Multi-machine collaborative fuse wire additive manufacturing method and system based on regional temperature prediction

Abstract

The invention provides a multi-machine collaborative fuse wire additive manufacturing method and system based on regional temperature prediction, which are suitable for electric arc fuse wire additive manufacturing of large-format magnesium and aluminum alloy components, and the method comprises the following steps: obtaining a change curve of temperature TiA and time of any point A of an end face when the ith layer of the component is formed under a single execution mechanism through analog simulation, according to a re-additive time range [tiA1-tiA0, tiA2-tiA0] corresponding to a single-layer forming time ti and an interlayer temperature control range T1-T2, a partition number range ni of the layer is determined, multi-actuator partition collaborative additive is adopted, an iterative partition segmentation method and path planning of all partitions are optimized, interlayer temperature control of all the partitions is achieved, local overtemperature is eliminated, an auxiliary heat source does not need to be additionally arranged, and the production efficiency is improved. And the additive heat source utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of arc fuse deposition forming technology, and in particular to a multi-machine collaborative fuse additive manufacturing method and system based on regional temperature prediction, which is suitable for arc fuse additive manufacturing of large-scale magnesium and aluminum alloy components. Background Art

[0002] As a rapid manufacturing technology, arc additive manufacturing technology has the characteristics of high flexibility and high material utilization rate. It has broad application prospects in the field of aerospace, weapons and other equipment models. At present, arc additive manufacturing technology still faces great difficulties in shape control and controllability. One of the key reasons is the control of interlayer temperature. When the interlayer temperature is too high, the edge of the formed part is prone to "flow" or nodules, which seriously reduces the forming accuracy. Some metal materials will have defects such as element segregation and thermal cracks, which seriously reduce the mechanical properties. When the interlayer temperature is too low, it is easy to form defects such as unfused, pores, and cracks, which seriously affect the part qualification rate. In the process of fuse additive manufacturing of large-scale magnesium and aluminum alloy components, the heat dissipation rate of the formed part is too fast, and the insufficient heat accumulation leads to too low interlayer temperature of the component and too large temperature gradient, which is easy to form defects.

[0003] To address the above-mentioned problems, researchers currently mainly use auxiliary heat sources or composite heat sources for forming, and use infrared thermometers to monitor temperature information in real time, adaptively adjust external cooling devices or adjust additive process parameters to achieve interlayer temperature control and reduce temperature gradients.

[0004] Patent document CN106670623B (Application Number: 201710177806.1) discloses a device for actively controlling interlayer temperature in arc additive manufacturing. This device uses electromagnetic induction heating and cooling devices to heat and cool the substrate or part, achieving interlayer temperature control. This method, which utilizes auxiliary heat and cooling sources, can be used to control interlayer temperature during the formation of large-format parts, but it does not improve the utilization rate of the additive heat source.

[0005] Patent document CN117259929A ​​(application number: 202311309070.0) discloses an additive manufacturing device suitable for large-sized rotating structures. The device can form large-sized rotating components without being restricted by the rotating body radius size and generatrix special-shaped curve, but is only applicable to rotating components and does not involve interlayer temperature control methods and additive heat source utilization.

[0006] Patent document CN119368930A (application number: 202411557066.0) discloses an additive manufacturing device and method with multiple heat sources and synchronous multi-wire feeding. A single actuator is equipped with a composite heat source and uses a multi-arc process to significantly increase the additive heat input. However, it does not solve the problem of interlayer temperature control during the formation of large-format parts.

[0007] Patent document CN118404164A (application number: 202410509469.1) discloses an additive method and device for a multi-wire arc combination based on heat and mass balance. The assembled multi-wire multi-arc composite gun body, through the heat and mass balance condition, uses the arc heat mainly to melt the wire, reducing the arc heat input acting on the substrate and additive sample. It is suitable for parts with a low heat dissipation rate, but not suitable for large-format component fuse additive, and does not involve interlayer temperature control methods.

