Table of Contents

Machining Issues Outside the Model

Some machining problems the simulation does not reproduce, or reproduces only in part. Each section below says what HiNC reports near the problem, what it leaves out, and what a published, openly licensed measurement shows about how large the unmodelled effect can be. Near these problems, read a result as a statement about the cutter, the spindle and the programmed path, not about the part or the machine that cut it.

Ordered from the part outward: the part's own stiffness and stress, the finished surface, the cutter's life, then the spindle and the machine structure. None of the cases cited here was simulated for this page; every number is the publisher's measurement, and the sources are listed at the end.

Thin Walls and Workpiece Deflection

The workpiece in the simulation is rigid: it does not bend away from the cutter, and its vibration is not estimated, as Chatter, and What the Simulation Does About It states for workpiece chatter. On a thin wall the removed material is therefore the programmed engagement, not the smaller engagement a deflecting wall leaves.

A published test shows the size of the difference. A Ti-6Al-4V wall 125 mm long and 60 mm high was finished from 2.6 mm to a 2.2 mm target in two 30 mm axial levels with a Ø16 end mill, down milling at a 0.4 mm width of cut. Cut conventionally, the wall ended 330 µm too thick at the top at mid-length and 360 µm too thick near its end; repeating the path twice without further infeed brought the first to 270 µm. A control loop that measured the wall during the cut and added engagement brought the two to 80 µm and 130 µm [1].

Read the simulated force on such a part as the load on the cutter and on the wall. How far the wall yields under it has to be judged outside the simulation.

Distortion After Unclamping

The simulation removes material geometrically. The part carries no stress field, so neither the stress the cut leaves in the surface layer nor the distortion that appears when the part is unclamped is estimated.

Hole-drilling measurements on 7050-T7451 aluminium blocks milled with four combinations of cutting speed and feed per tooth, with the same Ø12 end mill, put the machining-induced residual stress peak at 115–185 MPa and its penetration depth at 56–180 µm, depending on the combination. Wafers 1 mm thick cut from the milled blocks distorted, less where the measured stress layer was shallower [2].

Surface Roughness Values

The Surface Roughness chart (see Strip Charts) plots the geometric and deflection contributions to the finished surface in micrometres: the re-cut depth, the program-side cusp and the change in tool tip deflection. It does not compute a roughness parameter such as Ra or Rz, and it has no series for the cusp left between neighbouring passes.

Ball-end finishing of convex and concave R45 faces in 42CrMo4, in five raster directions at a 0.15 mm stepover, gave an average Rz that rose from about 3 µm to about 8 µm on the convex face as the raster turned from along the cylinder axis to across it, while the concave face stayed between about 4 µm and 6.3 µm (read from the published chart) [3]. The chart's trend does not follow such a measurement either: rebuilt in HiNC 3.2.42, the change of tip deflection the chart plots fell as the measured Rz rose, while the variation of the cutting force along each pass, which the chart does not plot, rose with it (a rank comparison over five directions; see Ball-End Finishing in Five Directions).

Tool Life and Fatigue

The simulation reports instantaneous failure — the yielding stress, spindle torque, spindle power and thermal yield ratios of Evaluating Process Machinability — and wear quantities, described in Tool Life and Wear. It does not count the cycles a cutter survives, and it has no fatigue or cumulative-damage model.

A run-to-failure data set in 42CrMo4 hardened to 38 HRC, with Ø10 end mills on one contour at 3,200 rpm and 640 mm/min, shows the spread such a count has to cover. At an 8 mm radial depth of cut the tools lasted 29–53 cycles before failing; at 4.5 mm, 59–149. With the holder length raised from 80 mm to 160 mm at 4.5 mm, one tool lasted 14 cycles and the other failed in its first [4]. The ratios can rank such configurations by load; the number of cycles is outside the model.

Spindle Power at Idle

The spindle input power the simulation reports is the cutting power divided by the spindle's energy efficiency, as Spindle Capability derives it, and it leaves out the dry-run power that bearing friction and windage take while the spindle turns; so do the power ratios. The spindle meter power adds the dry-run power, and it is the value to compare with a power reading on the machine. It comes only as close as the spindle's two dry-run coefficients are to that spindle. The shipped defaults are rough estimates from published studies of bearing friction and gear windage, not measurements of a spindle, and give 612 W at 18,000 rpm. Fit them to the machine's own readings in air, as Fitting the dry-run coefficients shows, before comparing.

A 1983 high-speed machining trial on an 18,000 rpm spindle rated 16.6 hp shows how large the idle share can be. The roughing cutter drew 12.0–13.5 hp on a 0.300 in deep pass at 150 in/min, and still 7.1–7.2 hp on a 0.054 in deep pass at 200 in/min; the report does not separate out the idle power [5].

