Temperature control research for laser-induction hybrid strengthening process

ABSTRACT

A comprehensive control methodology has been developed to address challenges such as temperature fluctuations, inconsistent depth penetration, and compositional integrity variations during laser-induction hybrid strengthening performed with consistent power parameters. This approach synergistically integrates a fuzzy controller and a decoupling compensator. In the initial part of this study, a temperature field model was utilized to investigate the effects of different process factors on temperature profiles. The analysis of step response data facilitated the development of mathematical models that delineate the correlation between the input factors and output responses. Subsequently, a decoupling compensation technique was used to reduce the coupling effects between temperature and its rate of change. A fuzzy controller was then developed to reduce the system's reliance on mathematical models, hence improving the accuracy of the control strategy. The results indicate that the application of fuzzy PI control and the implementation of decoupling compensation, the control strategy successfully improves the performance of the controller by minimizing parameter interactions. This strategy also incorporates real-time adjustment of PI parameters. Compared to conventional PI control, this method offers improved response speed and reduced overshoot. The laser-induction hybrid strengthening technique ensures superior temperature control, maintaining steady-state temperature precision within a 1% tolerance.

KEYWORDS:

• Laser-induction hybrid strengthening
• temperature evolution process
• multivariable decoupling
• fuzzy PI control

Introduction

As laser processing technology and automation advance rapidly, the high-end equipment manufacturing industry continually raises its quality standards for processing. Consequently, precise control over the laser processing procedure is essential for enhancing the overall quality of laser treatments. Laser-induction hybrid strengthening technology, a novel surface modification technique, utilizes electromagnetic induction and laser energy vectors as separate heat sources [1,2]. This method addresses the inherent shortcomings in laser quenching hardening, such as the non-uniform distribution of strengthened layer microstructures and pronounced hardness gradients within the transition zone. As a result, materials gain specific application value and improved service properties, demonstrating significant potential across various sectors including aerospace, automotive equipment, metallurgical machinery, and marine vessels. The operational mechanism of this technology involves using a laser beam to treat the surface of the workpiece, whereupon the material's surface quickly reaches temperatures exceeding the austenite transformation temperature. During the cooling process, by regulating the isothermal phase transformation temperature and duration, the induction heat source influences the composite phase transformation process as the material self-cools to a target temperature. This leads to the formation of a composite microstructure within the reinforced area [3–5], thereby maintaining specific surface hardness while significantly enhancing wear resistance, toughness, and contact fatigue performance [6–8].

The laser-induction hybrid strengthening process is affected by several process factors, including heat source power, scanning speed, and spot size. These factors not only affect the temperature but are also susceptible to external environmental disturbances. The presence of two heat sources further complicates the temperature distribution throughout the composite strengthening procedure. The laser-induction hybrid strengthening technology controls the temperature and duration of the phase transformation of the supercooled austenite, significantly impacting the temperature distribution and the quality of the strengthened layer [9–12]. This control is crucial as it directly influences the final properties of the material, ensuring optimal performance in its intended application.

Temperature instability in the laser-affected zone can cause variations in the depth of phase transformation hardening, and might even prevent achieving the phase transformation temperature or cause surface melting, potentially damaging the surface of the strengthened layer. Similarly, temperature instability during the induction heating phase can lead to microstructural irregularities following isothermal transformation, thus affecting the hardness and durability of the strengthened layer. Maintaining a stable temperature distribution is therefore crucial in the laser-induction hybrid strengthening process, as it directly impacts the quality of the strengthened layer.

Recent advancements have been made in process control for individual laser processing [13–15]. However, studies on temperature regulation in laser-induction hybrid strengthening are relatively scarce, often focusing solely on controlling a single temperature value. Diagne et al. [16] utilized a model reference adaptive control approach to adjust laser power and scanning speed, thereby achieving control over melt pool size and cooling rate. Li et al. [17,18] applied an induction-assisted laser dispersed quenching method for rail steel strengthening, manipulating the induction temperature to regulate the distribution of microstructures within the fortified layer. This method effectively addressed concerns related to reduced material ductility and delamination, common issues in employing single laser dispersed quenching methods for rail steel.