[0008] In summary, the use of auxiliary heat sources or composite heat sources supplemented by infrared thermometers can achieve interlayer temperature control, but there are certain limitations when facing the fused filament additive manufacturing of large-format parts: ① The multi-machine collaborative process is complex, and without simulation optimization, interlayer temperature control of large-format components is difficult to achieve, which is prone to local overheating problems and cannot maximize the utilization of each additive heat source; ② The heat dissipation conditions and temperature field distribution of the formed parts change in real time, and online monitoring of temperature information and dynamic adjustment of process parameters have little effect on interlayer temperature control.

[0009] The present invention sets the interlayer temperature range of the formed component and predicts the time range for further additive manufacturing. After partitioning the sheet, multiple actuators are used for collaborative additive manufacturing. The iterative partitioning method, path planning of each partition and process parameters are optimized to achieve interlayer temperature control of each partition and eliminate local low temperature problems, effectively improve the utilization rate of the heat source, and ultimately realize multi-machine cooperative fused wire additive manufacturing without external auxiliary heat sources. Summary of the Invention

[0010] In view of the defects in the prior art, the purpose of the present invention is to provide a multi-machine collaborative fusible filament additive manufacturing method and system based on regional temperature prediction.

[0011] According to the present invention, a multi-machine collaborative fused filament additive manufacturing method based on regional temperature prediction includes: Step S1: Simulate and obtain the temperature of any point A on the end face of the component when forming the i-th layer under the condition of a single actuator without an external heat source and time Change curve; Step S2: Based on the temperature of any point A on the end surface and time Change curve and optimal interlayer temperature control range ~ , determine the time range for additional material addition; determine the number range of end face partitions of the i-th layer according to the time range for additional material addition; Step S3: Determine the number of end face partitions of the i-th layer based on the range of the number of end face partitions of the i-th layer. Each partition is formed by an actuator. By optimizing the iterative partitioning method, the path planning of each partition and the process parameters, any point in each partition meets the preset requirements, and the forming time of a single actuator for each partition is determined.

[0012] Preferably, step S2 includes: Step S2.1: Based on the temperature of any point A on the end surface and time Change curve and optimal interlayer temperature control range ~ , determine the time range for additional material ;in, Indicates the time when the i-th layer begins to form, The temperature at time point A is ; According to the preferred interlayer temperature control range ~ Determine the time it takes for the temperature to drop to T1 and the time to T2 ; Step S2.2: Based on the positions of multiple end face A points, , get multiple range; among which, Indicates the time when the i-th layer is completed; Step S2.3: Based on ni= Get the range of the number of partitions at level i .

[0013] Preferably, the step S2.1 includes: The optimal interlayer temperature control range T1~T2 for magnesium alloy arc fuse additive manufacturing is 100~70℃; The optimal interlayer temperature control range T1~T2 for aluminum alloy arc fuse additive manufacturing is 110~50℃.

[0014] Preferably, step S3 includes: Step S3.1: Simulate and obtain the temperature of any point A' on the end surface of the i-th layer in the k-th zone of the multi-actuator without external heat source and time The change curve of Step S3.2: Determine the starting time of forming the i-th layer according to the preferred interlayer temperature control range T1~T2 and Initial temperature at time point A' , k zone starts to form time And this Temperature at time point A' , the lowest temperature of point A' in k zone , k zone end forming time And the temperature of point A' at this time , the i-th layer ends the forming time and Temperature at time point A' , calculate the forming time of this layer ; .

[0015] Preferably, the optimization of the iterative partitioning method, the path planning of each partition and the process parameters so that any point in each partition meets the preset requirements includes: Any point in each partition satisfies ,in, Indicates the number of end face partitions at layer i; It represents the time it takes for a single actuator to form the i-th layer. When the conditions are met, the number of partitions of the i-th layer and the corresponding partition cutting method, path planning of each partition, and process parameters are output. Otherwise, the number of partitions of the i-th layer end face is re-determined, and the iterative optimization is re-triggered until any point in each partition meets the conditions.

