CN113102869A - Particle swarm optimization-based double-wire MIG welding additive manufacturing system and method - Google Patents
Particle swarm optimization-based double-wire MIG welding additive manufacturing system and method Download PDFInfo
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- CN113102869A CN113102869A CN202110323628.5A CN202110323628A CN113102869A CN 113102869 A CN113102869 A CN 113102869A CN 202110323628 A CN202110323628 A CN 202110323628A CN 113102869 A CN113102869 A CN 113102869A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/16—Arc welding or cutting making use of shielding gas
- B23K9/173—Arc welding or cutting making use of shielding gas and of a consumable electrode
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/10—Other electric circuits therefor; Protective circuits; Remote controls
- B23K9/1006—Power supply
- B23K9/1043—Power supply characterised by the electric circuit
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/10—Other electric circuits therefor; Protective circuits; Remote controls
- B23K9/1006—Power supply
- B23K9/1043—Power supply characterised by the electric circuit
- B23K9/1056—Power supply characterised by the electric circuit by using digital means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/32—Accessories
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/32—Accessories
- B23K9/325—Devices for supplying or evacuating shielding gas
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Abstract
The invention discloses a particle swarm optimization-based double-wire MIG welding additive manufacturing system and method, which comprises the following steps: the device comprises a power supply module, a power supply control module, an additive manufacturing module, a quality fuzzy evaluation module, a PID current waveform intelligent control module and an additional protective gas module; the power supply module comprises a DSP control core, a main control circuit, a power circuit and a user setting interface; the power control module comprises a main program module, a functional module, a parameter setting module and an initialization module; the additive manufacturing module comprises a welding gun, an additive manufacturing robot and a control system; the quality fuzzy evaluation module is used for carrying out fuzzy evaluation on the quality of the double-wire MIG welding additive manufacturing sample; and the PID current waveform intelligent control module is used for optimizing pulse current output in the additive manufacturing process. According to the invention, the particle swarm optimization is carried out on the neural network PID current waveform control algorithm, so that a better control effect is obtained, and the material increase manufacturing quality and efficiency of the double-wire MIG welding are improved.
Description
Technical Field
The invention belongs to the technical field of welding additive manufacturing, and particularly relates to a particle swarm optimization-based double-wire MIG welding additive manufacturing system and method.
Background
The welding method is a new technological method for additive manufacturing. If the traditional single-wire MIG welding method does not adopt an improved process or a control method, the speed is directly increased, and weld forming defects such as undercut, hump welding bead and the like are easy to occur. And the adoption of the double-wire pulse MIG welding can strengthen the stirring of a molten pool, improve the electric arc distribution, improve the forming quality of a welding seam, obviously reduce the heat input and effectively improve the production efficiency of additive manufacturing.
In twin wire MIGs, in addition to protecting the weld pool from harmful contamination that may create defects, shielding gas significantly affects weld shape, weld appearance, droplet transfer, arc stability, joint metallurgy and mechanical properties, and is therefore a key factor in determining weld joint performance and welding process efficiency.
Disclosure of Invention
The invention mainly aims to overcome the defects and shortcomings of the prior art and provides a particle swarm optimization-based dual-wire MIG welding additive manufacturing system and method.
In order to achieve the purpose, the invention adopts the following technical scheme:
a particle swarm optimization-based dual-wire MIG welding additive manufacturing system comprises: the device comprises a power supply module, a power supply control module, a PID current waveform intelligent control module, a quality fuzzy evaluation module, an additive manufacturing module and an additional protective gas module;
the power supply module comprises a DSP control core, a main control circuit, a power circuit and a user setting interface;
the power supply control module comprises a main program module, a functional module, a parameter setting module and an initialization module;
the additive manufacturing module comprises a welding gun, an additive manufacturing robot and a control system, and is used for additive manufacturing of double-wire MIG welding;
the quality fuzzy evaluation module is used for carrying out fuzzy evaluation on the quality of the double-wire MIG welding additive manufacturing sample;
the PID current waveform intelligent control module is based on a neuron network optimized by a particle swarm optimization algorithm and is used for optimizing pulse current output in the additive manufacturing process;
the power supply control module and the PID current waveform intelligent control module control the output of the power supply module and output the output to the additive manufacturing module and the additional shielding gas module to control the welding process, and after welding is finished, the welding quality is evaluated by the quality fuzzy evaluation module.
