Practical Workflow to Extract Accurate Cycle and Standard Times from G-Code for Production Planning

Accurately extracting cycle and standard times from G-code gives planners realistic machine runtimes and enables reliable capacity planning, takt calculations, and workforce scheduling. Research shows CAM-estimated times frequently differ from shop-floor reality by 10–30%, causing over-optimistic schedules, late deliveries, and hidden overtime. This article explains how to parse G-code for motion time, add auxiliary and operator allowances, validate results with measured runs, and feed validated standard times into ERP/MES workflows so shops can increase throughput without hiring.

TL;DR:

• CAM and G-code parsing can estimate machine cycle time within 5–15% when tuned; expect initial variance of 10–30% before validation.

• Use a motion-only parser/simulator (with machine accel/jerk profiles), add measured auxiliary times, and validate with 5–20 sample parts to compute bias and corrections.

• Feed validated standard minutes to ERP/MES via CSV or API and update routings; aim for <5% bias for planning, 5–15% acceptable with correction factors.

What Are Cycle Time And Standard Time, And Why Do They Matter For Production Planning?

Defining cycle time vs standard time

Cycle time is the actual machine runtime required to produce one part or one cavity in a single cycle — typically measured as spindle-on plus motion time during cutting and non-cutting axis moves. Standard time is the planning metric used in routing and workforce schedules: it blends average observed cycle time with allowances for setup, loading/unloading, inspections, personal needs, and fatigue. For example, a measured machine cycle of 10.0 minutes may become a 12.3-minute standard after adding 15% allowances and handling time.

How inaccuracies impact scheduling and labor load

Inaccurate time estimates distort lead times and resource allocation. Industry audits and practitioner reports indicate CAM-simulated and planner-estimated cycle times commonly understate actual production by 10–30%; this is driven by ignored tool changes, rapids, or unrealistic feed assumptions. A 15% underestimation on 1,000 production hours can create 150 hours of unplanned overtime, missed due dates, and inflated WIP. OEE calculations, takt time alignment, and utilization targets rely on reliable times—errors propagate into purchasing, staffing, and customer commitments.

Key metrics to track (OEE, takt, utilization)

• OEE (Overall Equipment Effectiveness): Uses availability, performance, and quality; inaccurate cycle times distort the performance factor.

• Takt time: Customer-demand-driven rhythm; if cycle time exceeds takt, capacity shortfalls appear.

• Utilization: Percent of available machine time used; over-optimistic estimates inflate projected utilization and mislead hiring decisions.

Quantifying these metrics requires converting raw G-code motion estimates into planner-ready standard times. The rest of this workflow shows how to extract time-encoded actions from G-code, account for auxiliary and operator tasks, and close the loop with shop-floor validation to reduce that 10–30% variance.

How Does G-Code Encode Time-Related Information And What Can You Reliably Extract?

Sources of time data inside a G-code program

G-code embeds motion commands and parameters that imply time: feedrates (F), spindle speeds (S), linear (G1) and circular moves (G2/G3), rapid moves (G0), dwell (G4), and canned cycles (G81–G89). Tool change blocks (e.g., M6 or Txx followed by offsets) and M-codes control auxiliary devices. Units (G20/G21) and modal states set the interpretation of numeric values. From these, a parser can calculate move distances and nominal durations using feedrates and path geometry.

What CAM/simulated times represent vs actual machine behavior

CAM simulators produce nominal cut-time estimates based on toolpath length and programmed feedrates. High-end verifiers such as Vericut and vendor CAM simulators account for axis limits and machine kinematics but may still assume idealized conditions. CAM times typically exclude operator handling, part clamping, probing loops, and dynamic feed reductions like tool engagement constraints or chip thinning adjustments. Studies and community threads (see a practical discussion on calculate part cycle time from G-code) note that CAM times are a starting point, not a final standard.