[0016] According to the present invention, a multi-machine collaborative fused filament additive manufacturing system based on regional temperature prediction is provided, comprising: Module M1: Simulate and obtain the temperature of any point A on the end face of the formed component when the i-th layer is formed under the condition of a single actuator without an external heat source and time Change curve; Module M2: Based on the temperature of any point A on the end surface and time Change curve and optimal interlayer temperature control range ~ , determine the time range for additional material addition; determine the number range of end face partitions of the i-th layer according to the time range for additional material addition; Module M3: Determine the number of end face partitions of the i-th layer based on the range of the number of end face partitions of the i-th layer. Each partition is formed by an actuator. By optimizing the iterative partitioning method, the path planning of each partition and the process parameters, any point in each partition meets the preset requirements, and the forming time of a single actuator in each partition is determined.

[0017] Preferably, the module M2 includes: Module M2.1: Temperature based on any point A on the end surface and time Change curve and optimal interlayer temperature control range ~ , determine the time range for additional material ;in, Indicates the time when the i-th layer begins to form, The temperature at time point A is ; According to the preferred interlayer temperature control range ~ Determine the time it takes for the temperature to drop to T1 and the time to T2 ; Module M2.2: Based on multiple end face A point positions, according to , get multiple range; among which, Indicates the time when the i-th layer is completed; Module M2.3: Based on ni= Get the range of the number of partitions at level i .

[0018] Preferably, the module M2.1 includes: the preferred interlayer temperature control range T1~T2 for additive manufacturing of magnesium alloy arc fuse is 100~70°C; the preferred interlayer temperature control range T1~T2 for additive manufacturing of aluminum alloy arc fuse is 110~50°C.

[0019] Preferably, the module M3 includes: Module M3.1: Simulate and obtain the temperature of any point A' on the end surface of the i-th layer in the k-th zone of multiple actuators without external heating source and time The change curve of Module M3.2: Determine the starting time of the i-th layer according to the optimal interlayer temperature control range T1~T2 and Initial temperature at time point A' , k zone starts to form time And this Temperature at time point A' , the lowest temperature of point A' in k zone , k zone end forming time And the temperature of point A' at this time , the i-th layer ends the forming time and Temperature at time point A' , calculate the forming time of this layer ; .

[0020] Preferably, the optimization of the iterative partitioning method, the path planning of each partition and the process parameters so that any point in each partition meets the preset requirements includes: Any point in each partition satisfies ,in, Indicates the number of end face partitions at layer i; It represents the time it takes for a single actuator to form the i-th layer. When the conditions are met, the number of partitions of the i-th layer and the corresponding partition cutting method, path planning of each partition, and process parameters are output. Otherwise, the number of partitions of the i-th layer end face is re-determined, and the iterative optimization is re-triggered until any point in each partition meets the conditions.