Furthermore, the main control circuit specifically comprises a welding switch control circuit, a welding air supply control circuit and a welding output voltage and current detection circuit;
the input signal of the welding switch control circuit is a pilot wire welding gun starting signal, and the input signal is used for controlling the starting of a pilot wire gas detection function and the starting of a pilot wire gas detection function;
the input signal of the welding gas supply control circuit is from a DSP control core, and when the input signal is a low level signal, the gas supply valve is controlled to open and supply gas through the conduction of an optical coupler;
the input signal of the welding output voltage and current detection circuit is welding feedback current, and the output signal is an output current DSP detection signal, and the output signal is used for conditioning the feedback current and sending the feedback current to the DSP for sampling.
Furthermore, the additional shielding gas module is provided with two paths of three-phase gas valves, two paths of additional shielding gas branch shielding gas flow passages are arranged, and two additional shielding gas nozzles are additionally arranged on the welding gun nozzle and are respectively fixed on the left side and the right side of the welding gun nozzle by fasteners;
the welding gun is provided with a left gas nozzle, a middle gas nozzle and a right gas nozzle, the middle gas nozzle realizes the main shielding gas supply of the dual-wire welding additive manufacturing, and the left nozzle and the right nozzle realize the additional shielding gas supply.
Further, simulation is carried out on the power supply module by using Simulink software, and particle swarm optimization is carried out on a neuron network PID current waveform control algorithm.
Further, the function extremum optimization of the particle swarm optimization specifically comprises the following steps:
initializing a group of particles in a solvable space, wherein each particle comprises three kinds of information of position, speed and fitness value;
the motion of the particles updates the individual positions according to the individual extreme Pb and the global extreme Gb;
each time the position of the particle is updated, the fitness value needs to be calculated again, so that the positions of Pb and Gb are updated by comparing the fitness value of the updated particle with the fitness values of Pb and Gb.
Furthermore, the power control module adopts four paths of PWM, wherein two paths are used for digital-to-analog conversion and setting welding current and arc voltage, the PWM pulse waveform is generated by an oscillator of the DSP controller, the control register is programmed to realize the PWM pulse waveform, and the frequency is more than 20 kHz.
Further, the DPS control core specifically employs TMS320F280049 with the highest data processing frequency of 100 Hz.
Further, the main program module is used for timing control of two-wire MIG welding, and the two-wire AD adopts a 12-bit digital-to-analog converter using three 3.45MSPS on TMS320F 280049.
Further, the additive manufacturing robot specifically adopts a six-axis robot model HL 6-0900-3656.
The invention also provides a double-wire MIG welding additive manufacturing method based on the double-wire MIG welding additive manufacturing system, which comprises the following steps:
the DSP is initialized when the welding power supply is powered on, and then the system firstly receives a welding program or welding parameters preset by a human-computer interface, sets the parameters and presets a wire feeding speed;
the system judges the welding start and respectively judges whether the front wire and the rear wire are successfully arcing;
the system controls the welding process, and controls the cross flow of the welding current of the front wire and the welding current of the rear wire by using double-wire pulse time sequence control;
and after the welding gun is monitored to be disconnected, the PWM is turned off, the arc is closed, and the welding is finished.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the hardware circuit of the power supply control module is composed of two sets of monofilament main circuits, and the two sets of main circuits are simultaneously controlled by the same DSP control chip, so that the problem of cooperative control between two power supplies is avoided, the control performance of the chip can be fully exerted, and the efficiency is improved; the power control module adopts four paths of PWM, the functions of the power control module are mainly realized by programming a control register, and the software programming method can reduce hardware design and reduce cost.
