detection method for the machine tool holder chip jamming detection system described in Embodiment 1. The method consists of a calibration stage and a detection stage. The calibration stage is performed while the machine tool holder under test is idling, and specifically includes the following steps:

[0072] S101. The processing module calculates the number of sampling points N per cycle based on the real-time rotational speed n and the sampling frequency f using Formula 1. Formula 1 is as follows:

[0073] .

[0074] S102. Extract the first 2N sampling points from the collected raw data as the initial data segment, and remove the first and last 50 transient interference points to obtain the initial reference period.

[0075] S103. Standardize the reference period and then filter the effective periods to obtain the calibration period set.

[0076] S104. The median value of the sampling points corresponding to all calibration cycles is calculated using the median method, which serves as the dynamic baseline in the chip-free state. Then, the residual between each effective cycle and the dynamic baseline is calculated using the residual formula, and the calibration oscillation amount V0(k) is obtained from the peak-to-peak value of the residual.

[0077] S105. Based on the statistical characteristics of the calibrated runout, a threshold including 3 times the standard deviation is set, and the detection threshold V for chip detection is calculated using the threshold formula. t The threshold formula is:

[0078] .

[0079] σ is the mean of all oscillations during the calibration period. V0 This is the standard deviation of the calibrated periodic oscillation.

[0080] The testing phase is conducted under the actual machining conditions of the tool holder of the machine tool under test, such as... Figure 8 As shown, the steps include:

[0081] S1. The processing module obtains the real-time data transmitted by the transmission module.

[0082] S2. The processing module processes and periodically segments the real-time data to obtain a highly consistent set of effective periods.

[0083] S3. The processing module fits a smooth profile to each effective period using the RANSAC algorithm and calculates the detection residual to obtain the detection jump curve for each effective period.

[0084] S4. The processing module calculates the actual oscillation amount V1 (m) based on the detection bounce curve of each effective cycle.

[0085] When V1(m) > V t When V1(m) ≤ V, it is determined that there is debris. t The time was determined to be without debris; among them, V t This is the detection threshold.

[0086] By employing a coherent technical path—real-time data acquisition, periodic segmentation and purification, RANSAC fitting residual extraction, and oscillation threshold determination—high precision, strong adaptability, and high reliability of chip detection are achieved. The method first acquires real-time data such as spindle speed n, distance x(i), and acquisition frequency f. Combined with dynamic calculation of the number of sampling points per cycle, standardization processing, cross-correlation cycle positioning, and Pearson correlation coefficient screening, a highly consistent set of effective cycles is accurately segmented. This not only adapts to the spindle speed drift during machining through dynamic calculation of sampling points, but also eliminates interference from cutting fluid and temperature fluctuations through standardization, and finally performs dual correlation... By filtering out distorted cycles, the poor adaptability of existing detection methods—which are difficult to adapt to dynamic machining scenarios and are easily affected by interference—is addressed. Subsequently, the RANSAC algorithm is used to fit a smooth contour to each effective cycle. By calculating the detection residual, a detection runout curve accurately reflecting chip jamming anomalies is obtained. This effectively separates the normal structural contour of the tool holder from the local fluctuations caused by chip jamming, avoiding misjudgments and missed detections caused by the inability of traditional indirect detection methods (such as vibration and acoustic monitoring) to distinguish between normal fluctuations and chip jamming anomalies. This overcomes the core pain point of poor detection accuracy. Finally, the actual runout amount V1 (m) is calculated from the detection runout curve and compared with the detection threshold V. t The chip jamming detection is completed by direct comparison, and the entire process does not require manual intervention. It achieves real-time response and accurate detection of chip jamming during processing, which can not only prevent risks such as workpiece deviation and equipment damage caused by chip jamming in a timely manner, but also adapt to different standard tool holders such as BT and HSK, as well as scenarios that are prone to generating sticky chips, such as aluminum alloy processing, which significantly improves the quality stability of precision machining.

[0087] Specifically, step S2 includes:

[0088] S21. The processing module calculates the number of sampling points N per cycle based on real-time data using Formula 1; where Formula 1 is:

[0089] .

[0090] S22. The processing module performs standardization processing on the real-time data to obtain a standardized data set of all sampling points.

[0091] S23. The processing module extracts 2N sampling points from the beginning of the standardized data set, removes 50 transient interference points at the beginning and end, and obtains the initial reference period.