Due to the complexity involved in establishing a mathematical model for the induction heat source, the model predictive control method is deemed unsuitable; furthermore, the neural network control method requires extensive experimental data for parameter adjustment. Zhang et al. [19] proposed a method based on a fuzzy decoupling strategy that not only decouples temperature control couplings but also effectively enhances the stability and accuracy of the control system, thereby improving system robustness.

This article introduces a temperature control technique using fuzzy-decoupled PI control. The decoupling compensator effectively mitigates the impact of system coupling effects during multi-parameter adjustments. The fuzzy controller dynamically adjusts the gain parameters of the proportional and integral components during the temperature control process, allowing for precise and highly responsive control of the laser-induction hybrid strengthening process, thereby enhancing the overall performance and efficiency of the process to ensure optimal results.

Experimental and mathematical modeling

Experimental and material

The choice of material for simulation and experiments in this paper is U75V pearlitic steel in a hot-rolled state The chemical composition of U75V steel is presented in Table 1. This selection is particularly relevant given the application of the laser-induction hybrid strengthening technique for rail peening. U75V pearlitic steel, known for its pearlite microstructure, is commonly employed in rail manufacturing due to its favorable mechanical properties, such as strength and wear resistance. The hot-rolled state represents a typical condition encountered during rail production and usage.

Table 1. Chemical compositions of U75V steel (wt.%).

Elements	C	Mn	Si	P	S	V	Fe
Content	0.75–0.81	0.70–1.05	0.50–0.80	≤0.030	≤0.030	0.04–0.12	Bal.

In this study, a 6 kW fiber-coupled diode laser (Laserline LDF 6000-40, with a wavelength range of 940–1060 nm) is employed as the laser system, featuring a laser spot size adjustable from 15 to 50 mm. To fulfill the heating requirements of the composite strengthening process, an ultrasonic induction heating device has been incorporated. This device's induction coil, made of copper tubing and a magnetic core, can deliver up to 80 kW of power with a frequency adjustable between 3 and 40 kHz. Temperature monitoring is conducted using an infrared temperature sensor that utilizes temperature as a feedback signal, capable of measuring temperatures between 300 and 1800°C. To reduce the impact of laser radiation on the infrared sensor, a filter has been installed to exclude wavelengths within the range emitted by the laser.

Laser-induction hybrid strengthening is a multifaceted enhancement process involving the concurrent interaction of different energy sources. The laser heat source directly affects the workpiece's surface, inducing rapid heating and generating significant temperature gradients in the depth direction perpendicular to the surface. As the material's inherent heat diffusion takes effect, the workpiece experiences rapid cooling, leading to a martensitic phase transformation at the material's surface.

Following the initial heat treatment with the laser, secondary heating via an induction source is utilized to sustain the temperature above the austenite-to-pearlite transformation threshold. This method enables partial transformation of the microstructure into pearlite. To convert any remaining austenite into a martensitic structure, forced refrigeration is applied. Consequently, the surface of the workpiece develops a complex microstructure composed of both martensite and pearlite.

The temperature modulation during laser irradiation plays a crucial role in determining the conformance of the material to phase transformation requisites while avoiding surface melting. The temperature during induction heating significantly affects the type of microstructure that forms during the subsequent isothermal transformation process. Throughout the hybrid strengthening process, the thermal diffusion inherent to the material constitutes the primary mechanism for surface cooling. According to temperature transfer theory, the cooling rate, when maintaining surface temperature stability, serves as an indicative parameter reflecting the depth of the strengthened layer [20].

The temperature system, which is the controlled variable in this scenario, operates on the material surface during the composite strengthening process. This system's outputs include the temperatures in the laser-impacted area, the induction-influenced area, and the corresponding cooling rates. Inputs to this system consist of laser power, induction power, and scanning speed. Temperature sensors are utilized to collect temperature readings from various points on the material surface. The control system uses this temperature feedback to regulate the power of the laser and induction equipment, as well as the scanning speed of the machine tool, ensuring precise management of the composite strengthening process. The described process is depicted in Figure 1.

