Reflow Oven Research Articles

Sustainable energy conservation of reflow oven process on PCB manufacturing using AHP techniques

ABSTRACT This research aims to reduce electricity use and promote activities to reduce electricity use within the organisation through a case study from the PCB assembly department. It was found that the total annual electricity consumption is approximately 2,099,093 kWh/y. The amount of PCB production per time is 7 sheets, and the delay time to release PCB sheets is 15 s per sheet. Therefore, the researcher used the AHP techniques to identify the problems. The solution is to introduce innovative insulation wrapping that can help reduce heat radiation from the machine. From the experiment, it was found that after the improvement, the production number increased to 11 sheets, with a delay time of 10 s per sheet. Production increased by 33.3%, heat loss decreased by 53.5%. As a percentage per power dropped 51% and the percentage of the electricity cost was 70.1% the payback period was 1.69 years.

Properties of Mixing SAC Solder Alloys with Bismuth-Containing Solder Alloys for a Low Reflow Temperature Process

The subject of extensive research has been the establishing of lower temperature soldering of electronic assemblies that are similar to the once common yet still preferred eutectic Tin-Lead (SnPb) soldering processes that are below 217˚C. This research measured and compared the mixing mechanisms of paste and ball dissolution. Mixed soldered assemblies will have different dissolution results that are dependent on the peak reflow temperature. The hypothesis for this experiment was to determine the relationship of various times above the melting point of low temperature solders containing Bismuth. The results measured are from solder joints of both SAC305 and Bismuth containing solders formed at lower process temperatures compared to the standard high temperatures which peak at 230˚C-260˚C. The Bismuth containing solders evaluated start with the highest to lowest weight percentage of Bismuth, the eutectic 58Bismuth/42Tin (58Bi/42Sn), 57Bi/42Sn/1Ag and a propriety alloy that has a lower Bismuth content along with various micro alloys, defined as 40-58Bi/Sn/X (X representing proprietary micro alloys or doping). The assembly was with an 18mil 96.5Tin/3.0Silver/0.5Copper (SAC305) solder ball mixed with each solder paste. These solder alloys were exposed to three different peak temperatures 180˚C, 195˚C, 205˚C. Another reflow profile attribute of focus was times above 138˚C - the melting point of the eutectic Sn58Bi alloy, this term used throughout the study is referred to Time Above Melting (TAM). The ball and paste assembly used the times above melting of 120sec and 240sec to represent process extremes and verify their significance on improving mixing level results. The average mixing % levels were recorded and compared for each solder joint combination to test the theory that the solder paste solidifies during the time above melting (TAM) within the reflow oven. [1] This experiment effectively demonstrates that the reflow profile parameter for time above melting has limited effectiveness in additionally mixing the paste with the BGA solder ball. Optimization of mixing percentages will need to be achieved by optimizing the paste and ball volumes.

Decoupling of Temperature and Strain Effects on Optical Fiber-Based Measurements of Thermomechanical Loaded Printed Circuit Board Assemblies.

This study investigated the use of distributed optical fiber sensing to measure temperature and strain during thermomechanical processes in printed circuit board (PCB) manufacturing. An optical fiber (OF) was bonded to a PCB for simultaneous measurement of temperature and strain. Optical frequency-domain reflectometry was used to interrogate the fiber optic sensor. As the optical fiber is sensitive to both temperature and strain, a demodulation technique is required to separate both effects. Several demodulation techniques were compared to find the best one, highlighting their main limitations. The importance of good estimations of the temperature sensitivity coefficient of the OF and the coefficient of thermal expansion of the PCB was highlighted for accurate results. Furthermore, the temperature sensitivity of the bonded OF should not be neglected for accurate estimations of strains. The two-sensor combination model provided the best results, with a 2.3% error of temperature values and expected strain values. Based on this decoupling model, a methodology for measuring strain and temperature variations in PCB thermomechanical processes using a single and simple OF was developed and tested, and then applied to a trial in an industrial environment using a dynamic oven with similar characteristics to those of a reflow oven. This approach allows the measurement of the temperature profile on the PCB during oven travel and its strain state (warpage).

