CN108775975A - Reflow furnace temperature curve intelligent detection system and detection method - Google Patents
Reflow furnace temperature curve intelligent detection system and detection method Download PDFInfo
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Abstract
The invention provides a reflow soldering furnace temperature curve intelligent detection system and a detection method, wherein the detection system comprises a data acquisition module, a furnace temperature tester and a data processing module; the data acquisition module forwards the furnace temperature data to the data processing module; the furnace temperature tester outputs plate temperature data to the data processing module, conversion relation data can be calculated and generated by combining the furnace temperature data and the plate temperature data, and when the generation is carried out, the real-time furnace temperature curve data can be calculated through the conversion relation data only by acquiring the real-time furnace temperature curve data through the temperature measuring device, so that the production condition is monitored in real time.
Description
Technical Field
The invention relates to an online detection system and an online detection method for high-temperature furnace temperature, in particular to an intelligent detection system and an intelligent detection method for a reflow furnace temperature curve.
Background
SMT (Surface Mount Technology) is currently the most common manufacturing process in the electronics assembly industry, where reflow soldering is an important component of SMT. Reflow soldering is performed by re-melting the paste-like soft solder pre-distributed on the printed board, and then the physical connection and the electrical connection between the soldering terminal or the pin of the component and the pad on the printed board are realized. The reflow soldering process is complicated in variation, wherein more process parameters are involved, and the setting of the temperature curve is particularly critical.
The current temperature profile setting method includes obtaining a temperature profile about a test substrate by manufacturing the test substrate and by a furnace temperature detector, and taking the temperature profile as a reference profile; and calling the reference curve for trial processing before batch production, judging whether the furnace temperature is available by a worker, and entering batch production if the furnace temperature is available.
The existing temperature curve setting method has the problems that the current furnace temperature cannot be monitored in real time in the production process by using a reference furnace temperature curve, and whether the furnace temperature is abnormal or not can be judged only by judging whether the welded product is abnormal or not, so that batch production scrapping is easily caused.
Disclosure of Invention
The invention aims to provide an intelligent detection system for a reflow furnace temperature curve, which is used for detecting the temperature of a board in real time.
The second purpose of the invention is to provide an intelligent detection method for the temperature curve of the reflow soldering furnace, which can detect the board temperature in real time.
The invention provides an intelligent detection system for a reflow soldering furnace temperature curve, which comprises a data acquisition module, a furnace temperature tester and a data processing module; the data acquisition module comprises a main control unit and a temperature detection unit; the temperature detection unit comprises a plurality of temperature measurement elements which are fixedly arranged in the reflow furnace along the conveying direction; the main control unit acquires furnace temperature data sent by the temperature detection unit and forwards the furnace temperature data to the data processing module; the furnace temperature tester outputs plate temperature data to the data processing module.
According to the scheme, the plurality of temperature measuring elements are arranged on the two sides of the conveying track to obtain air temperature curve data in the furnace, and the furnace temperature detector can be sent into the conveying track together with the test PCBA to obtain board temperature curve data of the test PCBA; after the furnace air temperature curve data and the temperature curve data are compared and obtained, conversion relation data about the plate parameters and the hot air parameters can be obtained, and in actual production, real-time furnace air temperature curves only need to be detected through a temperature measuring element, so that real-time plate temperature curve data can be generated according to the furnace air temperature curves, the plate parameters of real-time plates, the real-time hot air parameters and the conversion relation data obtained previously, and real-time detection of the surface temperature of the plates is achieved.
The data acquisition module further comprises a speed detection unit; the main control unit acquires the speed data sent by the speed detection unit and forwards the speed data to the data processing module.
Therefore, the speed detection unit detects the conveying speed of the reflow oven, and the speed data are sent to the data processing module and then displayed together with the temperature curve data.
The data acquisition module further comprises a code scanning detection unit, wherein the code scanning detection unit comprises code scanning equipment, and the two code scanning equipment are respectively arranged at the transmission input end and the transmission output end of the reflow oven; the main control unit acquires the code scanning data sent by the code scanning detection unit and forwards the code scanning data to the data processing module.
It can be seen from above that, two sweep a yard equipment setting and sweep a yard at conveying input and conveying output, sweep a yard by the yard equipment that sweeps that corresponds when each product gets into and flows out, and the welding duration and the state of each product are all monitored.