[0092] S24. The processing module performs cross-correlation calculations on the initial reference period to obtain the cross-correlation coefficient R(τ) when the delay step size is τ; and determines that when R(τ) ≥ 0.9, it records τ as the starting point of the period, obtaining the set of starting points {τ1, τ2, ..., τ...}. M}, where M is the number of candidate cycles.

[0093] S25. The processing module takes each located starting point as a reference and extracts a fixed length N of sampling points to ensure that the data length of each candidate period is consistent.

[0094] S26. The processing module filters out the valid periods from the intercepted candidate periods to obtain a set of valid periods.

[0095] By refining the design of the cycle segmentation process, the adaptability, anti-interference ability, and data reliability of chip detection are significantly improved, laying a solid foundation for subsequent accurate judgment. The method first dynamically calculates the number of sampling points N per cycle, which can adapt to the working conditions of spindle speed drift during machining in real time, avoiding cycle segmentation distortion caused by speed fluctuations, and solving the problem of poor adaptability of existing detection methods to dynamic machining scenarios. Then, standardization processing is used to eliminate data amplitude offset and gain differences caused by external interference such as cutting fluid splashing and temperature drift. Combined with the operation of extracting 2N sampling points from the beginning segment of standardized data and removing transient interference points at the beginning and end, the purity and representativeness of the initial reference cycle are ensured. Finally, cross-correlation calculation (R(τ)≥0.9) is used to accurately locate the start of the cycle. This method ensures precise synchronization between each candidate cycle and the tool holder rotation cycle. Then, candidate cycles are truncated by a fixed length N and distorted cycles are eliminated through Pearson correlation coefficient filtering, further refining the data to obtain a highly consistent set of effective cycles. This avoids the subjectivity and lag of manual cycle segmentation and reduces invalid data caused by interference from instantaneous vibrations and dust obstruction through double precise filtering. It solves the problems of inconsistent data quality and low cycle matching accuracy in traditional testing, providing high-quality data support for subsequent RANSAC fitting, residual calculation, and vibration determination. This significantly reduces the false positive and false negative rates of chip jamming detection, while also being compatible with different rotation speeds and various standard tool holders such as BT and HSK, further enhancing the industrial practicality and scenario adaptability of the testing method.

[0096] Specifically, step S22 includes:

[0097] S221. Collect real-time data in a fixed time window to obtain the original data set {x(1), x(2), ..., x(L)}; where L is the total number of sampling points within the time window.

[0098] S222. Transform the original dataset into a standardized dataset {x1(1), x1(2), ..., x1(L)} using Formula 2; Formula 2 is:

[0099] ;

[0100] Where x1(i) is the standardized data of the i-th sampling point, μ0 is the mean of all raw data within the fixed time window, and σ0 is the standard deviation of all raw data within the fixed time window.

[0101] A collaborative design that collects real-time data within a fixed time window and converts it using standardized formulas constitutes the core of precise data preprocessing, significantly improving the detection's anti-interference capability, data consistency, and the reliability of subsequent processing. Collecting real-time data within a fixed time window to obtain the raw dataset ensures both the continuity and timeliness of data acquisition, allowing for real-time data updates via window sliding to adapt to dynamic changes in the machining process, while avoiding redundant data and storage pressure from one-time collection. Based on this, the raw data is converted into a standardized dataset, effectively eliminating the influence of the mean μ0 and standard deviation σ0 of the raw data within the fixed time window, and completely eliminating data amplitude shifts and gain differences caused by external factors such as coolant splashing, temperature drift, and differences in tool holder material. The overall design ensures that subsequent operations such as cross-correlation cycle positioning and Pearson correlation coefficient screening are based on clean data of a uniform scale, significantly improving the accuracy of cycle segmentation and the reliability of effective cycle screening. It also enhances the adaptability of the detection method to different machining scenarios and different types of tool holders, avoiding the risk of misjudgment or missed judgment caused by interference or scale differences from the data source, providing crucial support for the high-precision implementation of the entire chip jamming detection process.

[0102] The cross-correlation calculation formula in step S24 is:

[0103] .

[0104] Where τ is the delay step size, L is the total number of sampling points within the fixed time window, x1(i) is the standardized real-time data of the i-th sampling point, x2(i+τ) is the number of the i+τ-th sampling points in the initial reference period after a delay of τ steps, μ1 is the mean of the standardized data within the fixed time window, and μ2 is the mean of the initial reference period.