Limitations: spindle ramps, dwell times, canned cycles

G-code does not always include explicit durations for spindle acceleration, tool magazine motion, or external robot handoffs. Dwell (G4) is explicit, but spindle ramp time depends on machine torque and S-command dynamic behavior. Canned cycles hide repeated motion in high-level commands; a parser needs to expand them to calculate individual retract, feed, and dwell events. Differences between controllers (Fanuc, Siemens, Heidenhain) mean macros and modal behaviors vary—parsing must be controller-aware or conservative. The MIT manpower planning literature highlights how unmodeled auxiliary and human tasks can dominate total cycle time in labor-intensive operations (see MIT research on manpower planning and cycle-time reduction). In short: G-code yields reliable motion time for programmed moves, but accurate cycle time requires handling controller conventions, acceleration profiles, and non-cutting events.

What Is A Practical, Step-By-Step Workflow To Extract Cycle Time From G-Code For A Single Program?

Step 1 — prepare the program (comments, headers, units)

1. Validate program header for units (G20/G21), coordinate system (G54..G59), and tool offsets. Confirm expected spindle and coolant commands (S, M7/M8).

2. Remove or mark inactive blocks (commented or post-M30) to avoid parsing dead code.

3. Expand macros and subroutines when possible; if subroutines depend on run-time conditions, document assumptions.

Preparing the program ensures the parser reads feed and move geometry consistently.

Step 2 — simulate motion-only time with a parser/simulator

4. Use a motion-only simulator or parser to iterate each move block: - Compute move length for G0/G1/G2/G3 using linear and arc geometry. - Use programmed feedrate (F) to compute nominal time: time = distance / feed. - For rapids (G0), use machine rapid-rate or a configured rapid speed. - Model acceleration/deceleration: apply trapezoidal or jerk-limited profiles using machine max accel values to adjust short moves where steady-state feed isn't reached.

Example micro-calculation:

• G1 X50 Y0 F200 (units: mm/min) → distance = 50 mm → feed = 200 mm/min → nominal time = 0.25 min = 15 s.

• If move is short and machine takes 2 s to accelerate to full feed, adjust calculated time to 17 s.

Step 3 — add auxiliary and non-cutting times

5. Account for dwell blocks (G4) and canned-cycle expansions (calculate repeated retracts and dwell per hole).

6. Add tool-change and spindle ramp times: use measured or OEM values (typical tool change = 8–12% of cycle on manual machines; auto-changers reduce this considerably).

7. Append fixed per-part auxiliary times: load/unload, part probing, measurement, inspection; these can be measured on the shop floor.

Numeric breakdown (example for a 10-minute extracted motion time):

• Cutting motion = 65% (6.5 min)

• Tool change = 10% (1.0 min)

• Load/unload/inspect = 15% (1.5 min)

• Misc/unplanned = 10% (1.0 min) Total = 10 + 1 + 1.5 + 1 = 13.5 minutes (before allowances)

Note that shops with automation and pallets will see smaller handling percentages; manual fixtures increase operator minutes. For controller-specific commands like Fanuc macros or Heidenhain cycles, expand or simulate per controller reference to avoid missing hidden moves.

Which Tools, Scripts, And Simulators Reliably Extract Cycle Time From G-Code?

Commercial CAM/verification tools vs open-source parsers

Commercial verifiers such as Vericut (CGTech), Siemens NX PostProcessor + simulator, and many CAM vendor simulators include machine models for axis limits, acceleration, and collision checks. These tools typically provide high accuracy (often within 2–10% for motion time if properly configured) but carry license costs and require machine profile setup. CNC-specific products such as CNC Simulator Pro offer mid-tier accuracy and easier setup for single machines.

Open-source parsers like pygcode and various G-code utilities provide low-cost, scriptable options for basic time summation but usually lack detailed kinematic modeling. The RepRap G-code reference is useful for understanding G-code flavors in additive contexts; see the RepRap G-code documentation for command behavior.