The data acquisition module further comprises an abnormity alarm unit, and the main control unit outputs a control signal to the abnormity alarm unit.
Therefore, the abnormity warning unit comprises a warning lamp or a buzzer and other equipment capable of generating warning prompts, the board temperature curve data is acquired and then compared with the preset condition requirements, and if the board temperature curve data is not accordant with the preset condition, the abnormity warning unit receiving the feedback signal can generate the warning prompts for the working personnel.
Further, a plurality of temperature measuring elements are arranged on the left and right sides of the conveying track in the reflow oven.
Therefore, the temperatures on the two sides of the conveying track are the closest to the temperature on the conveying track, the calculation accuracy of the conversion relation data is improved, and the product conveying and welding are not hindered.
The reflow soldering furnace temperature curve intelligent detection method provided by the second object of the invention is applied to a data processing module of a reflow soldering furnace temperature curve intelligent detection system, and comprises the steps of generating test furnace temperature curve data according to the obtained test furnace temperature data, and generating test board temperature curve data related to testing PCBA according to the obtained test board temperature data; generating conversion relation data according to the test furnace temperature curve data, the test board temperature curve data, the board parameter data of the test PCBA and the test hot air parameter data; and generating real-time furnace temperature curve data according to the acquired real-time furnace temperature data, and generating real-time plate temperature curve data related to real-time plates according to the real-time furnace temperature curve data, the conversion relation data, the plate parameter data of the real-time plates and the real-time hot air parameter data.
According to the scheme, after the intelligent detection system for the temperature curve of the flow soldering furnace is used for acquiring the air temperature curve data in the reflow furnace and the board temperature curve data of the test PCBA, the parameters of hot air in the furnace and the physical parameters of the test PCBA are known, a conversion relation exists between the air temperature curve data and the board temperature curve data, the conversion relation is related to the parameters of the hot air in the furnace and the physical parameters of the test PCBA, after the conversion relation is calculated, the real-time board temperature curve data can be converted and calculated through the conversion relation only by detecting the real-time air temperature curve data during each production, and therefore the real-time detection of the surface temperature of a plate is achieved.
The method further comprises the step of acquiring real-time furnace temperature data according to a preset time frequency after the conversion relation data are generated.
Therefore, the measured temperatures of the plurality of temperature measuring elements are obtained by setting the fixed time interval according to the conveying position of the product, the tested product is closer to the element which is measuring the temperature, and the obtained data is more accurate.
The method further comprises the step of generating actual process parameter data according to the real-time plate temperature curve data after the real-time plate temperature curve data are generated.
Therefore, the process parameter data comprises maximum temperature rise slope data, maximum temperature fall slope data, constant temperature time data, backflow time data, maximum temperature data and the like, and the generated actual process parameter data is more beneficial to monitoring and judging the welding condition by workers.
The method further comprises the steps of judging whether the actual process parameter data meet the preset requirements or not after generating the actual process parameter data according to the real-time plate temperature curve data, and if not, generating prompt information.
Therefore, the preset requirements can comprise plate technological parameters, welding flux technological parameters and the like, and the actual technological parameter data are monitored, so that the rejection rate is effectively reduced.
The further scheme is that when the actual process parameter data is judged to meet the preset requirements, the preset requirements comprise the process parameter requirements of the selected welding materials.
Therefore, different solder pastes have different technological parameter requirements, actual technological parameter data are compared with the technological parameters of the solder paste on the currently processed plate, and prompt information is sent, so that monitoring and judgment of welding conditions by workers are facilitated.
Drawings
Fig. 1 is a connection block diagram of an embodiment of the intelligent detection system for the temperature curve of the reflow furnace.
FIG. 2 is a schematic diagram showing the distribution of temperature measuring elements in an embodiment of the intelligent detection system for the temperature curve of the reflow furnace.
FIG. 3 is a flowchart of an embodiment of the method for intelligently detecting a temperature curve of a reflow furnace according to the invention.
The invention is further explained with reference to the drawings and the embodiments.