Optimization Method for Hot Air Reflow Soldering Process Based on Robust Design

The process design of hot air reflow soldering is one of the key factors affecting the quality of PCBA (Printed Circuit Board Assembly) component products. In order to improve the product quality during the design process, this paper proposes a robust optimization-based finite element simulation analysis method including significant influencing factor screening, robustness evaluation, robust optimization, and reliability verification for the reflow soldering process. The simulation model of the reflow soldering process temperature field based on experiments is constructed and validated. Sensitivity analysis is used to select important influencing factors, such as the last five set temperature zones (T5 to T9) in the reflow oven and the thermal properties of materials such as PCBs (printed circuit boards), BGAs (ball grid arrays), and solder paste, as well as noise factors like the heating environment during the soldering process. Several surrogate models are used to construct the response surface, and the optimal fitting scheme is selected to effectively avoid poor fitting caused by inappropriate surrogate models. The 6σ robust optimization approach is introduced to evaluate and optimize the robustness of the process design parameter where the heating factor is chosen as the optimization target. The reliability analysis method is employed to validate the product quality. This paper establishes a comprehensive robustness analysis method for hot air reflow soldering, effectively reducing design costs and addressing the lack of robustness analysis in the current hot air reflow soldering process design.

Integrated Approach to Obtain Gas Flow Velocity in Convection Reflow Soldering Oven

The nozzle-matrix gas flow velocity has a great influence on the accuracy of the temperature field of a printed circuit board assembly (PCBA) during the hot air convection reflow soldering process. This paper proposes a new approach that integrates the theoretical calculation, numerical simulation and an experimental test to accurately determine the nozzle-matrix gas flow velocity. First, the temperature profile of the aluminum alloy thin plate in convection reflow ovens is measured using a Wiken tester. Second, the nozzle-matrix gas flow velocity is theoretically calculated with the Martin formula. The computational fluid dynamic (CFD)simulation is performed according to the Icepak code, where a single oven chamber model is established to represent the 10 zones of soldering ovens to reduce computational resources considering the supry of the soldering ovens. The simulated temperature profile of the aluminum alloy thin plate is obtained and the specific response surface model (RSM) is established to represent the deviation between the simulated temperature and the measured temperature. Finally, based on reverse problem analysis, non-linear programming by quadratic Lagrangian (NLPQL) is used to solve the mathematical optimization model with the objective of minimizing the temperature deviation to obtain the corrected nozzle-matrix gas flow velocity. To validate the accuracy, the temperature test and the modeling using the corrected gas flow velocity for a new PCBA component for the soldering ovens is conducted separately. The temperature comparison between the simulation and the test shows that the maximum temperature deviation is within 10 °C. This provides evidence that the nozzle-matrix gas flow velocity obtained by the new approach is accurate and effective.

Reflow profiling with the aid of machine learning models

PurposeThis paper aims to propose a method to quickly set the heating zone temperatures and conveyor speed of the reflow oven. This novel approach intensely eases the trial and error in reflow profiling and is especially helpful when reflowing thick printed circuit boards (PCBs) with bulky components. Machine learning (ML) models can reduce the time required for profiling from at least half a day of trial and error to just 1 h.Design/methodology/approachA highly compact computational fluid dynamics (CFD) model was used to simulate the reflow process, exhibiting an error rate of less than 1.5%. Validated models were used to generate data for training regression models. By leveraging a set of experiment results, the unknown input factors (i.e. the heat capacities of the bulkiest component and PCB) can be determined inversely. The trained Gaussian process regression models are then used to perform virtual reflow optimization while allowing a 4°C tolerance for peak temperatures. Upon ensuring that the profiles are inside the safe zone, the corresponding reflow recipes can be implemented to set up the reflow oven.FindingsML algorithms can be used to interpolate sparse data and provide speedy responses to simulate the reflow profile. This proposed approach can effectively address optimization problems involving multiple factors.Practical implicationsThe methodology used in this study can considerably reduce labor costs and time consumption associated with reflow profiling, which presently relies heavily on individual experience and skill. With the user interface and regression models used in this approach, reflow profiles can be swiftly simulated, facilitating iterative experiments and numerical modeling with great effectiveness. Smart reflow profiling has the potential to enhance quality control and increase throughput.Originality/valueIn this study, the employment of the ultimate compact CFD model eliminates the constraint of components’ configuration, as effective heat capacities are able to determine the temperature profiles of the component and PCB. The temperature profiles generated by the regression models are time-sequenced and in the same format as the CFD results. This approach considerably reduces the cost associated with training data, which is often a major challenge in the development of ML models.

Temperature Curve of Reflow Furnace Based on Newton’s Law of Cooling

Soldering in a reflow oven is an important and efficient technical means to produce integrated circuit boards. The key to the quality of integrated circuit boards lies in the furnace temperature curve. In this paper, Newton’s law of cooling is used to establish the mechanism model of the temperature of each zone of the furnace and the curve of furnace temperature, which can reduce the number of experiments in actual production and obtain a better furnace temperature curve, thus improving production efficiency. Finally, several concrete examples are given to discuss and solve some common problems in the industry.