Figure 1. Laser-induction hybrid strengthening temperature variation process chart: (a) schematic diagram of hybrid strengthening and (b) temperature field of hybrid strengthening.

Effect of process parameters on temperature

The pivotal factors governing the laser-induction hybrid strengthening process include laser power, induction power, scanning speed, and the spatial relationship between various heat sources. An analysis employing COMSOL Multiphysics 6.1 software is conducted to assess the influence of these process parameters on temperature fluctuations. The investigation of the temperature field within the laser-induction hybrid strengthening process is conducted using the transient heat transfer, primarily relying on the Fourier heat conduction equation [21].

The application of the laser heat source to the processing surface is achieved through boundary heat flux input, ensuring a uniform distribution of energy primarily concentrated within the spot size of the laser equipment. The foundation for electromagnetic induction heating is established by the principles of Joule's law and electromagnetic induction. At a specified frequency, an induced current flows through the induction coil, creating an alternating magnetic field at the same frequency both inside and outside the coil. The metal material being processed reacts to this alternating magnetic field by generating induced currents on its surface, which are also at the same frequency as the magnetic field. The direction of these induced currents is opposite to that of the coil's current, forming a closed loop on the surface. This process converts the electrical energy into thermal energy, effectively heating the metal surface.

To emulate real-world conditions of composite fortification, the simulation establishes the initial temperature of the workpiece and the surrounding environment are both established at 20°C. A radiation coefficient of 0.8 is considered to account for the surface radiation of the workpiece toward the environment. The workpiece dimensions are 15 mm by 50 mm. The temperature field contour map is obtained, as shown in Figure 2, where Figure 2 is the temperature field contour map of the workpiece surface at different time nodes within 52 s.

Figure 2. Evolution of temperature field in laser-induced hybrid strengthening process (a) 0.1 s, (b) 4 s, (c) 8 s, (d) 12 s, (e) 20 s, (f) 28 s, (g) 36 s, (h) 44 s, and (i) 52 s.

While maintaining all other process parameters constant, adjustments were made to specific parameters, resulting in the generation of temperature history curves as illustrated.

Analysis of the temperature curve in Figure 3 indicates that an increase in the output power of the induction heat source leads to a rise in temperature within the induction zone. This change in induction power, however, does not affect the temperature during the laser heating stage or the cooling rate in the natural cooling zone. This absence of correlation can be attributed to the separate pathways of the laser and induction heat sources, which dictate their temporal effects independently. Following laser application, the temperature peaks and then begins to decline. In this phase, the temperature gradient in the depth direction is significantly greater than that on the surface. Similarly, the area for heat dissipation is more extensive in the depth direction compared to the processing direction. During the cooling process, the predominant direction of heat dissipation is in the depth direction, minimizing the influence of the induction heat source on both the laser temperature and the cooling speed. Therefore, in the control process, adjustments to the induction power are typically disregarded with respect to their impact on the laser's earlier effects. Changes in laser power directly affect the overall temperature of the processed surface, with decreases in laser power leading to an overall temperature reduction. This is primarily due to the laser's initial interaction with the surface of the specimen, with subsequent temperature reductions stemming from cooling within the laser-affected region. Although alterations in heat source spacing do not affect the temperature in the laser intensification region, they do influence the initial temperature when the induction heat source is added. Adjusting the scanning speed also produces different temperature curves, with a decrease in scanning speed resulting in increased combined energy input from both the induction and laser heat sources, thus leading to an overall temperature increase.

Figure 3. Temperature variation process of hybrid strengthening under different process parameters: (a) laser power, (b) heat source spacing, (c) inductive power and (d) scanning speed.