[0021] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention discloses a multi-machine collaborative fused wire additive manufacturing method based on regional temperature prediction, which is suitable for the fused wire additive manufacturing of large-scale magnesium and aluminum alloy components. The method obtains the temperature TiA versus time curve of any point A on the end face of the component when forming the i-th layer under a single actuator through simulation. The number of partitions ni for the layer is determined based on the single-layer forming time ti and the additive time range [tiA1-tiA0, tiA2-tiA0] corresponding to the interlayer temperature control range T1~T2. The method adopts multi-actuator zone collaborative additive manufacturing, optimizes the iterative zone segmentation method and the path planning of each zone, realizes the interlayer temperature control of each zone, eliminates local overheating, and improves the utilization rate of the additive heat source. 2. The present invention predicts the forming temperature curve of each zone and controls it within the optimal interlayer temperature range by optimizing the zone segmentation method, zone path planning, and process parameters. This eliminates local overheating problems and effectively improves the utilization rate of the heat source for fused filament additive manufacturing. Ultimately, this achieves efficient, high-quality, and low-cost fused filament additive manufacturing with multi-machine collaboration without the need for an external auxiliary heat source. 3. The present invention aims to solve the bottleneck of insufficient utilization of fusible additive heat sources due to the need for external auxiliary heat sources to achieve interlayer temperature control for parts with larger forming widths. By setting the interlayer temperature range of the formed components and predicting the time range for re-additive manufacturing of each layer, the sheet is partitioned and multi-actuator collaborative additive manufacturing is adopted. The partitioning method, path planning and process parameters of each layer are optimized and iterated to control the interlayer temperature of each partition and eliminate local overheating, thereby improving the utilization of fusible additive heat sources and ultimately realizing multi-machine collaborative, efficient, high-quality and low-cost fusible additive manufacturing without external auxiliary heat sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings: Figure 1 is the temperature T of any point A on the end surface of the i-th layer of a single actuator without external heat source iA Schematic diagram of the change curve with time.

[0023] Figure 2 is the temperature T of any point A' on the end surface of the i-th layer in the k-th zone of the multi-actuator without external heat source kiA’ Schematic diagram of the change curve with time.

[0024] Figure 3 Schematic diagram of the additive manufacturing method for multi-machine partition collaboration of typical large-scale frame structures. DETAILED DESCRIPTION

[0025] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0026] Example 1 According to a multi-machine collaborative fused wire additive manufacturing method based on regional temperature prediction provided by the present invention, the temperature T of any point A on the end surface of the i-th layer of the component formed by a single actuator without an external heat source is obtained by simulation. iA The time variation curve is based on the layer forming time t i , the interlayer temperature control range T1~T2 corresponding to the additional material time range [t iA1- t iA0 , t iA2- t iA0 ], determine the number of partitions n in this layer i Each partition is formed by an actuator. By optimizing the iterative partition cutting method, the path planning of each partition and the process parameters, the temperature between the layers of each partition is controlled and the local over-temperature problem is eliminated, thus realizing multi-machine collaborative fused filament additive manufacturing without external auxiliary heat source.

[0027] The multi-machine collaborative fused filament additive manufacturing method based on regional temperature prediction includes: Step S1: Simulate and obtain the temperature of any point A on the end face of the component when forming the i-th layer under the condition of a single actuator without an external heat source and time Change curve; Step S2: Based on the temperature of any point A on the end surface and time Change curve and optimal interlayer temperature control range ~ , determine the time range for additional material addition; determine the number range of end face partitions of the i-th layer according to the time range for additional material addition; Specifically, the temperature at any point A on the end surface is and time The change curve is the curve of the temperature change of any point A over time after the i-th layer of the component is formed by a single actuator without an external heat source, and the time when the i-th layer starts to form is determined. And the initial temperature at point A , the i-th layer ends the forming time And the temperature at point A at this time , determine the time for the temperature to drop to T1 according to the preferred interlayer temperature control range and the time to T2 .

[0028] Among them, the preferred interlayer temperature control range T1~T2 for magnesium alloy arc fuse additive manufacturing is 100~70℃, and the preferred interlayer temperature control range T1~T2 for aluminum alloy arc fuse additive manufacturing is 110~50℃.

[0029] Number of partitions n i range, according to (n iA is an integer), obtain the position of point A, transform the position of point A to obtain the range of multiple points A; calculate n i = .

[0030] Step S3: Determine the number of end face partitions of the i-th layer based on the range of the number of end face partitions of the i-th layer. Each partition is formed by an actuator. By optimizing the iterative partitioning method, the path planning of each partition and the process parameters, any point in each partition meets the preset requirements, and the forming time of a single actuator for each partition is determined.