Detailed Description
Referring to fig. 1, fig. 1 is a connection block diagram of an embodiment of an intelligent detection system for a temperature curve of a reflow furnace according to the invention. Reflow oven temperature curve intelligent detection system includes data acquisition module 1, data processing module 2 and furnace temperature detector 3, and data acquisition module 1 is used for information data such as collection reflow oven furnace temperature, chain speed, sweep the sign indicating number and upload to data processing module 2, and furnace temperature detector 3 is used for obtaining the Board temperature data transmission of test PCBA (Printed Circuit Board + Assembly) to data processing module 2. The data processing module 2 is a terminal device with data processing capability, such as a computer or a smart phone. The furnace temperature detector 3 is an existing furnace temperature detecting device, such as a fifth generation intelligent furnace temperature tester KIC X5 manufactured by KIC corporation of usa.
The data acquisition module 1 comprises a main control unit 11, a temperature detection unit 12, a speed detection unit 13, a code scanning detection unit 14, an abnormal alarm unit 15 and a power supply 16. The main control unit 11 is a chip with data processing capability, such as a single chip or a DSP, the temperature detection unit 12 includes a plurality of temperature measurement elements and an analog-to-digital converter, the plurality of temperature measurement elements are connected to the analog-to-digital converter, the analog-to-digital converter is connected to the main control unit 11, and preferably, the temperature measurement elements are thermocouples. The main control unit 11 obtains the furnace temperature data sent by the temperature detection unit 12 and forwards the furnace temperature data to the data processing module 2.
The speed detection unit 13 is a speed sensor for detecting the chain speed of the conveying track on the reflow oven, and the main control unit 11 acquires the speed data sent by the speed detection unit 13 and forwards the speed data to the data processing module 2. The code scanning detection unit 14 comprises two code scanning devices which are respectively arranged at the transmission input end and the transmission output end of the reflow oven; the main control unit 11 obtains the code scanning data sent by the code scanning detection unit and forwards the code scanning data to the data processing module 2.
The abnormal alarm unit 15 comprises a three-color alarm lamp and a buzzer, when the data processing module 2 judges that the process parameters are abnormal, the data processing module can send signals to the main control unit 11, and the main control unit 11 sends control signals to the three-color alarm lamp and the buzzer.
Referring to fig. 2, fig. 2 is a schematic distribution diagram of temperature measuring elements in an embodiment of the intelligent detection system for temperature curves of the reflow furnace according to the invention. The X-axis forward direction is the conveying direction of the reflow furnace, the Y-axis direction is the width direction of the reflow furnace, the reflow furnace is provided with nine temperature zones in the X-axis forward direction, the nine temperature zones are respectively a first temperature zone 101, a second temperature zone 102, a third temperature zone 103, a fourth temperature zone 104, a fifth temperature zone 105, a sixth temperature zone 106, a seventh temperature zone 107, an eighth temperature zone 108 and a cooling zone 109, and the nine temperature zones are sequentially arranged between a conveying input end 111 and a conveying output end 112. A code scanning device 141 is provided at the delivery input 111 and a code scanning device 142 is provided at the delivery output 112.
The temperature detection unit 12 includes 26 temperature measurement elements 121, the temperature measurement elements 121 are disposed on both sides of the conveying rail 100 in the width direction, one temperature measurement element 121 located on the left side of the conveying rail 100 and one temperature measurement element 121 located on the right side of the conveying rail 100 are disposed at intervals, and the plurality of temperature measurement elements 121 do not overlap each other in a projection in the Y-axis direction. The plurality of temperature measuring elements 121 are sequentially connected in a forward direction along the X axis, and the connecting lines are Z-shaped on the conveying track 100. The first temperature zone 101 and the second temperature zone 102 are both provided with two temperature measuring elements 121, the third temperature zone 103 to the eighth temperature zone 108 are both provided with 3 temperature measuring elements, and the cooling zone 109 is provided with 4 temperature measuring elements. Taking the first temperature zone 101 as an example, the first temperature measuring element 121 is disposed on the left side of the first temperature zone 101 and located at 1/3 of the positive upward extending distance of the X-axis on the first temperature zone 101; the second temperature measuring element 121 is disposed on the right side of the first temperature range 101 and is located on the first temperature range 101 at a distance of 2/3 of the forward extension distance of the X axis. Taking the third temperature zone 103 as an example, the first temperature measuring element 121 is disposed on the left side of the third temperature zone 103 and located at 1/4 of the distance extending upward in the X-axis direction on the third temperature zone 103; the second temperature measuring element 121 is disposed on the right side of the third temperature range 103 and is located on the third temperature range 103 at a distance of 2/4 of the forward extension distance of the X-axis.