Maintaining a consistent temperature across both the laser irradiation and induction heating phases is critical for effectively regulating the temperature during the laser-induction hybrid reinforcement process. Research indicates that the depth of the laser quenching reinforcing layer decreases as the scanning speed increases in a temperature-controlled setting. Therefore, manipulating the scanning velocity in temperature-controlled mode is crucial for modifying the temperature gradient during the reinforcement process. Consequently, when adjusting the scanning speed in temperature-controlled mode, temperature variation curves under different cooling rates are plotted, as shown in Figure 4. The graph illustrates that in temperature-controlled mode, alterations in scanning speed impact both the heating rate and cooling rate of the workpiece.

Figure 4. Temperature history of hybrid strengthening at different scanning speeds under temperature control mode.

To verify the reliability of the surface temperature field within the model, a method of validating both the laser temperature curve and the induction temperature curve was employed. An infrared temperature measuring equipment was used to collect temperatures at the center of the specimen, ensuring that the laser power corresponded with the power used in the model. The actual measurements showed a relative error of 10.2% compared to the simulation results, as shown in Figure 5. Due to the obstruction of the infrared thermometer by the induction head, thermocouple temperature measurement was used to monitor temperature changes and then validate the surface temperature field under induction heating. The output power of the induction matched the power used in the simulations, and the recorded temperature data were compared with the simulation results, showing a relative error of 8.9% under the induction heat source. These results indicate that the model can accurately calculate the temperature distribution of the specimen under a moving heat source.

Figure 5. Temperature comparison between experimental and simulated results: (a) laser heat source and (b) induction heat source.

Temperature field models

Given that adjustments to induction power do not significantly affect the temperature or cooling rate in the laser zone, this study primarily focuses on the direct impact of induction output power on the temperature within the induction-affected area. To this end, the fuzzy PI control method is employed to analyze the temperature system as a linear system. Utilizing a fuzzy controller minimizes reliance on the mathematical model of the control system, thereby improving control precision. This approach facilitates more adaptive and responsive adjustments to the system's behavior in real time, enhancing the overall effectiveness of the temperature management during the process.

In this study, the step response method is employed to calculate the transfer functions between various input and output variables. On the strengthening platform, a laser power of 6000 W and a scanning speed of 3 mm/s are selected for workpiece surface processing. Temperature measuring devices are used to detect the temperature in the laser-affected region, and a temperature response curve for this region is constructed. Temperature response curves under different laser output powers are simulated using COMSOL Multiphysics 6.1 software, as shown in Figure 6.

Figure 6. Temperature step response curve in the laser zone of (a) measured curve and (b) simulated data curve.

Observing the figure, it is evident that under the influence of laser energy, the temperature history curve exhibits no hysteresis phenomenon. Based on the information presented, the transfer function for this segment of the temperature system can be determined: (1) $G_{11}\left(s\right)={\displaystyle \frac{0.208}{1.98s+1}}$(1) In this paper, the controlled object model comprises a three-input, three-output Multiple Input Multiple Output (MIMO) system. Employing a similar methodology, the transfer function of the system is analyzed, ultimately yielding the mathematical model for temperature control in the laser-induction hybrid strengthening process: (2) $G\left(s\right)=\left[\begin{array}{ccc}{G}_{11}& G_{12}& G_{13}\\ G_{21}& G_{22}& G_{23}\\ G_{31}& G_{32}& G_{33}\end{array}\right]=\left[\begin{array}{ccc}{\displaystyle \frac{0.208}{1.98s+1}}& {\displaystyle \frac{-153.6}{3.6s+1}}& 0\\ 0& {\displaystyle \frac{22.43}{3.37s+1}}& 0\\ 0& {\displaystyle \frac{-26.3}{6.2s+1}}& {\displaystyle \frac{17.28}{3.59s+1}}\end{array}\right]$(2)

Fuzzy PI decoupling control method

Design of the decoupling compensator

The laser-induction hybrid strengthening temperature system is a multivariable system with interconnections between control loops. To enhance overall control performance by mitigating the impact of coupling, decoupling strategies are employed. In engineering practice, four methods for designing decoupling compensators have been widely used: feedforward compensation, feedback compensation, diagonal compensation, and unit matrix compensation. The feedforward compensation decoupling method is one of the most widely used decoupling algorithms. Its relatively simple structure facilitates the achievement of decoupling objectives, resulting in a noticeably effective decoupling outcome. Liu et al. [22] proposed a decoupling strategy involving frequency feedforward control for the active loop and amplitude feedforward control for the reactive loop. This decoupling strategy, by directly modifying the reference values through feedforward, is easy to implement and adaptable to various control coordinates and structural setups.