Automation of a PCB Reflow Oven for Industry 4.0

With the rise of Industry 4.0, its pillars (which include Internet of Things, “Big Data”, data analytics, augmented reality, cybersecurity, etc.) have become unavoidable tendencies for the automated manufacturing industry. Equipment upgrade is required to match the new standards of digitally assisted automation. However, not all factories in the medium to small range (or independent manufacturers) can afford to upgrade their equipment. Therefore, the availability of affordable Industry 4.0 upgrades for now-outdated devices is necessary for manufacturers in the SME range (Small-Medium Enterprises) to stay relevant and profitable. More specifically, this work revolves around the automation of printed circuit board (PCB) manufacturing, which is one of the most popular and profitable areas involved in this movement; and within it, the large majority of manufacturing defects can be traced to the soldering or “reflow” stage. Manufacturing research must, thus, aim towards improving reflow ovens and, more specifically, aim to improve their autonomous capabilities and affordability. This work presents the design and results of a controlling interface utilizing a Raspberry Pi 4 as a coupling interface between an MQTT Broker (which monitors the overall system) and the oven itself (which is, intentionally, a sub-prime model which lacks native IoT support), resulting in successful, remote, network-based controlling and monitoring of the oven. Additionally, it documents the design and implementation of the network adaptations necessary for it to be considered a cybersecure IIoT Module and connect safely to the Production Cell’s Subnet. All of this to address the inclusion of specific Industry 4.0 needs such as autonomous functioning, data collection and cybersecurity in outdated manufacturing devices and help enrich the processes of SME PCB manufacturers.

Reflow Recipe Establishment Based on CFD-Informed Machine Learning Model

This article proposes a computational fluid dynamics-based machine learning (ML) model to set the temperature of a convection reflow oven and control conveyor speed. Due to the thermal mass variation of printed circuit boards (PCB) and surface mount components, the reflow recipe should be specified for each PCB assembly individually. Instead of relying on process engineers’ skills and experience, the machine learning model supported by heat transfer mechanisms provides a clear and logical path in the reflow recipe setup. An ideal recipe should let the largest component reach the minimum reflow temperature, meanwhile, the temperature cannot damage the small or heat-sensitive components. To simulate the reflow soldering process, the computational fluid dynamics (CFD) model was used after being validated with experiment results. A system was programmed to feed the output of the CFD model as the input to the ML model. Benefiting from the interpolation and fast response brought by ML models, we can rapidly obtain the results subjected to millions of recipes, which are the combination of preset temperatures of all the heating zones and the conveyor speed in the oven. Using a weighting filter function, the optimal recipe can be obtained. This methodology is especially beneficial to reflowing smaller batches of complex PCB assemblies with a big heat capacity.

Smarter Temperature Setup for Reflow Oven to Minimize Temperature Variation Among Components

The thermomechanical reliability of surface mount components (SMCs) is closely related to the reflow soldering process. With the rise of board complexity, an optimal reflow profile must consider the mix of large and small components, the density of the components on the board, and the board dimensions. It has been a significant challenge to achieve uniform temperature on SMCs with different sizes and different thermal masses, such as capacitors, transistors, quad-flat no-leads (QFN), and ball grid array (BGA) packages. To reflow large components such as BGA packages, the minimum reflow temperature should be reached, meanwhile, the temperature should not exceed the damaging temperature of the smaller or heat-sensitive components. The recommended reflow profile is defined as a band or process window. To achieve good quality soldering, the temperature applied to the solder under varied sizes of components should be within the recommended band. This article introduces a numerical method based on a compact model to predict the reflow profile for bulky BGA packages. The impinging gas temperature is used as the boundary condition to predict the temperature distribution throughout a board with different sized components. The proper oven temperature setting to achieve the ideal profiles is obtained finally.

An intelligent system for reflow oven temperature settings based on hybrid physics-machine learning model

PurposeThis paper aims to provide the proper preset temperatures of the convection reflow oven when reflowing a printed circuit board (PCB) assembly with varied sizes of components simultaneously.Design/methodology/approachIn this study, computational fluid dynamics modeling is used to simulate the reflow soldering process. The training data provided to the machine learning (ML) model is generated from a programmed system based on the physics model. Support vector regression and an artificial neural network are used to validate the accuracy of ML models.FindingsIntegrated physical and ML models synergistically can accurately predict reflow profiles of solder joints and alleviate the expense of repeated trials. Using this system, the reflow oven temperature settings to achieve the desired reflow profile can be obtained at substantially reduced computation cost.Practical implicationsThe prediction of the reflow profile subjected to varied temperature settings of the reflow oven is beneficial to process engineers when reflowing bulky components. The study of reflowing a new PCB assembly can be started at the early stage of board design with no need for a physical profiling board prototype.Originality/valueThis study provides a smart solution to determine the optimal preset temperatures of the reflow oven, which is usually relied on experience. The hybrid physics–ML model providing accurate prediction with the significantly reduced expense is used in this application for the first time.