Scripting approaches (Python libraries) and sample algorithm

Scripting approaches work well for batch processing: Python + pygcode for parsing, NumPy for geometry, and a small kinematics module for accel/jerk. Pseudo-code:

1. Parse blocks into (motion_type, coordinates, feed).

2. For each motion: - distance = compute_length(path) - t_nominal = distance / feed - t_adjusted = apply_accel_profile(distance, machine_accel)

3. Sum t_adjusted plus fixed auxiliary times.

Machine parameters (max feed, accel, rapid, tool change times) must be maintained in a machine profile (XML/JSON) to get realistic results.

How to choose: accuracy vs speed vs integration

• Choose commercial simulators for high accuracy, collision detection, and built-in machine profiles when you need to trust times for quoting or high-value parts.

• Use scripted parsers for batch processing of thousands of programs, CI integration, or when building a custom API to feed ERP/MES.

• If integration matters (APIs, exports), evaluate tools that can export CSV/JSON or provide RESTful endpoints to automate imports.

A practical video walkthrough helps operationalize these choices — viewers will learn parsing, simulation, and validation techniques in a short demo: .

For programming-side improvements that reduce cycle time, see the case study on how smarter CNC programming saved both time and cost in a shop.

How Should Shops Validate And Correct Extracted Times With Real Shop-Floor Data?

Designing quick validation runs and sample size

Validation is essential: run a sample batch of parts and capture measured times. Recommended sample sizes:

• Low-variability parts: 5–10 pieces

• High-variability or long cycles (>60 min): 3–5 pieces

• Small-batch or prototype runs: focus on representative operations and tooling.

Use randomized sampling across shifts if operator variability matters. Compute the mean, standard deviation, and bias (measured minus extracted) to derive correction factors.

Using machine monitoring or edge devices to capture cycle times

Edge monitoring devices and machine data collectors capture spindle-on hours, axis moves, and program-run events. JITbase-style edge devices and OEE dashboards can record spindle-on time, tool-change events, and non-cutting idle time; this granularity lets teams separate machine motion from operator handling. When possible, capture:

• Spindle on/off timestamps

• Program start/stop and modal states

• Tool change timestamps

• Operator load/unload and inspection events (manual tags or connected signals)

Operator interaction and manual events can be captured and reconciled with automated logs; see the piece on the connected worker and operator interaction.

Reconciling differences and tuning your model

After collecting data:

1. Compute bias = mean(measured) / mean(extracted).

2. If bias <1.05, consider using extracted times with a small safety margin.

3. If bias between 5–15%, apply a correction factor and re-run a validation set.

4. If bias >15%, investigate missing auxiliary times, mis-modeled acceleration, or controller macros.

Tuneable parameters include feed reduction factors, tool-change durations, and per-part handling times. For planning purposes, aim for less than 5% bias. If operator workload dominates, split totals into machine minutes and operator minutes; apply separate allowances and batching effects.

How Do You Convert Raw Cycle Times To Standard Times For Workforce Planning?

Applying allowances and performance factors

Convert observed cycle time into standard time using classical time study formulas:

• Standard Time = Observed Time / Performance Rating + Allowances Where performance rating accounts for operator working pace (commonly set at 1.0 for machine-controlled tasks), and allowances cover personal needs, fatigue, and unavoidable delays. Typical allowances range from 5% (highly automated, experienced crews) to 15% (manual shops with frequent interruptions).

Example: Observed cycle = 10.0 minutes; performance rating = 1.0; allowances = 12% → Standard = 10.0 / 1.0 (1 + 0.12) = 11.2 minutes.

Converting machine cycle to operator minutes

Operator workload often exceeds pure machine run time when loading/unloading, staging, inspection, and secondary operations are included. Break out tasks:

• Machine run time (per part)

• Load/unload time (per part or per pallet)

• Inspection and setup time (per lot or per part)

• Secondary finishing or deburring (per part)

If loading takes 30 seconds per part and machine run is 10 minutes, operator minutes per part = 10 + 0.5 = 10.5 minutes. Batch loading amortizes handling per part: 10 parts per pallet reduces per-part load time to 3 seconds, changing workload calculations.