Feedforward compensation treats the coupling channels, such as G12(s) and G32(s), as disturbance channels and utilizes them as inputs to the feedforward control channel. In accordance with the invariance principle, the calculated compensation values are combined with the system setpoints to diminish or eliminate the impact of V2 on Y1 and Y3. This approach effectively organizes the controlled variables in the temperature control system into three distinct loops, achieving the goal of decoupling.

Compared to alternative decoupling methods, feedforward decoupling is characterized by its simplicity, ease of implementation, and the minimal number of remaining control channels post-decoupling. This streamlined approach facilitates subsequent control processes. Therefore, feedforward compensation is chosen as the preferred method for decoupling the temperature system, as shown in Figure 7.

Figure 7. Structure diagram of temperature control system with feedforward decoupling compensation.

By introducing the feedforward compensator for decoupling the system, it transforms into three distinct individual loops. The decoupling process is represented as follows: (3) $\left[\begin{array}{c}{Y}_1\left(s\right)\\ Y_2\left(s\right)\\ Y_3\left(s\right)\end{array}\right]=\left[\begin{array}{ccc}{G}_{11}\left(s\right)& G_{12}\left(s\right)& 0\\ 0& G_{22}\left(s\right)& 0\\ 0& G_{32}\left(s\right)& G_{33}\left(S\right)\end{array}\right]\times \left[\begin{array}{ccc}1& D_{12}\left(s\right)& 0\\ 0& 1& 0\\ 0& D_{32}\left(s\right)& 1\end{array}\right]\left[\begin{array}{c}{V}_1\left(s\right)\\ V_2\left(s\right)\\ V_3\left(s\right)\end{array}\right]$(3) To achieve decoupling and simplify the matrix representation into a diagonal matrix form, the process can be described as follows: (4) $\left[\begin{array}{c}{Y}_1\left(s\right)\\ Y_2\left(s\right)\\ Y_3\left(s\right)\end{array}\right]=\left[\begin{array}{ccc}{M}_{11}\left(s\right)& 0& 0\\ 0& M_{22}\left(s\right)& 0\\ 0& 0& M_{33}\end{array}\right]\left[\begin{array}{c}{V}_1\left(s\right)\\ V_2\left(s\right)\\ V_3\left(s\right)\end{array}\right]$(4) The solution is as follows: (5) $\{\begin{array}{c}{G}_{12}\left(S\right)+G_{11}\left(S\right)D_{12}\left(S\right)=0\\ G_{32}\left(S\right)+D_{32}\left(S\right)G_{33}\left(S\right)=0\end{array}$(5) The values of the decoupling compensators can be obtained as follows: (6) $\{\begin{array}{c}{D}_{12}\left(S\right)=-{\displaystyle \frac{G_{12}\left(S\right)}{G_{11}\left(S\right)}}\\ D_{32}\left(S\right)=-{\displaystyle \frac{G_{32}\left(S\right)}{G_{33}\left(S\right)}}\end{array}$(6) The transfer functions M11(s)/M11(s)/M11(s) after the inclusion of the compensators are as follows: (7) $\{\begin{array}{c}{M}_{11}\left(S\right)=G_{11}\left(S\right)\\ M_{22}\left(S\right)=G_{22}\left(S\right)\\ M_{33}\left(S\right)=G_{33}\left(S\right)\end{array}$(7)