Referring to fig. 3, fig. 3 is a flowchart of an embodiment of the method for intelligently detecting a temperature curve of a reflow furnace according to the present invention. The reflow soldering furnace temperature curve intelligent detection method is applied to a data processing module 2 of a reflow soldering furnace temperature curve intelligent detection system, and the detection method realizes conversion calculation to obtain the actual temperature on a plate by detecting the temperature of air in the furnace.
First, step S1 is executed to generate test oven temperature curve data and test board temperature curve data. In the reflow furnace temperature curve intelligent detection system, data acquired by the data acquisition module 1 and data acquired by the furnace temperature detector 3 are transmitted to the data processing module 2. The furnace temperature detector 3 can be placed on the conveying track along with the test PCBA, test board temperature data of the test PCBA can be detected and obtained immediately, and after the test board temperature data are transmitted to the data processing module 2, the data processing module 2 generates test board temperature curve data related to the conveying distance.
Referring to fig. 2, 26 temperature measuring elements 121 are distributed between the two extending ends of the conveying track 100, the analog-to-digital converter can generate the test oven temperature data after acquiring the temperatures of the plurality of temperature measuring elements 121 and transmit the data to the main control unit 11, and after the main control unit 11 sends the plurality of test oven temperature data to the data processing module 2, the data processing module 2 generates the test oven temperature curve data related to the conveying distance.
Subsequently, the calculation step S2 is executed to generate conversion relation data. In step S1, the temperature curve data of the test oven and the temperature curve data of the test board are obtained, and the temperatures at the same distance value on the temperature curve data of the test oven and the temperature curve data of the test board are set to TFurnace with a heat exchangerAnd TBoardThen, there is formula 1: t isBoard＝K×TFurnace with a heat exchanger。
K is a function related to the physical parameters of the board (i.e. board parameter data for testing PCBA) and the physical parameters of the hot blast (test hot blast parameter data) K can be calculated by equation 2, K ═ F (u, L, ρ, η, λ, C)p) in formula 2, F is a conversion relation function, u is a hot air speed, L is a characteristic length of the PCB, rho is a hot air density, η is a hot air dynamic viscosity, lambda is a hot air heat conductivity coefficient, and finally C ispThe constant pressure heat capacity is hot air.
F can be calculated by equation 3: ═ Cmdt ═ Fz (t)2-t) dt; integrating 0-tau by the formulas on both sides of the middle number in formula 3And (6) solving the differential. Wherein C is the specific heat capacity of the PCB, m is the mass of the PCB, S is the surface area of the PCB, t2Is the temperature of the hot air, T is the temperature of the PCB (i.e. T)Board) And τ is time.
The invention aims to obtain T through acquisitionFurnace with a heat exchangerCombined with K to give TBoardK is related to a plurality of physical parameters of the plate and a plurality of physical parameters of the hot air, and thus, one plate temperature data T specific to actual use is calculatedBoardIt is necessary to calculate the temperature data T of the board by counting and analyzing known calculation parameters in a plurality of distance points or in a plurality of detections, for example, by a regression method or a neural network methodBoardAnd TFurnace with a heat exchangerAnd a plurality of physical parameters of the hot wind, namely conversion relation data.
Using regression method, using plate temperature TBoardAs a dependent variable y, and the furnace temperature TFurnace with a heat exchangerThe hot air speed, the characteristic length of the PCB, the hot air density, the hot air dynamic viscosity, the hot air heat conductivity coefficient, the hot air constant pressure heat capacity, the specific heat capacity of the PCB, the mass of the PCB, the surface area of the PCB and the hot air temperature are taken as a plurality of independent variables x1、x2、x3、···、xmAnd setting a regression equationWherein,for a hypothetical and known function that can be expressed in mathematical expressions,is an estimate of y, x, given by the above regression equation in case i1,iIs in case i x1Value of (a), xm,iThen x in case imby analogy, β1,...,βpFor each unknown, real (real) regression coefficient in the mathematical expression(regressioncoefficient[s]) P is a natural number,representing a real number set.
then using an optimization method to obtain beta1,...,βpIs optimized value b1,...,bpThereby minimizing
Where yi is the value of y in case i,as a monotonically increasing (monotonically increasing) function. The above optimization method may be a conventional calculus method, a genetic algorithm (genetic algorithm), or the like. Finally b is1,...,bpAnd substituting the regression equation into the required mathematical relation to obtain conversion relation data.
