Screen Printer Control Parameter Generation Using Simulation
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Solution Overview
Problem
Screen printers often print either too much or too little solder paste on printed circuit board pads or place it at inappropriate positions due to inadequate control parameters, and ambient conditions like temperature and humidity can lead to defects in the solder printing process.
Innovation Solution
An apparatus and method using a simulation model trained to predict solder paste states, combined with optimization, search, and machine learning-based reinforcement learning algorithms to generate control parameters for the screen printer, which adjusts parameters like squeegee blade pressure, speed, and stencil separation speed to improve printing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If control parameters of the screen printer are not optimized, then the printing process is simple and fast, but the manufacturing precision of solder paste deposition is poor
Solution Approach 1:
The system performs preliminary simulation and prediction of solder paste printing outcomes before actual printing. The simulation model predicts printing results based on control parameters, and the inspection information is analyzed in advance to determine optimal parameters, preventing defects before they occur.
Solution Approach 2:
The patent creates a virtual copy of the printing process through simulation models. Virtual solder pastes are generated by applying control parameters to the simulation model, allowing prediction and optimization of actual printing outcomes without performing the physical printing process repeatedly.
2Manufacturing precision
If multiple algorithms are used to generate control parameters, then the manufacturing precision improves, but the productivity decreases due to complex calculations
Solution Approach 1:
The system segments the parameter generation process into distinct algorithmic components. Multiple algorithms (optimization, search, and reinforcement learning) generate candidate parameters independently, which are then evaluated and selected, allowing parallel processing and efficient resource utilization.
Solution Approach 2:
The system dynamically changes and adjusts control parameters based on simulation results and inspection information. The reinforcement learning algorithm continuously optimizes parameters by learning from predicted outcomes, adapting the parameter set to achieve optimal printing precision.
3Reliability
If control parameters do not account for ambient conditions, then the operation is simple, but the reliability of solder paste printing deteriorates
Solution Approach 1:
The system incorporates feedback loops where inspection information from actual printing is analyzed, compared with simulation predictions, and used to adjust control parameters. This closed-loop feedback ensures that ambient condition variations are compensated for, maintaining consistent printing quality.
Solution Approach 2:
The control parameters are made dynamic rather than static. The system continuously adapts parameters based on real-time inspection information and simulation predictions, allowing the printing process to respond to changing ambient conditions and maintain reliability.
Data Source
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AI summary
An apparatus, a recording medium, and a method for generating a control parameter of a screen printer (110) are disclosed. The apparatus includes a memory (122) that stores a simulation model configured to derive predictive inspection information on a printed state of solder paste based on a plurality of control parameters of the screen printer; a communication circuit (123) configured to receive first inspection information on a plurality of solder pastes printed by the screen printer based on a first control parameter, and a processor (124) electrically connected to the memory and the communication circuit. The processor obtains first predictive inspection information by applying the first control parameter to the simulation model, generates a plurality of candidate control parameters based on the first predictive inspection information, determines a plurality of second control parameters among the candidate control parameters, and transmits the plurality of second control parameters to the screen printer via the communication circuit.