2. The invention introduces the additional shielding gas module, the additional shielding gas acts on the liquid molten pool, the gas is sprayed to cover the newly formed welding line to isolate air, and molten metal behind the high-temperature molten pool is prevented from being influenced by the environment, so that the welding line forming is adjusted, the welding process window is expanded, and the material increase manufacturing quality and efficiency of the double-wire MIG welding are improved.
3. The quality fuzzy evaluation module is used for analyzing quantitative analysis factors such as current stability and non-quantitative analysis factors such as sample appearance defects of the test piece, and finishing evaluation on the quality of the additive manufacturing sample.
4. The invention carries out particle swarm optimization on the neural network PID current waveform control algorithm, the neural network PID control optimized by the particle swarm optimization has better control effect, the control quantity not only approaches to the target value more quickly, but also has shorter dynamic response time and smaller overshoot. The current waveform and weld joint test verifies that the adaptive adjustment effect of the algorithm on the current waveform output by large current is obviously superior to that of the classical PID current waveform control algorithm, and the current waveform test weld joint quality after the neuron network PID control optimization is optimized by the particle swarm optimization is good.
Drawings
FIG. 1 is a schematic block diagram of the system of the present invention;
FIG. 2 is a hardware configuration of an inventive dual wire MIG welding power module;
FIG. 3a is a weld switch control circuit of the present invention;
FIG. 3b is a weld switch control circuit of the present invention;
FIG. 4 is a weld feed control circuit of the present invention;
FIG. 5 is an output current detection circuit of the present invention;
FIG. 6 is an output voltage detection circuit of the present invention;
FIG. 7 is a schematic diagram of the power control module of the present invention;
FIG. 8 is a flowchart of a twin wire MIG welding process of the present invention;
FIG. 9 is a flow chart of the PSO algorithm based function extremum optimization algorithm of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Examples
As shown in fig. 1, the present invention provides a particle swarm optimization-based dual-wire MIG welding additive manufacturing system, which includes:
the device comprises a power supply module, a power supply control module, an additive manufacturing module, a quality fuzzy evaluation module, a PID current waveform intelligent control module and an additional protective gas module; the power supply module comprises a DSP control core, a main control circuit, a power circuit and a user setting interface; the power supply control module comprises a main program module, a functional module, a parameter setting module and an initialization module; the additive manufacturing module comprises a welding gun, an additive manufacturing robot and a control system, and is used for additive manufacturing of double-wire MIG welding;
the quality fuzzy evaluation module is used for carrying out fuzzy evaluation on the quality of the double-wire MIG welding additive manufacturing sample;
the PID current waveform intelligent control module is based on a neuron network optimized by a particle swarm optimization algorithm and is used for optimizing pulse current output in the additive manufacturing process;
in this embodiment, the main control circuit is composed of a welding switch control circuit, a welding gas supply control circuit and an output voltage and current detection circuit; the output voltage and current detection circuit comprises an output voltage detection circuit and an output current detection circuit;
the input signal of the welding switch control circuit is a pilot wire welding gun starting signal, and the input signal has two functions: controlling the opening of a front guide wire gas detection function and the opening of a front guide wire gas detection function; the input signal of the welding gas supply control circuit is from a DSP control core, and when the input signal is a low level signal, the gas supply valve is controlled to open and supply gas through the conduction of an optical coupler; the input signal of the welding output voltage and current detection circuit is welding feedback current, the output signal is output current DSP detection signal, and the circuit is used for conditioning the feedback current and sending the feedback current to DSP for sampling. Fig. 2 is a schematic structural diagram of a power module according to the present invention.