When calculating the mathematical relationship by using the neural network method, the plate temperature T is usedBoardAs output (output) y of the neural network, and with furnace temperature TFurnace with a heat exchangerThe speed of hot air, characteristic length of PCB, density of hot air, dynamic viscosity of hot air, coefficient of thermal conductivity of hot air, constant heat capacity of hot air, specific heat capacity of PCB, mass of PCB, surface area of PCB and temperature of hot air as multiple inputs of neural network])x1、x2、x3、···、xmAnd the neural network is set to a certain structure, and is assumed to be represented by the following equation:wherein,to be a function that is assumed and unknown and not necessarily capable of being expressed in mathematical expressions,is an estimate of y, x, given by the above equation in case i1,iIs in case i x1Value of (a), xm,iThen x in case imThe value of (c), and so on,for each unknown, real parameter (parameter s) in the neural network]) Initially will beAre respectively set asq is a natural number and q is a natural number,representing a real number set. And assume that there are one or more known activation functions(s) in the neural network])。
Then, supervised learning (i.e., x) is performed1,i1,x2,i1,...,xm,i1Inputting the neural network to obtainUpdatingMake it respectively intoThen x is put1,i2,x2,i2,...,xm,i2Inputting the neural network to obtainUpdatingMake it respectively intoRepeating the calculation by analogy, and calculating x1,in,x2,in,...,xm,inInput to a neural network to obtainUpdatingMake it respectively intoWherein { i1,...,inN }. The above updatesFor the purpose of minimizingWherein y isiIs the value of y in the individual case i,is a monotonically increasing function.
Finally, willSubstituting the parameters of the neural networkThe obtained neural network is the mathematical relation to be calculated, that is, when any x is input1、x2、x3、···、xmTo the neural network, the value of y output by the neural network is then compared with x1、x2、x3、···、xmThere is the above-mentioned mathematical relationship, i.e., the conversion relationship data, between the values of (a) and (b).
After obtaining the conversion relation data, step S3 is executed to obtain the real relation dataTime furnace temperature curve data TFurnace real timeReal-time hot air parameter data and plate parameter data about real-time plates. When the plate is actually processed, the physical parameters of the real-time plate are different from those of the PCBA, and the parameters of the test hot air are different from those of the real-time hot air, so that real-time parameter data of the production processing needs to be acquired. The positions of the 26 temperature measuring elements 121 are fixed, and the code scanning detection unit 14 and the speed detection unit 13 are arranged on the reflow oven, so that the time points when the product reaches each temperature measuring element 121 can be calculated, the time points are set to be a fixed time frequency data set, and the 26 temperature measuring elements 121 sequentially start to detect the temperature according to the time frequency data set, so that each temperature measuring element 121 is located at the closest position of the product during temperature measurement, and the data accuracy is improved.
Subsequently, step S4 is executed, and in step S3, real-time furnace temperature curve data T is acquiredFurnace real timeAfter the real-time hot air parameter data and the real-time plate parameter data are obtained, the real-time plate temperature data can be calculated according to the conversion relation data obtained in the step S2, and real-time plate temperature curve data, namely the temperature of the plate being processed, can be generated according to the plurality of plate temperature data at different conveying positions.
And then executing the step S5 to produce actual process parameter data according to the acquired real-time plate temperature curve data. The process parameter data comprises maximum temperature rise slope data, maximum temperature fall slope data, constant temperature (150-190 ℃) time data, backflow (the temperature is less than 213 ℃), highest temperature data, time data above 230 ℃ and the like, and the generated actual process parameter data is more beneficial to monitoring and judging the welding condition by workers.