[0105] Specifically, step S26 includes:

[0106] S261. Select the first candidate period as the reference period;

[0107] S262. The Pearson correlation coefficient r for the reference period is calculated using the coefficient formula. The coefficient formula is as follows:

[0108] .

[0109] Where r is the Pearson correlation coefficient between the m-th candidate period and the baseline period, m is the candidate period number, and x c (m, i) represents the standardized data of the i-th sampling point in the m-th candidate period, x b (i) represents the standardized data of the i-th sampling point in the reference period, μ m Let μ be the mean of the m-th candidate period. b The average value over the baseline period.

[0110] S263. Retain candidate periods with r ≥ 0.85 and remove distorted periods with r < 0.85 to obtain the effective period set.

[0111] By selecting the first candidate period as the baseline period, and combining the Pearson correlation coefficient calculation with a screening threshold of r≥0.85, the design achieves precise purification of candidate periods, significantly improving the consistency and reliability of the effective period set. Based on the initially screened candidate periods, the design accurately identifies and eliminates distorted periods caused by factors such as instantaneous vibration, temporary chip obstruction, and sensor signal fluctuations through correlation analysis, retaining only the effective periods that are highly correlated with the baseline period. This fundamentally solves the problem of inconsistent period data quality and abnormal data interfering with subsequent calculations in existing detection methods. The selected set of effective cycles possesses uniform rhythmic characteristics and data consistency, providing a high-quality data foundation for subsequent fitting of smooth contours, calculation of detection residuals, and extraction of actual runout using the RANSAC algorithm. This effectively avoids fitting deviations and runout calculation distortions caused by distorted data, significantly reducing the false positive and false negative rates of chip jam detection. Furthermore, this selection logic requires no manual intervention and adapts to dynamic working conditions such as spindle speed fluctuations and tool holder type differences during machining, further enhancing the automation and industrial applicability of the detection method and laying a solid data foundation for the accurate and efficient implementation of the entire chip jam detection process.

[0112] Specifically, in step S3, the RANSAC algorithm is used to fit each effective cycle to obtain a smooth profile of the normal structure of the machine tool holder. The detection residual for each effective cycle is calculated using the residual formula, which is:

[0113] .

[0114] Where δ1(m,i) is the detection residual of the ith sampling point in the m-th effective period, x p(m, i) represents the standardized data of the i-th sampling point in the m-th candidate period of the effective period, and x'(m, i) represents the RANSAC fitted value of the i-th sampling point in the m-th candidate period of the effective period. Leveraging the outlier resistance of the RANSAC algorithm, abnormal interference is removed from the standardized data of each effective period, and a smooth contour corresponding to the normal structure of the machine tool holder is fitted. Then, the standardized data of the i-th sampling point in the m-th effective period is restored to the actual distance value using the residual formula. Subtracting this from the corresponding fitted value directly quantifies the local positional deviation caused by chip jamming. The RANSAC algorithm effectively avoids the influence of interference factors such as instantaneous vibration and temporary chip adhesion on the contour fitting, ensuring that the smooth contour truly reflects the shape of the tool holder in a chip-free state. The residual formula, through data restoration and difference calculation, accurately transforms the minute displacement caused by chip jamming into quantifiable residual data. The resulting detection runout curve can intuitively highlight the location and impact of chip jamming, providing effective data support for the accurate calculation of actual runout. This significantly reduces the risk of misjudgment or missed judgment caused by data interference. At the same time, it is adapted to the dynamic rotation of the tool holder during machining, further improving the accuracy and reliability of the detection method and laying a solid core technical foundation for the accurate determination of chip jamming.

[0115] Specifically, in step S4, the abnormal fluctuation amplitude is quantified by the peak-to-peak value of the residual sequence to form the core feature parameter for chip detection. The detected oscillation amount for each effective cycle is calculated using the oscillation amount formula. The oscillation amount formula is:

[0116] .

[0117] `max(δ1(m,i))` represents the maximum value of the detection residual within the m-th effective cycle, and `min(δ1(m,i))` represents the minimum value of the detection residual within the m-th effective cycle. The actual oscillation amount is V1(m). The technical effects of this design are significant: on the one hand, peak-to-peak value calculation can comprehensively cover the maximum fluctuation range of the residual within a single cycle, accurately capturing abnormal tool holder runout caused by chip jamming, avoiding the shortcomings of single residual point data failing to reflect overall fluctuations and being susceptible to accidental interference, thus solving the problem of inaccurate quantification of abnormal amplitude in existing detection methods. On the other hand, it transforms the abstract residual sequence into a concrete oscillation amount value, simplifying chip jamming judgment to the oscillation amount and the detection threshold V1(m). t Direct comparison reduces the complexity of the judgment logic and improves the objectivity and consistency of the judgment, effectively avoiding the subjective errors of manual judgment. At the same time, this quantification method is adaptable to machining conditions with different speeds and tool holder types. Based on the effective cycle and accurate residual data after previous purification, it further ensures the accuracy of the runout calculation, providing an intuitive and reliable core basis for the rapid and accurate judgment of chip jamming, and greatly improving the practicality and industrial adaptability of the detection method.