Design of a fuzzy PI controller

This study employs the fuzzy PI control method to manage the temperature during the laser-induction hybrid strengthening process. Figure 8 illustrates the structural diagram of the fuzzy PI control principle. By integrating fuzzy control theory with the traditional PI control algorithm, this method addresses the limitations inherent to the conventional PI algorithm, such as difficulties in precise target value control and coping with external disturbances. It also helps prevent surface melting due to excessive temperatures. The addition of fuzzy control theory improves the adaptability and robustness of the control system, enhancing its resilience against uncertainties and fluctuations. The fuzzy PI control method uses linguistic variables and fuzzy rules, offering a more flexible and intelligent approach to temperature regulation. Additionally, this method effectively eliminates steady-state errors, significantly enhancing the system's control accuracy and precision.

Figure 8. Structure diagram of fuzzy PI control principle.

Considering the characteristics of the laser-induction hybrid temperature intensification system and the imperative to achieve overall temperature change process control for stability, while maintaining the simplicity of the control process, the fuzzy language for the system inputs and outputs is partitioned into the seven segments:

{negative large, negative medium, negative small, zero, positive small, positive medium, positive large}

Their corresponding symbols are denoted as:

{NB, NM, NS, ZO, PS, PM, PB}.

To achieve the fuzzification of input variables, it is necessary to map these inputs to the fuzzy control domain. The conversion coefficient used in this mapping process is referred to as the quantization factor. Similarly, the output derived from reasoning must also be converted into a range that can be utilized by the controller using the same method, and the conversion coefficient for this process is known as the scale factor. The selections for the domains of fuzzy PI control are presented in Tables 2–4.

Table 2. Parameter table of laser temperature fuzzy control.

Variable name	Basic domain	Fuzzy domain	Quantification factor	Scale factor
Temperature error (e)	{−100,100}	{−6,6}	0.06
Error variation (ec)	{−30,30}	{−6,6}	0.2
$\mathrm{\Delta }$Kp	{−3,3}	{−3,3}		1
$\mathrm{\Delta }$Ki	{−2,2}	{−3,3}		0.67

Table 3. Parameter table of induction temperature fuzzy control.

Variable name	Basic domain	Fuzzy domain	Quantification factor	Scale factor
Temperature error (e)	{−50,50}	{−6,6}	0.12
Error variation (ec)	{−10,10}	{−6,6}	0.6
$\mathrm{\Delta }$Kp	{−0.2,0.2}	{−3,3}		0.067
$\mathrm{\Delta }$Ki	{−0.15,0.15}	{−3,3}		0.5

Table 4. Parameter table of cooling speed fuzzy control.

Variable name	Basic domain	Fuzzy domain	Quantification factor	Scale factor
Temperature error (e)	{−10,10}	{−6,6}	0.6
Error variation (ec)	{−3,3}	{−6,6}	2
$\mathrm{\Delta }$Kp	{−0.08,0.08}	{−3,3}		0.0267
$\mathrm{\Delta }$Ki	{−0,05,0,05}	{−3,3}		0.0167

In the design of the fuzzy PI controller for the laser-induction hybrid intensification process, employing triangular membership functions for the five central affiliation functions is advantageous as it enhances the system's response speed. Triangular affiliation functions are known for their computational efficiency, making them suitable for systems where rapid response is critical. Additionally, Gaussian membership functions, which resemble a normal distribution curve, are used at both ends of the spectrum. These provide broader coverage, improving the controller's ability to manage inputs that are far from the center of the range, thus ensuring a more comprehensive response to varying conditions within the system.

In the fuzzy PI control process, the inputs of the fuzzy controller are the error (e) and the error change rate (e_c). These values are obtained through the detection and calculation of the temperature, and the adjustment of PI parameters is determined through fuzzy rules. Real-time adjustments to the PI control parameters are then made, facilitating intelligent control throughout the entire process. The fuzzy rule control table is outlined in Table 5.

