Tool Life Predictor

Predict tool life with local Taylor-based calculations and local correction rules.

All tools free forever

Tip: Estimate edge life by Taylor equation and correction factors.

Results

8.96
Predicted tool life (min)
0.448
Life ratio T2/T1
7.17
Suggested change point (80%) (min)
4.5
Estimated parts per edge (pcs)
380.654
Taylor constant C
Linked Parameter Diagram
toolLifePredictor

Input / Output Bars

Inputs

Reference speed V1180
Reference life T120
Taylor exponent n0.25
Target speed V2220

Outputs

Predicted tool life8.963
Life ratio T2/T10.448
Suggested change point (80%)7.17
Estimated parts per edge4.481

Geometry View

Cost / Time Profile

toolLifePredictor
Predicted tool life
8.963
Life ratio T2/T1
0.448
Suggested change point (80%)
7.17
Estimated parts per edge
4.481
Reference speed V1
180
Reference life T1
20

Tool role and boundaries

Tool Life Predictor is not a one-shot number widget. It is an engineering baseline tool for real shop-floor decisions. Predict tool life with local Taylor-based calculations and local correction rules. This tool is a material and engineering-data aid where assumptions must be explicit before process tuning.

Treat every output as a first-pass candidate, not an immediate production command: run defaults first, tune one variable at a time, and record machine, tooling, fixture, and material-lot context.

Fast baseline workflow

  1. Run once with defaults to confirm units and expected behavior.
  2. Lock constraints first (dimensions, machine limits, setup boundaries), then tune controls.
  3. Change one key variable per iteration and record why it changed.
  4. Check primary outputs against machine capability before secondary metrics.
  5. Validate first piece with conservative override before moving to target cycle.
  6. Store accepted values with revision tags so shift handoff stays reproducible.

Input strategy

Use a three-layer input model:

  • Constraint layer: dimensions, tolerances, travels, clamping, controller limits.
  • Control layer: speed, feed, engagement, compensation, cycle parameters.
  • Target layer: takt time, cost, scrap risk, tool-change frequency.

A common failure mode is pushing control values before constraints are stable. Lock constraints first, then build a stable operating window with small increments.

Output interpretation

Interpret results in order: primary safety checks first, then stability, then economics.

  1. Safety: no machine, tool, or fixture limit violations.
  2. Stability: load, thermal, and vibration behavior remains controlled.
  3. Economics: cycle and cost align with shift target.

Current focus outputs include Taylor model, Life in minutes/parts, Local optimization tips. If numbers conflict with floor behavior, verify units and inputs before changing strategy.

Typical failure modes and fixes

  • Sudden output jump: verify units, decimal precision, and input ordering first.
  • Unexpected trend: inspect workholding, tool condition, and thermal stability before retuning.
  • Big machine-to-machine delta: compare servo behavior, coolant coverage, spindle health, and compensation tables.
  • Shift handoff instability: enforce revision logging for program, tool, and parameter timestamp.

Keep rollback points and use single-variable increments to avoid coupled uncertainty.

FAQ

Can outputs be used directly for production?

Not immediately. Validate first piece, then short-run stability, then release to full production.

Why does floor behavior differ from computed values?

This is expected. Material lot, tool wear, thermal state, and machine dynamics all shift outcomes.

When should I recalculate?

Recalculate whenever tooling, fixturing, material lot, controller parameters, or takt target changes.

Final recommendation

Use Tool Life Predictor inside a fixed loop: baseline, first-piece validation, single-variable tuning, parameter freeze, and revision tracking. The outcome is not just one result but a repeatable process capability.