Machine Geometric Error

A project's machine chain is taken as exact. The controller model and the cut use the same chain, so there is no separate nominal machine that the controller believes in and actual machine that cuts: squareness and straightness errors, pivot offsets, reversal and backlash of a real machine are not represented, and the simulated part carries none of them. How a worn or imprecise machine enters the optimizer instead is in Machine Condition and Safety Factors.

On a three-axis machining centre, compensating the volumetric error measured with a laser tracer took it from 49 µm to 14 µm. On a part finished by circular milling before and after the compensation, the mean roundness over seven measured sections improved from 10.13 µm to 6.16 µm on its Ø300 mm diameter and from 9.39 µm to 5.71 µm on its Ø200 mm diameter, while its Ø100 mm diameter stayed at about 5.4–5.6 µm [6].

Machine Thermal Drift

The temperatures the simulation computes are those of the cutting zone — cutter, chip and workpiece surface — and the spindle body's thermal envelope; see Temperature and Wear. The thermal growth of the machine structure is not modelled, so the tool-to-part position does not drift over a run.

Capacitive sensors at four targets near the corners of a machine table measured how far the spindle moved relative to the table while the X axis warmed up with repeated 24 in rapid moves: about −160 µm in X at the two targets on one side of the table and about −50 to −60 µm at the other two, with Y and Z near zero [7].

Sources

The numbers above are quoted unchanged from these sources, or read from their charts where the section says so. Each is provided by its authors or publisher as is, without warranty, and none of them endorses HiNC.

  1. L. Evers, M. Müller, C. Möller, A. Brouschkin, J. H. Dege, “Control Loop Based Dimensional Error Compensation for Milling of Near-Net-Shaped, Thin-Walled Structures”, in H. Kohl et al. (eds.), GCSM 2024, Lecture Notes in Mechanical Engineering, Springer, 2025, pp. 399–407, https://doi.org/10.1007/978-3-031-93891-7_44 (open-access copy: https://doi.org/10.15480/882.15340). © The Author(s) 2025, licensed CC BY 4.0. Values from Section 4.1.
  2. D. Weber, B. Kirsch, C. R. Chighizola, C. R. D'Elia, B. S. Linke, M. R. Hill, J. C. Aurich, “Analysis of machining-induced residual stresses of milled aluminum workpieces, their repeatability, and their resulting distortion”, The International Journal of Advanced Manufacturing Technology 115, 1089–1110 (2021), https://doi.org/10.1007/s00170-021-07171-7. © The Author(s) 2021, licensed CC BY 4.0. Values from Table 7.
  3. B. Mikó, B. Varga, W. Zębala, “The Effect of the Feed Direction on the Micro- and Macro Accuracy of 3D Ball-end Milling of Chromium-Molybdenum Alloy Steel”, Materials 12(24), 4038 (2019), https://doi.org/10.3390/ma12244038. © 2019 by the authors, licensee MDPI, licensed CC BY 4.0. Values read from Figure 7.
  4. G. Piecuch, T. Żabinski, “A new open dataset from a milling process – data for classification and estimation of tool life”, figshare data set (2025), https://doi.org/10.6084/m9.figshare.28589216.v1, described in Scientific Data 12, 650 (2025), https://doi.org/10.1038/s41597-025-04923-y. © The Author(s) 2025, licensed CC BY 4.0. Values from the CycleToFailure column of the data set's metadata, counted as the cycles a tool ran before the failing one.
  5. J. D. Hankins, “High Speed Machining of Space Shuttle External Tank Liquid Hydrogen Barrel Panel”, NASA-TM-82557, NASA Marshall Space Flight Center, November 1983, https://ntrs.nasa.gov/citations/19840006284. Work of the U.S. Government, public use permitted (NASA STI terms). Values from the horsepower data sheets, Tables 3 and 4.
  6. M. Holub, R. Jankovych, J. Vetiska, J. Sramek, P. Blecha, J. Smolik, P. Heinrich, “Experimental Study of the Volumetric Error Effect on the Resulting Working Accuracy—Roundness”, Applied Sciences 10(18), 6233 (2020), https://doi.org/10.3390/app10186233. © 2020 by the authors, licensee MDPI, licensed CC BY 4.0. Values from Figure 7 and Tables 11–13.
  7. G. W. Vogl (2023), “Thermal Drift Monitoring Experiment 01”, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2934, a work of NIST employees, not subject to copyright in the United States (NIST data terms). The warm-up displacement is from Figure 8a of G. W. Vogl et al., “Vision-based thermal drift monitoring method for machine tools”, CIRP Annals 72 (2023) 301–304, https://doi.org/10.1016/j.cirp.2023.04.053.

See Also