Table 5. Fuzzy control rule table.

e\ec	NB	NM	NS	ZO	PS	PM	PB
NB	NM/ZO	PB/NB	NM/NM	NM/NM	NM/ZO	ZO/ZO	ZO/ZO
ZO/ZO	NB/NB	PB/NB	NM/NM	NM/NS	NM/NS	NS/NS	ZO/ZO
ZO/ZO	PB/NB	PM/NM	PS/NS	NS/NS	NS/NS	ZO/NM	PS/NM
ZO	PM/NM	ZO/NM	PS/PS	ZO/ZO	PS/PS	PS/NM	PM/NM
ZO/ZO	PS/NM	PS/NM	ZO/ZO	NM/NS	PS/NS	NM/NS	NM/NS
ZO/ZO	ZO/ZO	NS/ZO	NM/ZO	NM/ZO	NM/NM	PB/NB	NB/NB
NB/NB	ZO/ZO	ZO/ZO	NM/PS	NM/PM	NM/NM	PB/NM	NB/NB

Temperature control results and analysis

System simulation

Following the determination of system parameters and decoupling compensator parameters through calculations, a temperature control system for laser-induction hybrid strengthening, along with the corresponding decoupled PI control system, was established using the Simulink plugin in MATLAB, the structure diagram is shown in Figure 9.

Figure 9. Simulink simulation model of fuzzy PI decoupling controller.

To achieve a more precise simulation of real-world operating conditions, the consideration of saturators is recommended to restrict the power output range of the controller. It is crucial to note that the laser has an output power range of 0–8000 W, whereas the induction heating device operates within an output power range of 0–40 kW. To enhance the realism of the simulation, the starting temperature is set at 20°C, the initial rate of cooling is set at 130°C/s, and the initial induction temperature is set at 300°C. Simulation results, as depicted in Figure 10, provide insights into the performance and behavior of the established temperature control system under the influence of various input conditions and dynamic changes in the laser-induction hybrid strengthening process.

Figure 10. Comparison of fuzzy PI decoupling control and traditional PI control.

The comparison of the performance between fuzzy PI decoupling control and traditional PI control in laser temperature response reveals a notable reduction in overshoot. However, there is no significant improvement in response speed. This can be attributed to the constraint on laser power due to the power limit, causing the laser power to reach its maximum value early in the strengthening process. Consequently, while the response time remains unchanged, the time for the system to reach stability is shortened, enabling the system to achieve temperature stability more rapidly with lower overshoot.

When examining the temperature response to induction and the cooling rate response, the use of the adaptive fuzzy control algorithm in fuzzy PI decoupling control yields faster response times during process adjustments and lower overshoot. This improves the efficiency of temperature control in the laser-induction hybrid strengthening process. The fuzzy PI decoupling control approach results in a system response characterized by reduced response time and decreased overshoot in comparison to the classic PI controller, signifying enhanced control performance in terms of induction temperature and cooling rate.

Temperature control system and control results

The control of the temperature change process in laser-sensor hybrid strengthening necessitates the establishment of a versatile hybrid strengthening platform with customizable parameters. The schematic diagram for the platform is depicted in Figure 11. This platform is designed to facilitate the coordinated adjustment of the laser, induction heating equipment, and cooling device. Key components of the platform include a temperature acquisition device and a position adjustment device. The temperature acquisition device is responsible for collecting real-time temperature data during the hybrid strengthening process. This data is crucial for monitoring and regulating the temperature changes induced by the laser and induction heating. The information gathered by the temperature acquisition device enables precise control and adjustment of the strengthening parameters. Additionally, the position adjustment device plays a pivotal role in the hybrid strengthening platform. This device allows for the fine-tuning of the relative positions of the laser, induction heating equipment, and cooling device. The ability to adjust the positions of these components ensures optimal heat distribution and treatment across the material surface, contributing to the effectiveness of the hybrid strengthening process.

Figure 11. Schematic diagram of temperature control system for laser-induction hybrid strengthening.