Comparison of intermetallic compound growth and tensile behavior of Sn-3.0Ag-0.5Cu/Cu solder joints by conventional and microwave hybrid heating

Microwave hybrid heating (MHH) is a possible approach for soldering; yet, the long-term reliability of solder joints is still unclear. In this work, a comparative study was carried out on the influence of isothermal aging on SAC305/Cu solder joints by conventional reflow and MHH. The conventional reflow oven was set to 260 °C at 60 s holding time, and MHH was carried out at high operating power at 40 s. The reflowed sample was isothermally aged at 150 °C for various aging times (0, 240, 480 and 960 h) in a laboratory oven. The conventional reflow and MHH transformed the IMC structure from scallop-like to a planar-like Cu6Sn5 with a linear increase in thickness and a thin layer of Cu3Sn was observed at 240 h of aging. While Ag3Sn particles grew larger at longer aging time. The MHH shows a thinner Cu6Sn5 layer, and a smaller grain size compared to conventional reflow. The ultimate tensile strength (UTS) for conventional reflow (32.7 MPa) is 26.8% lower compared to MHH (44.7 MPa). The UTS for MHH is greater than conventional reflow as the isothermal aging time increases.

Research on Furnace Temperature Curve Control Based on Genetic Algorithm

Electronics are widely used in everyday life, and in the production of electronic products various electronic components need to be soldered to printed circuit boards. With the miniaturization of electronic components, traditional soldering methods cannot be completed. Reflow soldering technology can be used to complete the printing of tiny electronic components, and in this process, it is critical to the quality of the product to maintain the proper process temperature for each part of the reflow soldering. In this paper, how to achieve temperature control in this process is studied and solved by establishing a mathematical model about differential equations. First, by analyzing the heat transfer of air in the reflow oven, it is found that the air in the oven satisfies the heat transfer equation. Secondly, the temperature of the small temperature zone in the base data is used to obtain the temperature distribution in the furnace at steady state. Again, the temperature of the temperature zone and the speed of the conveyor belt are determined so as to achieve the minimum target area. By constructing the model of genetic algorithm, the area minimization is used as the fitness function. Finally, in this paper, we make the images on both sides from 217°C to the peak temperature as symmetrical as possible. For this problem, we can still build the fitness function by genetic algorithm to solve it.

PCB board heat transfer model based on heat transfer mechanism analysis

Temperature control plays a vital role in the circuit board production process. In order to control the temperature change in the reflow oven, we establish a differential equation model of the PCB heat transfer based on the theory of heat transfer and use the boundary conditions to solve the temperature distribution in the oven. We also accurately simulate the heating and cooling process of the PCB. According to the requirements for the shape of the oven temperature curve in actual production, we respectively use the heating factor and the area enclosed by the temperature as evaluation indicators, establish a target optimization model, and obtain the best set temperature for each temperature zone and the best transmission speed of PCB board. Through analysis of different cases, we could get the conclusion that the model in this paper is reasonable and effective.

Temperature Modeling of Reflow Soldering Based on the Heat Transfer Mechanisms

In the production of integrated circuit boards, electronic components need to be automatically welded to the circuit board by a reflow oven. Reflow oven welding is the core process of producing circuit boards, and the centre temperature of the welding area is extremely important to the quality of the final product of electronic components. Based on the experimental data, we establish a non-steady heat conduction model based on Newton's cooling law, and use the method of least squares to determine the dynamic parameters, and finally use the finite difference method (FDM) to solve the models.Secondly, the reflow oven speed will also have an impact on the product quality. At the same time, a single-objective linear programming model with the maximum furnace speed is established, then the step size is to perform a fine search to solve the problem.