For workforce planning, attach operator minutes to routing steps as standard operator minutes and include them in labor forecasting. Learn how accurate standards improve staffing and productivity in the labor management benefits article.

Examples: single-setup vs multi-setup part

Single-setup part example:

• Machine time: 8.0 min

• Load/unload per part: 0.5 min

• Inspection per part: 0.2 min

• Allowances: 10% → Standard = (8.0+0.5+0.2) 1.10 = 9.47 min

Multi-setup part (two operations):

• Op1 machine time: 6.0 min; Op1 handling: 1.0 min

• Op2 machine time: 4.0 min; Op2 handling: 0.5 min

• Combined allowances: 12% applied per operation for planning of routing steps

Converting machine minutes into planner-ready operator minutes clarifies where staffing or automation investments will yield capacity gains. For strategies to increase throughput without hiring, see the machinist shortage guide on practical measures: how to overcome the machinist shortage and boost production capacity.

How Do You Integrate Extracted And Validated Times Into Production Planning And ERP/MES Systems?

Data formats and exports (CSV, JSON, API)

Validated time data should be exportable in standard formats:

• CSV: simple batch import to most ERPs (fields: operation code, work center, standard minutes, setup minutes, version).

• JSON/REST API: for real-time or scheduled synchronization with modern MES/ERP systems.

• Excel templates: useful for manual review before committing to ERP.

Maintain a machine-profile and time-versioning column so planners can revert or track changes.

Mapping to routing/operation codes and work centers

Normalize operation codes so the extracted time maps to the ERP’s routing steps: operation number, description, work center, standard minutes, and setup minutes. Include machine identifiers and capability tags (e.g., 3-axis milling, 4-axis lathe) to support finite scheduling and automated resource selection. Use a consistent naming scheme to avoid duplicate or orphaned operations.

For importing times into planning tools, see how a modern production planning tool consumes standard minutes to produce realistic schedules.

Using time data for short-term scheduling and long-term capacity

• Short-term: Feed run minutes into finite schedulers to sequence work and avoid overloads; use real-time sync to correct schedules when measured times drift.

• Long-term: Aggregate validated standard times for capacity planning, workforce forecasting, and OEE trend analysis.

Version control matters: tag time estimates with validation date and sample size. If measured times trend upward, automate alerts and schedule re-validation runs to maintain planning accuracy. For a discussion on real-time updates and scheduling, consult the article on real-time scheduling.

What Are The Key Points And How Do Common Tools/Specs Compare?

Key points checklist

• Validate program units and expand controller-specific macros before parsing.

• Use machine profiles (max feed, accel, rapid rates) to model realistic motion times.

• Add measured auxiliary times: tool changes, load/unload, inspection.

• Validate with sample runs (5–20 parts) and compute bias; target <5% for planning.

• Separate machine minutes from operator minutes for accurate workforce planning.

• Export validated standards to ERP/MES with version control and scheduled re-validation.

Comparison/specs table: simulators, parsers, and CAMs

Notes:

• Accuracy ranges depend on machine profile fidelity and whether auxiliary times are included.

• Commercial tools often include support and machine libraries; open-source solutions require investment to create accurate machine models.

• For shops seeking fast ROI without high software spend, combine open-source parsing with measured auxiliary time capture via edge devices.

For additional context on open-source G-code flavors and command semantics in additive contexts, consult the RepRap G-code reference.

The Bottom Line: Should you extract cycle times from G-code?

Extracting cycle times from G-code is a practical and measurable way to improve planning accuracy, reduce hidden overtime, and increase throughput without adding staff. Implement a validated workflow—motion parsing with machine profiles, measured auxiliary time capture, and scheduled re-validation—and integrate standard minutes into ERP/MES to realize capacity gains and more reliable schedules.

Frequently Asked Questions