[0031] Specifically, the temperature of any point A' on the end surface of the i-th layer in the k-th zone of the multi-actuator without external heat source is obtained by simulation. and time The change curve of the layer temperature is determined according to the optimal interlayer temperature control range T1~T2 to determine the starting time of the i-th layer. And the initial temperature at point A' , k zone starts to form time And the temperature at point A' , the lowest temperature of point A' in k zone , k zone end forming time And the temperature of point A' at this time , the i-th layer ends the forming time And the temperature at point A' , calculate the forming time of this layer ; ; Optimize the partitioning method, partition path planning and process parameters so that any point A' in each partition satisfies When the conditions are met, the number of partitions in the i-th layer and the corresponding partition cutting method, path planning of each partition and process parameters are output; otherwise, the number of end face partitions in the i-th layer is re-determined and the iterative optimization is re-triggered until any point in each partition meets the conditions.

[0032] Output the number of partitions n at layer i kAnd the corresponding partitioning method, partition path planning and process parameters.

[0033] Among them, the time when the i-th layer starts to form The time when the k zone begins to form , the i-th layer forming end time The time when the k zone ends and the forming There is incomplete overlap, and there is an empty travel path in the actual actuator, and a certain amount of time must be reserved to ensure that each partition completes the forming of the same layer, without staggered layers causing defects such as unfused regional overlap.

[0034] The output of the number of partitions nk in the i-th layer is the optimization result that satisfies the interlayer temperature control. Using this optimization result, it can be ensured that all points in each partition meet the interlayer temperature control requirements during forming, and that the layer height of each partition is consistent and the regional overlap is flawless.

[0035] The present invention also provides a multi-machine collaborative fuse additive manufacturing system based on regional temperature prediction. The multi-machine collaborative fuse additive manufacturing system based on regional temperature prediction can be realized by executing the process steps of the multi-machine collaborative fuse additive manufacturing method based on regional temperature prediction, that is, those skilled in the art can understand the multi-machine collaborative fuse additive manufacturing method based on regional temperature prediction as an optimal implementation of the multi-machine collaborative fuse additive manufacturing system based on regional temperature prediction.

[0036] Example 2 Example 2 is a preferred example of Example 1 This embodiment takes a typical frame component of 2400mm (length) * 1500mm (width) as an example; According to the present invention, a multi-machine collaborative fused filament additive manufacturing method based on regional temperature prediction includes: Step 1: Determine the additive direction and optimize the additive model, then perform layer slicing and path planning to determine the forming time t of the single actuator in the i-th layer i ; Calculate the second layer (i=2), t2=160min.

[0037] Step 2: Simulate and obtain the temperature T of any point A on the end surface of the i-th layer of a single actuator without an external heat source iA The time variation curve is used to determine the time range of the additional material addition according to the optimal interlayer temperature control range T1~T2℃. iA1- t iA0 , t iA2- t iA0 ] and the number of partitions in this layer n i range.

[0038] Set T1 = 90 ° C, T2 = 70 ° C, and take any point A to calculate t2A1- t 2A0 =20min, t 2A2- t 2A0 =28min, according to , n 2A The number of partitions ranges from [4, 9], change the position of point A, and find n 2A Value, n2= , and finally the number of partitions n2 is in the range of [3, 10].

[0039] Step 3: Partition the i-th layer into n k , where n k ∈n i , optimize each partition model, perform path planning for each partition, and determine the forming time t of each partition single actuator ki ; Set n k =6, divide the second layer into 6 areas, and determine the forming time t 12 , t 22 …t 62 .

[0040] Step 4: Simulate and obtain the temperature T of any point A' on the end surface of the i-th layer in the k-th zone of the multi-actuator without external heating source kiA' The time variation curve is as follows: According to the optimal interlayer temperature control range T1~T2, the starting forming time t of the i-th layer is determined. kiA'0 And the initial temperature T at point A' kiA'0 , k zone starts to form time t kiA's And the temperature T at point A' kiA's , the lowest temperature T at point A' in k zone kiA'min , k zone end forming time t kiA'e And the temperature of point A' is T kiA'e , the i-th layer ends forming time t kiA'3 And the temperature T at point A' kiA'3 , calculate the forming time t of this layer i '; The simulation obtains the temperature T of any point A' in the selected area 1-6 12A' 、T 22A' …T 62A' The target temperature and the corresponding time are obtained according to the curve.