Investigations of Infrared Desktop Reflow Oven with FPCB Substrate during Reflow Soldering Process

This paper presents the study of infrared (IR) reflow oven characteristics for suitable operating conditions of the flexible printed circuit board (FPCB) in the reflow soldering process. A computer-based model that imitates a real-time oven was developed with practical boundary conditions. Since the radiation effect is dominant in the reflow process, a discrete ordinate (DO) model was selected to simulate the effect. The experimental work acts as a benchmark and the reflow profile was set to follow the standards of JSTD-020E. The simulation of the model has a great consensus between the experimental data. It was found that the temperature distribution was inhomogeneous along with the phases. The FPCB surface also has a higher surface temperature than oven air during the operating reflow profile. An in-depth study using the simulation approach reveals that the temperature distribution of the desktop reflow oven is dependent on several factors, namely fan speed, FPCB position, and FPCB thickness. The rotational fan generates an unsteady flow that induces inhomogeneous temperature at different positions in the reflow oven cavity. The results are useful for studying further improvements to achieve temperature uniformity within the oven chamber.

Effects of the Solder Phase Transformation on the Optimization of Reflow Soldering Parameters and Temperature Profiles

In this study, a heat convection model of the reflow oven and a heat conduction model of the soldering area are proposed based on heat transfer theory, and a dynamic Thomas algorithm is developed for solving linear equations with coefficient matrix evolving over time in the tridiagonal system, which is derived from a heat transfer problem with moving boundaries in the solder phase transition process. We have also carried out numerical simulations for investigating the accuracy of the mathematical model, in which the temperature profiles are calculated and compared for different cases with considering or ignoring phase transformations, respectively. Parameters of reflow soldering, such as the conveyor speed, the set temperature in each zone, and a part of the heating factor, are optimized by the use of the nondominated sorting genetic algorithm II. By comparing the temperature profile and optimal solutions in the two cases, numerical results show that phase transitions of the solder have great impacts on optimal parameters and the slope of temperature profiles. Moreover, the phenomenon that the heating factor varies with the maximum set temperature in a banded distribution is investigated and analyzed, which is an important part of this work.

ANALYSIS OF MACHINE AVAILABILITY AT SURFACE-MOUNT TECHNOLOGY (SMT) LINE USING WITNESS SIMULATION

In the competition to excel in the Industrial 4.0 race, companies especially in Semiconductor Industry, strive to attain efficiency by removing process bottlenecks, improving productivity and increasing machine availability during operation. As so, this research was conducted to analyze the machine’s availability based on various preventive maintenance (PM) scheduling. A model was developed in WITNESS 14 Manufacturing Performance Edition software to imitate the Surface-mount Technology (SMT) line in a manufacturing system. The model was build based on a serial layout of five machines which are a screen printer, glue dispenser, chip shooter, pick and place and reflow oven. Then, the simulation was run to study the availability of each machine with relation to different stoppages intervals and duration based on the selected PM schedule. The findings of this project show that PM for an SMT line should be carried out every week with 30 minutes PM duration to achieve high machine availability and minimal machine downtime.

Heat Transfer Modeling and Oven Temperature Curve Optimization of Integrated Circuit Board Reflow Soldering

In order to use mechanism model analysis instead of experimental test to control and adjust the temperature and oven temperature curve of each part of the integrated circuit board reflow oven to make it meet the process requirements, a fitting algorithm was used to establish a heat transfer model of the reflow soldering. The established reflow soldering heat transfer model was used to simulate and fit to obtain the integrated circuit board reflow soldering oven temperature curve, and compared it with the reflow soldering experiment curve to verify the reliability of the model’s simulation performance. The comparison results show that the reflow soldering oven temperature simulation curve is in good agreement with the experimental curve, and it can replace the experimental test to find the best. On this basis, the genetic algorithm was further used to optimize the temperature setting and oven temperature curve for integrated circuit boards. After optimization, the conveyor belt velocity of the integrated circuit board is preferably 1.4799cm/s, and the set temperature of temperature zones is 179 °C at small temperature zones 1~5, 185 °C at small temperature zone 6, 235 °C at small temperature zone 7, and 264 °C at small temperature zones 8~9; the area of the oven temperature curve that exceeds 217 °C decreases by $831.1611~^{\circ }\textrm {C}\cdot \text{s}$ , and its symmetry with respect to the peak is better, which is much closer to the ideal oven temperature curve. The research results provide a useful reference for the design of integrated circuit board reflow soldering process.

Research forecasting model to solve the problem of marketing needs of enterprises

This essay focuses on the temperature variation law of the center of the target board soldering area at a certain position when the reflow oven starts working as the core discussion. Using the principle of heat conduction, Fourier's law to solve the problem of temperature values and temperature change laws in the center of the soldering area of the target circuit board at different locations under set conditions. As in the actual process, the temperature change in the center of the soldering area is limited to certain conditions, we can use the target planning, prediction methods, to build a model to solve the maximum problem. At the same time we need to consider the quality of the target circuit board, the use of neural networks, genetic algorithms to establish optimization models for target optimization, and ultimately the optimal furnace temperature curve.