In a controlled experiment employing constant process parameters, samples undergo processing with specific settings: a laser power of 6000 W, a spot size of 15 × 50 mm, an output power of 10 kW for the induction heating device, a heat source spacing of 15 mm, and a scanning speed of 2.5 mm/s across the workpiece surface. Throughout the experiment, temperature values are meticulously measured in distinct regions, namely the laser processing area, the induction processing area, and the cooling rate between heat sources. These measurements are captured using infrared temperature measurement equipment. Subsequently, temperature curves are plotted based on the acquired measurements, as depicted in Figure 12. The curves represent the dynamic changes in temperature over time in the specified areas during the hybrid strengthening process.

Figure 12. Temperature curve in constant power output mode for (a) temperature of laser-heated zone temperature, (b) temperature of induction heated zone and (c) cooling speed.

The analysis of the temperature curves reveals several key observations. In the laser processing region, there is a pronounced temperature fluctuation, particularly during the strengthening process, where temperature fluctuations exceed 100°C. This extreme temperature instability significantly impacts the quality of the strengthening layer, introducing potential issues. Similarly, under stable power conditions, the temperature in the induction processing zone experiences significant changes, with the greatest temperature fluctuation reaching 37°C. Moreover, the assessment of cooling speed commences as the cooling measurement point approaches the workpiece, resulting in notable variations. Under constant power settings, both temperature and cooling velocity experience significant changes, which have a detrimental impact on the quality of the sample's strengthening during laser-induction hybrid processing.

To address these challenges, a temperature control system for the composite strengthening process is implemented, with control setpoints of 1100°C for laser temperature, 450°C for induction temperature, and a cooling rate of 130°C/s, aiming to enhance the consistency and quality of the laser-induction hybrid strengthening process. The subsequent processing of the specimens under these controlled conditions is monitored for process parameters, yielding the following results.

In the laser-induction hybrid strengthening process, a systematic analysis was conducted by adjusting different induction temperature setpoints to examine the microstructure distribution in the strengthened layer. The laser zone temperature was maintained at 1200°C, with a cooling rate of 130°C/s. Induction temperatures were individually controlled at 350°C, 400°C, 450°C, and 500°C, as illustrated in Figure 13. It is essential to emphasize that the temperature is measured at the beginning of the first coil in the composite strengthening process. As a result of repeated heating from numerous coil passes, the subsequent coils acquire additional heat. The selection of induction processing temperatures was informed by simulation outcomes, ensuring a comprehensive understanding of the impact of different temperature setpoints on the resulting microstructure distribution in the strengthened layer.

Figure 13. Temperature curve in temperature control mode for (a) temperature of laser-heated zone temperature, (b) temperature of induction heated zone and (c) cooling speed.

Conclusions

In the development of the laser-induction hybrid strengthening process, crafting an accurate mathematical model for the temperature system is challenging due to external environmental factors, variations in material texture, dimensional changes, and the complex interactions between the heat sources. To overcome these obstacles, this study adopts a hybrid methodology that merges simulation with experimental approaches.

1. The temperature system is mathematically modeled using the step response method. Furthermore, a fuzzy controller is integrated to facilitate real-time adjustments to the PI control parameters. This integration reduces reliance on the mathematical model and improves temperature regulation during the hybrid strengthening process, ensuring more effective control and adaptability to varying conditions.

2. Recognizing the complexities of a multi-input multi-output system and the need to minimize coupling effects, a feedforward decoupling compensation method is employed. This approach stands in contrast to traditional PI controllers by effectively reducing overrun and enhancing response times. It achieves this by dynamically adjusting the PI control parameters in response to variations in errors, thus providing a more responsive and accurate control system.

3. A laser-induction hybrid strengthening control experimental platform was established to perform temperature control experiments. Comparative analysis of the temperature curves post-control was conducted. The findings suggest that the laser-induction hybrid strengthening technology substantially improves the stability of the temperature variation process. Notably, this technique achieves steady-state errors of less than 1% for laser temperature, induction temperature, and cooling rate.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Key Research and Development Program of China under grant number 2023YFB4603400, the ‘Elite’ Program of Zhejiang Province under grant number 2022C03021 and 2023C01064, and the Significant Science and Technology Project of Longyou under grant number JHXM2023072.