Intelligent temperature measurement cutter and temperature field reconstruction method

Abstract

The invention relates to the field of cutting tool temperature measurement, in particular to an intelligent temperature measurement tool and a tool rake face temperature field reconstruction method based on inverse heat conduction. The device comprises a blade, a blade surface groove, a superfine thermocouple, a PCB (Printed Circuit Board), a battery and a data display device which are arranged on a knife handle main body, the blade is connected with the knife handle body through the blade installation screw, and the blade surface groove is formed in the front knife face of the blade. The invention provides a tool rake face temperature field reconstruction method based on inverse heat conduction in order to solve the technical problem of tool temperature measurement in the turning process. By integrating the advanced temperature measurement technology and mathematical modeling, temperature monitoring can be achieved in the complex machining environment, and the temperature dynamic change of the contact area of the tool and the workpiece is effectively captured.

Description

Technical Field

[0001] The present invention relates to the field of temperature measurement of cutting tools, and in particular to an intelligent temperature measurement tool and a method for reconstructing the temperature field of the tool rake face based on inverse heat conduction. Background Art

[0002] During the cutting process, metal deformation is highly concentrated in a narrow area near the cutting edge of the tool's rake face. This area typically experiences the highest temperatures. Accurately measuring the temperature in this area is crucial to meet the stringent requirements of modern manufacturing. Measuring and predicting the temperature in the cutting zone is extremely challenging due to the narrow and elongated shear zone and the contact and motion between the tool and chip. High temperatures significantly impact tool wear, workpiece machining quality, and cutting stability, and cause thermal deformation of the tool, a significant source of error in the machining process. Current temperature measurement methods can achieve in-situ temperature measurements at limited points within the tool-chip contact zone and temperatures outside this zone. However, obtaining the critical temperature field and peak temperature in the tool-chip contact zone is difficult. Commonly used measurement methods include non-contact measurement and manual thermocouples. Non-contact measurement relies heavily on environmental parameters, resulting in significant uncertainty and subjectivity in the measured temperature. Manual thermocouple temperature measurement requires multiple drillings into the workpiece, resulting in a single measurement point and a lack of continuous monitoring of transient cutting temperatures. This poses a significant challenge to intelligent machining processes. Therefore, a method for intelligent temperature measurement and reconstruction of the tool and rake face temperature field is urgently needed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for reconstructing the temperature field of a tool and a rake face of a tool based on a thermocouple to accurately obtain the temperature distribution in the cutting area.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention includes a blade arranged on a handle body, a blade surface groove, an ultrafine thermocouple, a PCB circuit board, a battery, and a data display device; the blade is connected to the handle body via a blade mounting screw, the blade surface groove is arranged on the blade rake face; and the ultrafine thermocouple is arranged in the blade surface groove. The intelligent temperature measurement tool proposed in the present invention is used to implement a tool rake face temperature field reconstruction method based on inverse heat conduction. The steps are as follows: ① Establish a heat transfer analytical model for point heat sources; A point heat source P(x1, y1, z1) emits heat instantaneously in an infinite medium. After a time τ, the temperature rise at any point M(x, y, z) is θ. Using Fourier transform to solve the solid heat conduction differential equation, we obtain the analytical expression for the temperature field represented by the instantaneous heat generation Q of the point heat source, the specific heat capacity c of the medium, the density ρ of the medium, and the thermal diffusivity α of the medium: A point heat source P(x1, y1, z1) continuously generates heat in an infinite medium. At the observation time t, the temperature rise at any point M(x, y, z) is θ, which can be expressed as follows using the heat intensity q of the point heat