[0118] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0119] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; 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; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0120] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that they are in indirect contact through an intermediate medium. Furthermore, "above," "over," or "on top" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," or "beneath" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0121] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0122] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A machine tool holder chip jamming detection system, characterized in that, include: Acquisition module, transmission module, and processing module; The acquisition module is connected to the transmission module, and it is used to acquire the rotational speed n and the distance x(i) between the machine tool tool holder and the machine tool tool holder during machining. The transmission module connects the acquisition module and the processing module. It is used to acquire real-time data, including the rotational speed n of the machine tool holder during machining, the acquisition frequency f of the acquisition module, and the distance x(i) between the acquisition module and the machine tool holder; and transmits the real-time data to the processing module. The processing module is connected to the machine tool. It is used to process and analyze real-time data to obtain the actual runout V1 (m) of the machine tool tool holder during machining, and to determine whether there is chip jamming based on the actual runout. If there is chip jamming, the processing module issues an alarm and controls the machine tool tool holder to stop machining. The acquisition module includes an eddy current sensor (1) and a support; The bracket is set on one side of the machine tool holder; the eddy current sensor (1) is set on the bracket; The eddy current sensor (1) is connected to the transmission module; The sampling frequency of the eddy current sensor (1) is the sampling frequency f, and the distance detected by the eddy current sensor (1) is x(i). The bracket includes a fixed base (2), a connecting base (3), and a mounting base (4); The fixed base (2) is fixedly installed on the machine tool, the mounting base (4) is set on the fixed base (2) through the connecting base (3), and the eddy current sensor (1) is set on the mounting base (4); The distance between the eddy current sensor (1) and the machine tool holder is 0.3~3mm; The fixed base (2) is in the shape of a fan-shaped plate, with a fan-shaped through hole (21) in the middle, and the central angle of the fan-shaped through hole (21) is 60°~90°; Two elongated holes (22) are opened circumferentially above the fan-shaped through hole (21), which are used to make fine adjustments to the installation position of the fixing seat (2); The length of the elongated hole (22) is 25~30mm and the width is 6~8mm.

2. The machine tool holder chip detection system as described in claim 1, characterized in that, The connecting seat (3) is L-shaped and has an integrally formed horizontal section (31) and a vertical section (32). The horizontal section (31) has a first connecting hole (33) for connecting the fixed base (2); the vertical section (32) has a first mounting hole (34) for setting the mounting base (4), the first mounting hole (34) being a waist-shaped hole extending vertically; The length of the waist-shaped hole is 15~20mm and the width is 5~6mm.

3. The machine tool holder chip detection system as described in claim 2, characterized in that, The vertical section (32) includes the first step (35) and the second step (36); The second step (36) is connected to the horizontal section (31). The thickness of the first step (35) is less than that of the second step (36); the first mounting hole (34) is located in the first step (35).

4. The machine tool holder chip detection system as described in claim 1, characterized in that, The mounting base (4) is a block structure, and it has a second connecting hole (41) and a second mounting hole (42). The mounting base (4) is connected to the mounting base (3) through the second connecting hole (41); the second mounting hole (42) is used to mount the eddy current sensor.

5. A detection method for a machine tool holder chip jamming detection system as described in any one of claims 1-4, characterized in that, Including the following steps: S1. The processing module obtains the real-time data transmitted by the transmission module; S2. The processing module processes and periodically segments the real-time data to obtain a highly consistent set of effective periods. S3. The processing module fits a smooth profile for each effective period using the RANSAC algorithm and calculates the detection residual to obtain the detection jump curve for each effective period. S4. The processing module calculates the actual oscillation amount V1 (m) based on the detection bounce curve of each effective cycle. When V1(m) > V t When V1(m) ≤ V, it is determined that there is debris. t It was determined that there were no chips stuck in the casing. Among them, V t This is the detection threshold.

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