Then, executing a judgment step S6, judging whether the actual process parameter data meets the preset requirement or not by the system, if so, executing a step S9, and displaying a comparison result; if the determination result is negative, step S7 is executed to display the comparison result, and then step S8 is executed to send a prompt. The preset requirements comprise process parameter data of a plate, process parameter data of a welding material or preset reference temperature curve data and the like, and specifically, the preset requirements are process parameter requirements of currently adopted solder paste; specifically, the sending of the prompt includes sending a prompt on a display screen window of the computer or sending a feedback signal to the main control unit 11, the main control unit 11 sends a control signal to the abnormality alarm unit 15 to send an alarm light or an alarm sound, and the data processing module 2 can output a signal for stopping the transport to the reflow oven.
Finally, it should be emphasized that the above-described embodiments are merely preferred embodiments of the present invention, and are not intended to limit the invention, as variations and modifications are possible to those skilled in the art, for example, a reflow oven having more than just 9 temperature zones, a method of arranging temperature measuring elements may be used in a reflow oven having, for example, 12 temperature zones plus one cooling zone, and other reflow ovens having multiple temperature zones and cooling zones. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The utility model provides a reflow oven temperature curve intelligent detection system which characterized in that includes:
the device comprises a data acquisition module, a furnace temperature tester and a data processing module;
the data acquisition module comprises a main control unit and a temperature detection unit;
the temperature detection unit comprises a plurality of temperature measurement elements which are fixedly arranged in the reflow furnace along the conveying direction;
the main control unit acquires furnace temperature data sent by the temperature detection unit and forwards the furnace temperature data to the data processing module;
and the furnace temperature tester outputs plate temperature data to the data processing module.
2. The reflow oven temperature curve intelligent detection system of claim 1, characterized in that:
the data acquisition module also comprises a speed detection unit;
and the main control unit acquires the speed data sent by the speed detection unit and forwards the speed data to the data processing module.
3. The reflow oven temperature curve intelligent detection system of claim 2, characterized in that:
the data acquisition module further comprises a code scanning detection unit, the code scanning detection unit comprises code scanning equipment, and the two code scanning equipment are respectively arranged at the transmission input end and the transmission output end of the reflow oven;
and the main control unit acquires the code scanning data sent by the code scanning detection unit and forwards the code scanning data to the data processing module.
4. The reflow oven temperature curve intelligent detection system of claim 3, characterized in that:
the data acquisition module further comprises an abnormity alarm unit, and the main control unit outputs a control signal to the abnormity alarm unit.
5. The reflow oven temperature curve intelligent detection system of any one of claims 1 to 4, characterized in that:
the plurality of temperature measuring elements are arranged on the left side and the right side of the conveying track in the reflow furnace.
6. An intelligent detection method for a reflow oven temperature curve is characterized in that the method is applied to the data processing module of the intelligent detection system for the reflow oven temperature curve of any one of the claims 1 to 5;
the intelligent detection method for the temperature curve of the reflow oven comprises the following steps:
generating test furnace temperature curve data according to the obtained test furnace temperature data, and generating test board temperature curve data related to the test PCBA according to the obtained test board temperature data;
generating conversion relation data according to the test furnace temperature curve data, the test board temperature curve data, the board parameter data of the test PCBA and the test hot air parameter data;
and generating real-time furnace temperature curve data according to the acquired real-time furnace temperature data, and generating real-time plate temperature curve data related to the real-time plate according to the real-time furnace temperature curve data, the conversion relation data, the plate parameter data of the real-time plate and the real-time hot air parameter data.
7. The reflow oven temperature curve intelligent detection method of claim 6, characterized in that:
after the generating of the conversion relation data, the method further includes:
and acquiring the real-time furnace temperature data according to a preset time frequency.
8. The reflow oven temperature curve intelligent detection method of claim 6, characterized in that:
after the real-time board temperature curve data is generated, the method further comprises the following steps:
and generating actual process parameter data according to the real-time board temperature curve data, wherein the actual process parameter data comprises maximum temperature rise slope data, maximum temperature fall slope data, constant temperature time data, backflow time data and maximum temperature data.
9. The reflow oven temperature curve intelligent detection method of claim 8, characterized in that:
after generating actual process parameter data according to the real-time plate temperature curve data, the method further comprises the following steps:
and judging whether the actual process parameter data meet the preset requirements, and if not, generating prompt information.