source and the thermal conductivity λ of the medium: ②Establish a heat source and heat transfer model for the blade rake face; 201 The contact surface between the chip formed by turning and the rake face of the insert is regarded as the surface heat source area causing the temperature rise of the rake face, where the tool nose angle g, tool nose arc r, cutting depth a p =y1+r, tool-chip contact length L=x2; the horizontal coordinate of the point of intersection between the tool tip arc segment and the secondary cutting edge is: 202 The surface heat source is discretized into a static, finite-length, continuous heating line heat source perpendicular to the tool-chip contact direction along the tool-chip contact direction; at time τ i The horizontal coordinates are x i and x j The point heat source on the line heat source passes through dτ i After a certain amount of heat is emitted, after a period of time τ, the temperature rise dθ at any point (x, y) at the observation time t is caused i and dθ j Respectively expressed as: 203 respectively for y i and y j The horizontal coordinates are x i and x j The temperature rise caused by the line heat source θ i and θ j : 204 Let time τ i At 0≤x≤x1, the temperature rise caused by the surface heat source is θ1, and the temperature rise caused by the surface heat source x1<x≤x2 is θ2, so dθ1=θ i ,dθ2=θ j ; The heat source on the entire surface 205 continues to generate heat until the observation time t. The temperature rise of the point (x, y) on the rake surface of the blade is θ, then: Where q is the heat flux density, θ0 is the initial temperature of the point (x, y); ③ The heat source method and particle swarm algorithm are combined to inversely calculate the heat source intensity and solve the temperature field of the tool-chip contact area near the tool tip on the blade rake face. 301 uses a particle swarm optimization algorithm combined with a heat source method to solve the temperature field of the blade rake face. The two-dimensional unsteady-state heat conduction inverse problem is transformed into an optimization problem for solution. The particle velocity and position are updated according to the following formula: Among them, v i,k is the velocity of the i-th particle in the k-th iteration; x i,k is the position of the i-th particle in the k-th iteration; p i,best is the historical optimal position of the i-th particle; g best is the historical optimal position of the entire particle population; ω is the inertia weight; α is the constraint factor; c1 is the cognitive factor; c2 is the social factor; r1, r2 are random numbers between [0, 1]; The 302 heat source intensity is expressed in a power series. The position of the particle represents the parameter in the heat source intensity expression. Each particle position corresponds to a heat source intensity expression, which is used to predict the temperature curve of the temperature measurement point. The criterion for judging the quality of the particle position is the sum of the squared errors between the predicted temperature and the measured temperature, which is called the fitness p. i ; Where M is the total number of time steps; T m For the mth time step, measure the temperature; For the mth time step, the i-th particle, predict the temperature; The optimization goal of the 303 particle swarm algorithm is to find a heat flux expression that minimizes the error between the predicted temperature and the measured temperature; the measured temperature is regarded as the predicted temperature, so that the error is zero, and the heat flux is represented by the position of the particles; Among them, x i,k,j+1 is the j+1th dimension of the position of the i-th particle at the k-th iteration; j max K is the highest power of temperature measured by power series fitting; j is the magnitude of the jth coefficient of the measured temperature using a power series fit; 304 In the first iteration, the j+1th dimension of the position of the i+1th particle is determined by the j+1th dimension of the i-th particle, and the sizes of the dimensions of the first particle are random numbers between -1 and 1; xi+1,1,j+1 =cos(4cos -1 (x i,1,j+1 )),0<j<j max (eleven); The inertia weight update method adopted by 305 is as follows: Among them, ω start is the velocity of the i-th particle in the k-th iteration; ω end is the position of the i-th particle in the k-th iteration; k is the number of current iterations; k max Maximum number of iterations. The end of the ultra-fine thermocouple is set at 1mm from the tip of the knife 2 The data display device and the battery are both arranged on one side of the PCB circuit board. The ultra-fine thermocouple has a cross-sectional size of 0.1 mm×0.2 mm, a spherical end, and a diameter of 0.25 mm.

