Process time variance is frequently assessed in assembly line balancing research, a crucial production planning step. However, it has been largely overlooked in planning of highly automated car body construction assembly lines, owing to the deterministic nature of robots. Resistance spot welding, the most prevalent joining technique, employs feedback control systems for quality assurance, leading to varying process times. When multiple welds are done per cycle, these variations lead to fluctuations in overall cycle times. A quantitative analysis of 16 assembly lines reveals a 0.6 s median cycle time range. By incorporating process time variance in the planning of car body construction, this cycle time variance can be controlled to mitigate effects on assembly line performance. However, modelling of process times is required because these cannot be known in advance. As simulations on the balancing of car body construction assembly lines indicate, the model accuracy, however, needs to be high. Inaccurate distributional shape and spread predictions cause deficient planning assumptions, resulting in unpredictable outcomes.

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Resistance Spot Welding: Quantitative Assessment of Its Impact on Cycle Time and Robotic Assembly Line Balancing

  • Dawid Stade,
  • Martin Manns

摘要

Process time variance is frequently assessed in assembly line balancing research, a crucial production planning step. However, it has been largely overlooked in planning of highly automated car body construction assembly lines, owing to the deterministic nature of robots. Resistance spot welding, the most prevalent joining technique, employs feedback control systems for quality assurance, leading to varying process times. When multiple welds are done per cycle, these variations lead to fluctuations in overall cycle times. A quantitative analysis of 16 assembly lines reveals a 0.6 s median cycle time range. By incorporating process time variance in the planning of car body construction, this cycle time variance can be controlled to mitigate effects on assembly line performance. However, modelling of process times is required because these cannot be known in advance. As simulations on the balancing of car body construction assembly lines indicate, the model accuracy, however, needs to be high. Inaccurate distributional shape and spread predictions cause deficient planning assumptions, resulting in unpredictable outcomes.