Digital Twin Series · Level 3 · Case study
Battery-Module Assembly Line Digital Twin
What does a digital twin of a whole assembly line tell me before the line is built?
A battery-module line has nine stations in a row: cell loading, cell handling, pressing, laser cleaning, busbar assembly, welding, electrical test, end-of-line test and palletizing. The twin lets you simulate the robots, the buffers and the flow, find the bottleneck, and try changes before any of them cost real money. The station times, buffer sizes and rates on this page are an example set, not measured data: the tools compute their own results, and you can put in your own.
The cycle is split into activities. A faster robot shortens the moves, but pick, place and release also depend on the gripper and the cell, so only part of the cycle scales with speed (an assumption of this model: the moves scale fully, the rest only half). The default times are an example; take yours from the robot path simulation. Compare the module time with the takt time of the line.
A hand-off between two stations is simulated second by second. The upstream station makes a pallet every cycle with some variation, and the downstream station has random stops. The buffer absorbs the difference. Too small and the upstream station is blocked; too large and WIP and floor space grow.
The stack is modelled as a spring: force = stiffness x compression. Cell thickness varies, so a force-controlled press gives a varying final thickness, while a position-controlled press gives a constant thickness and a varying force. The stiffness, cell thickness and spread are assumptions: measure them on your own cells.
Each inspection station finds a share of the defects and wrongly rejects a share of the good parts. The numbers are what you would set from a measurement study of the real station. A twin cannot tell you them: it only shows what they do to yield.
The line is a series of stations with a buffer between each pair. It is simulated cell by cell with random cycle variation and random stops. The utilisation bar shows the busy share of each station; the one at the top is the bottleneck. The station times are an example set that makes welding the slowest, as in the case. Replace them with times from your own line.
Speeding up the bottleneck only helps until the next station becomes the limit. The curve flattens where that happens, and spending more on the same station is then wasted.
| Change | Throughput (modules/h) | Change | New bottleneck |
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Each change is run on the same random sequence, so the differences are not noise. Faster robots take 20% off the process time of the robot stations. A faster conveyor shortens the pallet transfer between stations. Parallel welding gives welding two robots. Daily output assumes the stated working hours.
A simulated line feeds the board. When the weld station slows (here because of fouled laser optics) the throughput of the whole line falls, and the twin raises an alert after three low readings. The live data also lets you run what-if experiments from the real state of the line.
The "after" case is whatever changes you ticked in the what-if section. The margin per module and the cost of the changes are assumptions that you must replace with your own. Extra output is counted only when there is demand for it.
| Stage | Tools shown in the case | What they do here |
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The tools are examples, not recommendations. Choose by what your team owns and what your machines and PLCs support.
The case is illustrative and the simulators are simplified teaching models. Measure your own line, validate your own model, and review safety logic with a qualified safety engineer.