[0005] The present invention has the following positive effects: Addressing the technical challenges of tool temperature measurement during turning, the present invention proposes a method for reconstructing the tool rake face temperature field based on inverse heat conduction. By integrating advanced temperature measurement technology and mathematical modeling, the present invention enables temperature monitoring in complex machining environments, effectively capturing the dynamic temperature changes in the tool-workpiece contact area. The reconstructed temperature field provides important data support for optimizing cutting parameters and real-time monitoring of the machining process, significantly improving machining accuracy and stability. This method not only identifies the thermal load on the tool during machining but also enables timely adjustment of the cutting strategy to avoid tool wear or failure due to overheating, thereby extending the tool's service life. By optimizing the tool temperature distribution, the present invention further improves workpiece machining quality and reduces machining errors caused by thermal deformation or material burns, making it particularly suitable for applications requiring high surface quality and precision. Furthermore, while achieving efficient temperature monitoring, the present invention enhances the adaptability and intelligence of intelligent manufacturing systems. Its application scope covers a wide range of materials, tools, and machining conditions, and is widely used in high-end manufacturing fields such as aviation, automotive, and mold manufacturing. It not only reduces production costs for enterprises but also provides new research directions and application value for the development of advanced manufacturing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work. Figure 1 This is a schematic diagram of the temperature measuring turning tool structure of the present invention; Figure 2 This is a schematic diagram of embedding an ultra-fine thermocouple into a temperature-measuring turning tool according to the present invention; Figure 3 is the coordinate of the temperature field on the rake face; Figure 4 Temperature curve obtained for the turning temperature measurement system; Figure 5 The rake face is 1mm 2 Reconstruct the temperature field. In the figure: 1 handle body, 2 blade, 201 blade surface groove, 3 ultra-fine thermocouple, 4 PCB circuit board, 5 battery, 6 data display device. DETAILED DESCRIPTION The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without creative work are within the scope of protection of the present invention. Establishment of the turning temperature measuring device characterizing the present invention: like Figure 1-2 As shown, the turning temperature measurement device consists of a toolholder body 1, a blade 2, a blade surface groove 201, an ultrafine thermocouple 3, a PCB circuit board 4, a battery 5, and a data display device 6. The prototype structure of the blade 2 is APKT1235. The blade 2 is mounted on the toolholder body 1 via blade mounting screws; the blade surface groove 201 is positioned near the blade tip; the ultrafine thermocouple 3 is bonded to the blade surface groove 201 with a high-temperature-resistant adhesive, with its end temperature measurement point positioned within a 1mm*1mm radius of the blade tip; the ultrafine thermocouple 3 terminals are connected to the PCB circuit board 4, which is in turn connected to the data display device 6; and the battery 5 is connected to the PCB circuit board 4 to power the circuit board. Implementation method of the temperature field reconstruction method of the present invention: The temperature field is reconstructed by combining the temperature data obtained by the turning temperature measurement system with the heat transfer model of the blade 2. The steps are as follows: ① Establish a heat transfer analytical model for point heat sources; When a point heat source P(x1, y1, z1) emits a certain amount of heat instantaneously in an infinite medium, the temperature rise at any point M(x, y, z) after a time τ is θ. Solving the solid heat conduction differential equation using Fourier transform, we can obtain the analytical expression of the temperature field represented by the instantaneous heat generation Q of the point heat source, the specific heat capacity c of the medium, the density ρ of the medium, and the thermal diffusivity α of the medium: 102 It is then obtained that a point heat source P(x1, y1, z1) continuously generates heat in an infinite medium. At the observation time t, the temperature rise of any point M(x, y, z) is θ, which can be expressed using the heating intensity q of the point heat source and the thermal conductivity λ of the medium as follows: ② Establish a heat source and heat transfer model for the rake face of blade 2; 201 Figure 3 The temperature field coordinates shown in the figure are used to consider the contact surface between the chip formed by turning and the rake face of the insert 2 as the surface heat source area causing