Constant Table Setup Optimization for Automatic Placement Lines
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Solution Overview
Problem
Current methods for determining setups for constant tables in automatic placement machines are time-consuming and often result in unsatisfactory outcomes, relying on manual calculations and trial-and-error approaches by production schedulers.
Innovation Solution
The method employs mixed integer linear optimization using input data such as table locations, component placement positions, and cycle times to calculate setups for constant tables, allowing for the determination of optimal setup groups and reducing changeover resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error methods are used to determine setups for constant tables, then production schedulers can rely on experience-based strategies, but the time requirement becomes very high and results are often not satisfactory
Solution Approach 1:
The patent replaces manual trial-and-error methods with a mathematical optimization model (mixed integer linear programming). The system automatically calculates optimal setups for constant tables using objective functions and constraints, eliminating the need for production schedulers to manually iterate through possibilities. This substitution of mechanical/manual processes with automated computational methods directly reduces time requirements while improving accuracy.
Solution Approach 2:
The patent transforms the setup determination problem from a qualitative experience-based process into a quantitative optimization problem. By defining objective functions (minimizing number of setup groups, minimizing cycle time) and constraints (table capacity, component requirements), the system changes the parameters from subjective scheduler judgment to objective mathematical variables that can be optimized automatically.
2Adaptability or versatility
If more setup groups are created to accommodate all lots, then all components can be manufactured, but the changeover resource requirement increases and productivity decreases
Solution Approach 1:
The patent makes constant tables universal by determining their setups in advance to serve multiple setup groups. The optimization model identifies components that can be accommodated on constant tables across different setup groups, allowing these tables to perform multiple functions rather than being dedicated to single setup groups. This reduces the total number of setup groups needed while maintaining manufacturing coverage.
Solution Approach 2:
The patent performs preliminary determination of constant table setups before production begins. By calculating optimal setups in advance using the optimization model, the system prepares the constant tables to handle multiple components across different setup groups, eliminating the need for frequent changeovers during production and thereby increasing throughput.
3Ease of manufacture
If constant tables are used with fixed setups for all setup groups, then changeover resource requirement is reduced and setup equipment is saved, but the ability to accommodate all components may be limited
Solution Approach 1:
The patent optimizes the parameters of constant table setups (which components are placed on which tables) using mathematical programming. By adjusting these parameters within the optimization model, the system finds configurations that maximize component accommodation capability while maintaining the constant table advantage of reduced changeovers. The constraints in the model ensure that adaptability requirements are met.
Solution Approach 2:
While constant tables have fixed physical positions, the patent introduces dynamics into the setup determination process. The optimization model dynamically assigns different components to constant tables based on production requirements, creating flexible logical configurations rather than rigid physical constraints. This allows constant tables to adapt to different production scenarios without requiring physical reconfiguration.
Data Source
AI summary
A method determines setups for constant tables of automatic placement machines in placement lines at predetermined table locations by mixed integer linear optimization based on input data describing the placement infrastructure and input parameters that can be specified by the operator or user. The method can be used regardless of the mounting technique used (for example, plug in mounting technique, surface mounting technique, or a hybrid technique). The method can be advantageously used with other methods, for example, cluster methods for forming setup groups or line balancing for cycle time optimization.