[0041] Step 5: Optimize the partitioning method, path planning of each partition and process parameters so that any point A' in each partition satisfies , go to step 6, otherwise repeat steps 3-5.

[0042] The target temperature and the corresponding time in the partitions 1-6 are judged to see if they meet the temperature requirements, and the cutting positions of the 6 partitions, the path planning and process parameters in each partition are optimized so that all points in the partitions 1-6 meet the requirements. If it is satisfied, go to step 6. If multiple rounds of iterative optimization cannot solve the problem, go back to step 3 and reset n k .

[0043] Step 6: Output the number of partitions n in the i-th layer k And the corresponding partitioning method, partition path planning and process parameters.

[0044] Output the partitioning positions of the 6 partitions in the second layer, the path planning and process parameters within each partition.

[0045] Those skilled in the art will appreciate that, in addition to implementing the system, device, and various modules provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, and the like by logically programming the method steps. Therefore, the system, device, and various modules provided by the present invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; the modules for implementing various functions can also be considered both software programs for implementing the method and structures within the hardware component.

[0046] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. A multi-machine collaborative fused filament additive manufacturing method based on regional temperature prediction, characterized in that: include: Step S1: Simulate and obtain the temperature of any point A on the end face of the component when forming the i-th layer under the condition of a single actuator without an external heat source and time Change curve; Step S2: Based on the temperature of any point A on the end surface and time Change curve and optimal interlayer temperature control range ~ , determine the time range for additional material addition; determine the number range of end face partitions of the i-th layer according to the time range for additional material addition; Step S3: Determine the number of end face partitions of the i-th layer based on the range of the number of end face partitions of the i-th layer. Each partition is formed by an actuator. By optimizing the iterative partitioning method, the path planning of each partition and the process parameters, any point in each partition meets the preset requirements, and the forming time of a single actuator for each partition is determined.

2. The multi-machine collaborative fused filament additive manufacturing method based on regional temperature prediction according to claim 1 is characterized in that: The step S2 comprises: Step S2.1: Based on the temperature of any point A on the end surface and time Change curve and optimal interlayer temperature control range ~ , determine the time range for additional material ;in, Indicates the time when the i-th layer begins to form, The temperature at time point A is ; According to the preferred interlayer temperature control range ~ Determine the time it takes for the temperature to drop to T1 and the time to T2 ; Step S2.2: Based on the positions of multiple end face A points, , get multiple range; among which, Indicates the time when the i-th layer is completed; Step S2.3: Based on ni= Get the range of the number of partitions at level i .

3. The multi-machine collaborative fused filament additive manufacturing method based on regional temperature prediction according to claim 2 is characterized in that: The step S2.1 includes: The optimal interlayer temperature control range T1~T2 for magnesium alloy arc fuse additive manufacturing is 100~70℃; The optimal interlayer temperature control range T1~T2 for aluminum alloy arc fuse additive manufacturing is 110~50℃.