the temperature rise of the rake face. The tool tip angle is 93°, the tool tip arc r = 0.2 mm, and the cutting depth a p =y1+r, tool-chip contact length L=x2; the horizontal coordinate of the point of intersection between the tool tip arc segment and the secondary cutting edge is: 202 The surface heat source is discretized into countless static, finite-length, continuous heating line heat sources perpendicular to the tool-chip contact direction. i The horizontal coordinates are x i and x j The point heat source on the line heat source passes through dτ i After a certain amount of heat is emitted, after a period of time τ, the temperature rise dθ at any point (x, y) at the observation time t is caused i and dθ j Respectively expressed as: 203 respectively for y i and y j The horizontal coordinates are x i and x j The temperature rise caused by the line heat source θ i and θ j : 204 Let time τ i At 0≤x≤x1, the temperature rise caused by the surface heat source is θ1, and the temperature rise caused by the surface heat source x1<x≤x2 is θ2, so dθ1=θ i ,dθ2=θ j ; The heat source on the entire surface 205 continues to generate heat, causing the temperature rise of the point (x, y) on the rake surface of the blade 2 at the observation time t to be θ, then: Where q is the heat flux density, θ0 is the initial temperature of the point (x, y), and once the intensity of the heat source is known, the temperature field can be derived; ② Combine the heat source method and particle swarm optimization to inversely calculate the heat source intensity; 301 uses a particle swarm optimization algorithm combined with a heat source method to solve the temperature field of the rake face of blade 2, transforming the two-dimensional unsteady-state heat conduction inverse problem into an optimization problem for solution; the particle velocity and position can be updated according to the following formula: Among them, v i,k is the velocity of the i-th particle in the k-th iteration; x i,k is the position of the i-th particle in the k-th iteration; p i,best is the historical optimal position of the i-th particle; g best is the historical optimal position of the entire particle population; ω is the inertia weight; α is the constraint factor; c1 is the cognitive factor; c2 is the social factor; r1, r2 are random numbers between [0, 1]; The 302 heat source intensity is expressed in a power series. The particle position represents the parameters in the heat source intensity expression. Each particle position corresponds to a heat source intensity expression. These expressions can be substituted into the temperature field solution formula to predict the temperature curve of the temperature measurement point. The criterion for judging the quality of the particle position is the sum of the squared errors between the predicted temperature and the measured temperature, which is called the fitness p. i ; Where M is the total number of time steps; T m For the mth time step, measure the temperature; For the mth time step, the i-th particle, predict the temperature; The optimization goal of the particle swarm algorithm is to find a heat flux density expression that minimizes the error between the predicted temperature and the measured temperature. The measured temperature can be regarded as the predicted temperature, so that the error is zero. The heat flux density can be represented by the position of the particles. Among them, x i,k,j+1 is the j+1th dimension of the position of the i-th particle at the k-th iteration; j max K is the highest power of temperature measured by power series fitting; j is the magnitude of the jth coefficient of the measured temperature fitted by the power series; 304 In the first iteration, the j+1th dimension of the position of the i+1th particle is determined by the j+1th dimension of the i-th particle, and the sizes of each dimension of the 1st particle are random numbers between -1 and 1; x i+1,1,j+1 =cos(4cos -1 (x i,1,j+1 )),0<j<j max (eleven); The 305 algorithm uses a larger inertia weight in the early stages of the iteration to maintain the strength of the global search, and a smaller inertia weight in the later stages of the iteration to promote more accurate local search. The inertia weight update method used is as follows: Among them, ω start is the velocity of the i-th particle in the k-th iteration; ω end is the position of the i-th particle in the k-th iteration; k is the number of current iterations; k max Maximum number of iterations. Use Figure 4 The temperature curve obtained is as shown in the following example. Figure 5 The front cutting edge of the blade 2 shown is 1mm 2 Temperature field. The above-described embodiments are only preferred embodiments of the present invention and are not exhaustive of all feasible implementations of the present invention. For those skilled in the art, any obvious modifications made thereto without departing from the principles and spirit of the present invention should be considered to be included within the scope of protection of the claims of the present invention.