As shown in fig. 3a and 3B, for the welding switch control circuit, the 15V input voltage is divided by three power supply resistors of the comparator U22A, resulting in 5V voltage values at a (monitoring point) and B. The voltage at C is also obtained by dividing voltage by the resistor and the peripheral circuit. When a BUTTON1 key of a peripheral circuit is pressed down, the voltage at the C position is obtained to be 3.2V through circuit voltage division, then the voltage at the C position is compared with the voltage at the A, B position through U22A and U22B respectively, one path of output signal after the voltage comparison at the A, C position can obtain the output of the D position to be in a high-resistance state, the final output signal is the conduction value of a U21 optocoupler, and because the base voltage is required to be in a high level when the Q2 is switched on, namely the output of the D position is in a low level, the triode is not conducted, so that the U4 optocoupler in the welding switch control signal output circuit is not conducted, and the signal CLOUT1 is in a high level; B. and the other path of signal output after the voltage comparison at the position C is low level, so that the U20 optical coupler is conducted, the U4 optical coupler is further conducted, and the low level is output. When the BUTTON2 key of the peripheral circuit is pressed, the voltage output at C is 8.4V calculated by voltage division, the output at D is high impedance state compared with the output of A through U22B, and the comparison result with the output of B through U22B can determine that the voltage at E is also high impedance state. The other path of signal input at the point D is 15V signal, the base voltage of the Q2 obtained after the voltage division of the resistor and the voltage stabilizing tube is 3.6V and is greater than the conduction voltage of the triode by 0.7V, so that the Q2 tube is conducted, and the base voltage is clamped at the conduction voltage value. And under the state that Q2 switched on, can make U21 opto-coupler switch on, and U21 opto-coupler switches on and can trigger U5 opto-coupler and switch on. While when BUTTON1 and BUTTON2 are both in the OFF state, the output at point D is low, and the output at point E is high, if BUTTON1 and BUTTON2 are both synchronously pressed, the signal outputs are the same as when BUTTON1 is pressed, and when the output signal CLOUT1 is low and the output signal CLOUT2 is also low, the welding control program and the welding air supply program are respectively executed, and when the output signals are both high, the welding operation is started.
As shown in fig. 4, for the weld blow control circuit, the circuit input signal CLIN1 is from the DSP and represents a high output level when the DSP pin output value is 3.3V and a low output level when the pin output value is 0V. The output signal of the circuit is PLOUT1, which controls an external bleed valve. When the input CLIN1 is at low level, the relay can be opened by the optical coupling conduction, and the air supply valve circuit starts air supply.
As shown in fig. 5, the output current sensing circuit input signal is the PLIN3 welding feedback current; the circuit output signal is the output current DSP detection signal CLOUT 5. The circuit is used for conditioning the feedback current and sending the conditioned feedback current to the DSP for sampling.
As shown in FIG. 6, for the output voltage detection circuit, the circuit input signal is signal PLIN4 which is representative of the welding output voltage signal; signal CLOUT4, which is provided to the DSP as a welding output voltage sample signal, and signal CLOUT3, which is a DSP short circuit detection signal. The circuit has the function of conditioning a welding output voltage signal and then sending the conditioned welding output voltage signal to a DSP chip for sampling, detecting and processing.
In this embodiment, the main program module of the power control module is used for the timing control of the twin-wire MIG welding, and the overall process includes: presetting parameters, judging the start of welding, and controlling and ending the process; the double-wire A/D sampling uses three 12-bit digital-to-analog converters of 3.45MSPS on TMS320F280049, an AD sampling function is called in an interrupt service program, and function parameters are corrected; the power control system of the power supply is shared by four paths of PWM, two paths are specially used for digital-to-analog conversion, the other two paths can be used for digital-to-analog conversion and can also give welding current and arc voltage, the pulse waveform of the power supply is generated by a DSP self oscillator, the main function is realized by programming a control register, and the frequency is more than 20 kHz. Fig. 7 is a diagram showing a software system structure when the power control module is implemented.