4. The multi-machine collaborative fused filament additive manufacturing method based on regional temperature prediction according to claim 1 is characterized in that: The step S3 comprises: Step S3.1: Simulate and obtain the temperature of any point A' on the end surface of the i-th layer in the k-th zone of the multi-actuator without external heat source and time The change curve of Step S3.2: Determine the starting time of forming the i-th layer according to the preferred interlayer temperature control range T1~T2 and Initial temperature at time point A' , k zone starts to form time And this Temperature at time point A' , the lowest temperature of point A' in k zone , k zone end forming time And the temperature of point A' at this time , the i-th layer ends the forming time and Temperature at time point A' , calculate the forming time of this layer ; 。 5. The multi-machine collaborative fused filament additive manufacturing method based on regional temperature prediction according to claim 1, characterized in that: The optimization of the iterative partitioning method, the path planning of each partition, and the process parameters so that any point in each partition meets the preset requirements includes: Any point in each partition satisfies ,in, Indicates the number of end face partitions at layer i; It represents the time it takes for a single actuator to form the i-th layer. When the conditions are met, the number of partitions of the i-th layer and the corresponding partition cutting method, path planning of each partition, and process parameters are output. Otherwise, the number of partitions of the i-th layer end face is re-determined, and the iterative optimization is re-triggered until any point in each partition meets the conditions.

6. A multi-machine collaborative fused filament additive manufacturing system based on regional temperature prediction, characterized in that: include: Module M1: Simulate and obtain the temperature of any point A on the end face of the formed component when the i-th layer is formed under the condition of a single actuator without an external heat source and time Change curve; Module M2: Based on the temperature of any point A on the end surface and time Change curve and optimal interlayer temperature control range ~ , determine the time range for additional material addition; determine the number range of end face partitions of the i-th layer according to the time range for additional material addition; Module M3: Determine the number of end face partitions of the i-th layer based on the range of the number of end face partitions of the i-th layer. Each partition is formed by an actuator. By optimizing the iterative partitioning method, the path planning of each partition and the process parameters, any point in each partition meets the preset requirements, and the forming time of a single actuator in each partition is determined.

7. The multi-machine collaborative fused filament additive manufacturing system based on regional temperature prediction according to claim 6, characterized in that: The module M2 includes: Module M2.1: Temperature based on any point A on the end surface and time Change curve and optimal interlayer temperature control range ~ , determine the time range for additional material ;in, Indicates the time when the i-th layer begins to form, The temperature at time point A is ; According to the preferred interlayer temperature control range ~ Determine the time it takes for the temperature to drop to T1 and the time to T2 ; Module M2.2: Based on multiple end face A point positions, according to , get multiple range; among which, Indicates the time when the i-th layer is completed; Module M2.3: Based on ni= Get the range of the number of partitions at level i .

8. The multi-machine collaborative fused filament additive manufacturing system based on regional temperature prediction according to claim 7 is characterized in that: The module M2.1 includes: the preferred interlayer temperature control range T1~T2 for additive manufacturing of magnesium alloy arc fuse is 100~70℃; the preferred interlayer temperature control range T1~T2 for additive manufacturing of aluminum alloy arc fuse is 110~50℃.

9. The multi-machine collaborative fused filament additive manufacturing system based on regional temperature prediction according to claim 6, characterized in that: The module M3 includes: Module M3.1: Simulate and obtain the temperature of any point A' on the end surface of the i-th layer in the k-th zone of multiple actuators without external heating source and time The change curve of Module M3.2: Determine the starting time of the i-th layer according to the optimal interlayer temperature control range T1~T2 and Initial temperature at time point A' , k zone starts to form time And this Temperature at time point A' , the lowest temperature of point A' in k zone , k zone end forming time And the temperature of point A' at this time , the i-th layer ends the forming time and Temperature at time point A' , calculate the forming time of this layer ; 。 10. The multi-machine collaborative fused filament additive manufacturing system based on regional temperature prediction according to claim 6, characterized in that: The optimization of the iterative partitioning method, the path planning of each partition, and the process parameters so that any point in each partition meets the preset requirements includes: Any point in each partition satisfies ,in, Indicates the number of end face partitions at layer i; It represents the time it takes for a single actuator to form the i-th layer. When the conditions are met, the number of partitions of the i-th layer and the corresponding partition cutting method, path planning of each partition, and process parameters are output. Otherwise, the number of partitions of the i-th layer end face is re-determined, and the iterative optimization is re-triggered until any point in each partition meets the conditions.

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