10. The reflow oven temperature curve intelligent detection method of claim 9, characterized in that:
and when judging whether the actual process parameter data meets the preset requirements, the preset requirements comprise the process parameter requirements of the selected welding materials.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112163693A (en) * | 2020-08-27 | 2021-01-01 | 福建摩尔软件有限公司 | Control and optimization method, device, equipment and medium for reflow soldering process |
| CN112163694A (en) * | 2020-08-27 | 2021-01-01 | 福建摩尔软件有限公司 | Method, apparatus, device and medium for monitoring and predicting quality of reflow soldering product |
| CN112161717A (en) * | 2020-08-27 | 2021-01-01 | 福建摩尔软件有限公司 | Method, device, equipment and medium for automatically drawing temperature curve of reflow furnace |
| CN112183930A (en) * | 2020-08-27 | 2021-01-05 | 福建摩尔软件有限公司 | Method, device, equipment and medium for counting production orders of reflow soldering products |
| CN112632856A (en) * | 2020-12-21 | 2021-04-09 | 江苏警官学院 | Conveyor belt speed and temperature control method of reflow furnace |
| CN112650186A (en) * | 2021-01-11 | 2021-04-13 | 伟创力电子技术(苏州)有限公司 | Control method for automatic plate lowering of reflow oven front rail |
| CN112872527A (en) * | 2021-01-12 | 2021-06-01 | 山东师范大学 | Welding method and system based on central temperature prediction curve of reflow soldering area |
| CN112872522A (en) * | 2021-01-11 | 2021-06-01 | 武汉倍普科技有限公司 | Reflow furnace temperature curve intelligent detection system |
| WO2021219024A1 (en) * | 2020-04-29 | 2021-11-04 | 福迪威（上海）工业仪器技术研发有限公司 | Apparatus and method for measuring temperature of object in space |
| CN114444435A (en) * | 2022-01-07 | 2022-05-06 | 苏州浪潮智能科技有限公司 | PCB reflow furnace temperature control method, system, terminal and storage medium |
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| CN112163694A (en) * | 2020-08-27 | 2021-01-01 | 福建摩尔软件有限公司 | Method, apparatus, device and medium for monitoring and predicting quality of reflow soldering product |
| CN112161717A (en) * | 2020-08-27 | 2021-01-01 | 福建摩尔软件有限公司 | Method, device, equipment and medium for automatically drawing temperature curve of reflow furnace |
| CN112183930A (en) * | 2020-08-27 | 2021-01-05 | 福建摩尔软件有限公司 | Method, device, equipment and medium for counting production orders of reflow soldering products |
| CN112163693A (en) * | 2020-08-27 | 2021-01-01 | 福建摩尔软件有限公司 | Control and optimization method, device, equipment and medium for reflow soldering process |
| CN112632856B (en) * | 2020-12-21 | 2023-09-19 | 江苏警官学院 | Method for controlling speed and temperature of conveyor belt of reflow oven |
| CN112632856A (en) * | 2020-12-21 | 2021-04-09 | 江苏警官学院 | Conveyor belt speed and temperature control method of reflow furnace |
| CN112650186A (en) * | 2021-01-11 | 2021-04-13 | 伟创力电子技术(苏州)有限公司 | Control method for automatic plate lowering of reflow oven front rail |
| CN112872522A (en) * | 2021-01-11 | 2021-06-01 | 武汉倍普科技有限公司 | Reflow furnace temperature curve intelligent detection system |
| CN112872527A (en) * | 2021-01-12 | 2021-06-01 | 山东师范大学 | Welding method and system based on central temperature prediction curve of reflow soldering area |
| CN112872527B (en) * | 2021-01-12 | 2022-12-09 | 山东师范大学 | Welding method and system based on central temperature prediction curve of reflow soldering area |
| CN114444435A (en) * | 2022-01-07 | 2022-05-06 | 苏州浪潮智能科技有限公司 | PCB reflow furnace temperature control method, system, terminal and storage medium |
| CN114444435B (en) * | 2022-01-07 | 2023-11-03 | 苏州浪潮智能科技有限公司 | PCB reflow soldering furnace temperature control method, system, terminal and storage medium |
| CN118168675A (en) * | 2023-12-26 | 2024-06-11 | 苏州云造智能科技有限公司 | AI intelligent monitoring system of reflow oven |
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