In the embodiment, the additional shielding gas module acts on the liquid molten pool, and covers the newly formed weld joint by utilizing gas injection to isolate air, so that molten metal behind the high-temperature molten pool is prevented from being influenced by the environment, the weld joint forming is adjusted, the welding process window is expanded, and the quality and the efficiency of additive manufacturing of the twin-wire MIG welding are improved; the used shielding gas is argon, the additional shielding gas module is located outside the platform, two paths of three-phase gas valves are added on the basis of the original shielding gas device and the wire feeding device, two paths of additional shielding gas branch shielding gas flow passages are expanded, two additional shielding gas nozzles are added on the welding gun nozzle and are respectively fixed on the left side and the right side of the welding gun nozzle by fasteners, the welding gun is provided with a left gas nozzle, a middle gas nozzle and a right gas nozzle, the middle gas nozzle realizes double-wire welding material increase manufacturing main shielding gas supply, and the left nozzle and the right nozzle realize additional shielding gas supply.
In the embodiment, the additive manufacturing robot specifically adopts a six-axis robot of HL 6-0900-; the DPS control core adopts TMS320F280049 with the highest data processing frequency of 100 MHz.
In this embodiment, Simulink software is used to simulate the main circuit, the control circuit and the whole circuit of the dual-wire MIG welding power supply system. And performing particle swarm optimization on the neural network PID current waveform control algorithm.
As shown in fig. 9, a flowchart of the function extremum optimizing algorithm based on the PSO algorithm of the invention specifically includes the following steps:
firstly, initializing a group of particles in a solvable space, wherein each particle comprises three kinds of information of position, speed and fitness value; the motion of the particles updates the individual positions according to the individual extreme Pb and the global extreme Gb; each time the position of the particle is updated, the fitness value needs to be calculated again, so that the positions of Pb and Gb are updated by comparing the fitness value of the updated particle with the fitness values of Pb and Gb.
Based on the system described in the above embodiment, the present invention further provides a particle swarm optimization-based dual-wire MIG welding additive manufacturing method, as shown in fig. 8, including the following steps:
the DSP is initialized when the welding power supply is powered on, and then the system firstly receives a welding program or welding parameters preset by a human-computer interface, sets the parameters and presets a wire feeding speed;
the system judges the welding start and respectively judges whether the front wire and the rear wire are successfully arcing;
the system controls the welding process, and controls the cross flow of the welding current of the front wire and the welding current of the rear wire by using double-wire pulse time sequence control;
and after the welding gun is monitored to be disconnected, the PWM is turned off, the arc is closed, and the welding is finished.
It should also be noted that in this specification, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A two-wire MIG welding additive manufacturing system based on particle swarm optimization is characterized by comprising the following components: the device comprises a power supply module, a power supply control module, a PID current waveform intelligent control module, a quality fuzzy evaluation module, an additive manufacturing module and an additional protective gas module;
the power supply module comprises a DSP control core, a main control circuit, a power circuit and a user setting interface;
the power supply control module comprises a main program module, a functional module, a parameter setting module and an initialization module;
the additive manufacturing module comprises a welding gun, an additive manufacturing robot and a control system, and is used for additive manufacturing of double-wire MIG welding;
the quality fuzzy evaluation module is used for carrying out fuzzy evaluation on the quality of the double-wire MIG welding additive manufacturing sample;
the PID current waveform intelligent control module is based on a neuron network optimized by a particle swarm optimization algorithm and is used for optimizing pulse current output in the additive manufacturing process;
the power supply control module and the PID current waveform intelligent control module control the output of the power supply module and output the output to the additive manufacturing module and the additional shielding gas module to control the welding process, and after welding is finished, the welding quality is evaluated by the quality fuzzy evaluation module.