Claims

1. An intelligent temperature measuring tool, characterized in that It includes: A blade (2), a blade surface groove (201), an ultrafine thermocouple (3), a PCB circuit board (4), a battery (5), and a data display device (6) are arranged on a handle body (1); the blade (2) is connected to the handle body (1) via a blade mounting screw, the blade surface groove (201) is arranged on the front cutting surface of the blade (2); and the ultrafine thermocouple (3) is arranged in the blade surface groove (201).

2. A method for reconstructing the temperature field of the tool rake face based on inverse heat conduction is implemented using the intelligent temperature measuring tool according to claim 1, characterized in that Here are the steps: ① Establish a heat transfer analytical model for point heat sources; 101 A point heat source P (x1, y1, z1) emits heat instantaneously in an infinite medium. After a time τ, the temperature rise of any point M (x, y, z) is θ. The Fourier transform is used to solve the solid heat conduction differential equation, and the analytical expression of the temperature field represented by the instantaneous heat generation Q of the point heat source, the specific heat capacity c of the medium, the density ρ of the medium and the thermal diffusion coefficient α of the medium is obtained as follows: 102 A point heat source P(x1,y1,z1) continuously generates heat in an infinite medium. At the observation time t, the temperature rise of any point M(x,y,z) is θ, which can be expressed by the heating intensity q of the point heat source and the thermal conductivity λ of the medium: ② Establish a heat source heat transfer model of the front cutting edge of the blade (2); The contact surface between the chip formed by turning and the front cutting edge of the insert (2) is regarded as the surface heat source area causing the temperature rise of the front cutting edge, where the tool tip angle g, tool tip arc r, cutting depth a p =y1+r, tool-chip contact length L=x2; the horizontal coordinate of the point of intersection between the tool tip arc segment and the secondary cutting edge is: 202 The surface heat source is discretized into a static finite-length continuous heating line heat source perpendicular to the chip contact direction along the chip contact direction; at time τ i The horizontal coordinates are x i and x j The point heat source on the line heat source passes through dτ i After a certain amount of heat is emitted, after a period of time τ, the temperature rise dθ of any point (x, y) at the observation time t is caused i and dθ j Respectively expressed as: 203 respectively for y i and j The integration gives the horizontal coordinates of x i and x j The temperature rise caused by the line heat source θ i and θ j : 204 Let time τ i At the point where 0≤x≤x1, the temperature rise caused by the surface heat source is θ1, and the temperature rise caused by the surface heat source x1<x≤x2 is θ2, so dθ1=θ i ,dθ2=θ j ; The heat source on the entire surface 205 continues to generate heat until the observation time t. The temperature rise of the point (x, y) on the front cutting surface of the blade (2) is θ, then: Where q is the heat flux, θ0 is the initial temperature of the point (x, y); ③ Combining the heat source method and particle swarm algorithm to inversely calculate the heat source intensity, the temperature field of the cutting edge contact area near the cutting tip of the insert (2) is solved.

3. The temperature field reconstruction method according to claim 2, characterized in that: The step ③ comprises: 301 The particle swarm algorithm is combined with the heat source method for solving the temperature field of the front cutting edge of the blade (2) to transform the two-dimensional unsteady-state heat conduction inverse problem into an optimization problem for solution. The particle velocity and position are updated according to the following formula: Among them, v i,k is the velocity of the i-th particle in the k-th iteration; x i,k is the position of the i-th particle at the k-th iteration; p i,best is the historical optimal position of the ith particle; g best is the historical optimal position of the entire particle population; ω is the inertia weight; α is the constraint factor; c1 is the cognitive factor; c2 is the social factor; r1, r2 are random numbers between [0,1]; 302 The heat source intensity is expressed in power series. The position of the particle represents the parameter in the heat source intensity expression. Each particle position corresponds to a heat source intensity expression, which is used to predict the temperature curve of the temperature measurement point. The criterion for judging the quality of the particle position is the sum of the square errors between the predicted temperature and the measured temperature, which is called the fitness p i ; Where M is the total number of time steps; T m For the mth time step, measure the temperature; For the mth time step, the ith particle, predict the temperature; The optimization goal of the particle swarm algorithm is to find a heat flux expression that minimizes the error between the predicted temperature and the measured temperature; the measured temperature is regarded as the predicted temperature, so that the error is zero, and the heat flux is represented by the position of the particles; Among them, x i,k,j+1 is the j+1th dimension of the position of the ith particle at the kth iteration; max K is the highest order of temperature measured using power series fitting; j is the order of magnitude of the jth coefficient of the measured temperature fitted by the power series; 304 In the first iteration, the j+1th dimension of the position of the i+1th particle is determined by the j+1th dimension of the i-th particle, while the sizes of each dimension of the 1st particle are random numbers between -1 and 1; x i+1,1,j+1 = cos(4cos -1 (x i,1,j+1 )),0<j<j max (eleven); The inertia weight update method adopted by 305 is as follows: Among them, ω start is the velocity of the i-th particle in the k-th iteration; ω end is the position of the i-th particle in the k-th iteration; k is the current iteration number; k max Maximum number of iterations.

4. The intelligent temperature measuring tool according to claim 1, characterized in that: The end of the ultra-fine thermocouple (3) is arranged 1 mm from the tip of the knife. 2 The data display device (6) and the battery (5) are both arranged on one side of the PCB circuit board (4).

5. The intelligent temperature measuring tool according to claim 1, characterized in that: The ultra-fine thermocouple (3) has a cross-sectional size of 0.1 mm×0.2 mm, and a spherical end with a diameter of 0.25 mm.

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