2. The particle swarm optimization-based dual-wire MIG welding additive manufacturing system according to claim 1, wherein the master control circuit specifically comprises a welding switch control circuit, a welding gas supply control circuit, a welding output voltage and current detection circuit;
the input signal of the welding switch control circuit is a pilot wire welding gun starting signal, and the input signal is used for controlling the starting of a pilot wire gas detection function and the starting of a pilot wire gas detection function;
the input signal of the welding gas supply control circuit is from a DSP control core, and when the input signal is a low level signal, the gas supply valve is controlled to open and supply gas through the conduction of an optical coupler;
the input signal of the welding output voltage and current detection circuit is welding feedback current, and the output signal is an output current DSP detection signal, and the output signal is used for conditioning the feedback current and sending the feedback current to the DSP for sampling.
3. The particle swarm optimization-based dual-wire MIG welding additive manufacturing system according to claim 1, wherein the additional shielding gas module is provided with two three-phase gas valves, two additional shielding gas branch shielding gas flow passages, two additional shielding gas nozzles are added on the welding gun nozzle and are respectively fixed on the left side and the right side of the welding gun nozzle through fasteners;
the welding gun is provided with a left gas nozzle, a middle gas nozzle and a right gas nozzle, the middle gas nozzle realizes the main shielding gas supply of the dual-wire welding additive manufacturing, and the left nozzle and the right nozzle realize the additional shielding gas supply.
4. The particle swarm optimization-based dual-wire MIG welding additive manufacturing system according to claim 1, wherein the power module is simulated by Simulink software, and the optimization of the particle swarm optimization is performed on a neural network PID current waveform control algorithm.
5. The particle swarm optimization-based dual-wire MIG welding additive manufacturing system according to claim 4, wherein the function extremum optimization of the particle swarm optimization specifically comprises the following steps:
initializing a group of particles in a solvable space, wherein each particle comprises three kinds of information of position, speed and fitness value;
the motion of the particles updates the individual positions according to the individual extreme Pb and the global extreme Gb;
each time the position of the particle is updated, the fitness value needs to be calculated again, so that the positions of Pb and Gb are updated by comparing the fitness value of the updated particle with the fitness values of Pb and Gb.
6. The particle swarm optimization-based dual-wire MIG welding additive manufacturing system according to claim 1, wherein the power control module adopts four PWM (pulse-Width modulation), two PWM paths are used for digital-to-analog conversion and for setting the welding current and the arc voltage, PWM pulse waveforms of the system are generated by an oscillator of a DSP (digital signal processor) controller, and the PWM pulse waveforms are realized by programming a control register and have the frequency of more than 20 kHz.
7. The particle swarm optimization-based dual-wire MIG welding additive manufacturing system according to claim 1, wherein the DPS control core is specifically TMS320F280049 with the highest data processing frequency of 100 Hz.
8. The particle swarm optimization-based dual-wire MIG welding additive manufacturing system of claim 6, wherein the main program module is used for timing control of dual-wire MIG welding, and the dual-wire AD thereof adopts a 12-bit digital-to-analog converter using three 3.45MSPS on TMS320F 280049.
9. The particle swarm optimization-based double-wire MIG welding additive manufacturing system as claimed in claim 1, wherein the additive manufacturing robot is a six-axis robot of HL6-0900-3656 type.
10. A method for additive manufacturing of twin-wire MIG welding based on the system for additive manufacturing of twin-wire MIG welding according to any of the claims 1 to 9, comprising the steps of:
the DSP is initialized when the welding power supply is powered on, and then the system firstly receives a welding program or welding parameters preset by a human-computer interface, sets the parameters and presets a wire feeding speed;
the system judges the welding start and respectively judges whether the front wire and the rear wire are successfully arcing;
the system controls the welding process, and controls the cross flow of the welding current of the front wire and the welding current of the rear wire by using double-wire pulse time sequence control;
and after the welding gun is monitored to be disconnected, the PWM is turned off, the arc is closed, and the welding is finished.
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