Automatic Control Devices TRIZ Cases

Using action identifiers and numeric tables, this case shows how automated machine control programs are assembled faster with fewer coding errors.

Tilted tool-path control converts theoretical slope trajectories into actual machining paths, producing continuous metal-panel slopes with less grinding.

Model predictive control coordinates roll gap, force, speed, and tension to improve cold rolling mill thickness uniformity and flatness.

Optical workpiece position data is used to adapt forming parameters from prior runs, improving geometric accuracy and quality consistency.

Vibration sensing triggers automatic grinding wheel repositioning to offset wear, improving panel edge precision and reducing downtime.

Digital process model matching lets a movable emergency-stop device join the right robot safety circuit without manual setup.

A neural network reads CNC control data to detect collisions, tool wear, and bearing damage early, cutting downtime and improving machining quality.

Model-based tool displacement estimation corrects axis commands in non-motion directions to reduce machining errors from structural deformation.

Pre-stored wear data lets the controller update tool offsets at machining start or end, reducing defects without stopping production.

Infrared temperature screening and face authentication stop feverish operators from starting machining center work, improving safety without in-process interruption.

By mapping actual cutting points to machining features, this case makes five-axis defect causes easier to trace on the workpiece surface.

Air-cut motion is timed to synchronize spindle speed and axial feed at hole entry, cutting tapping cycle time without staging waypoints.

Grinding machine data is used to predict gear rolling test and noise behavior, cutting physical test effort in series production.

Recorded grinding parameters feed a data model that predicts gear rolling test results and reduces full physical inspection effort.

Adding C1 and S axes brings the cutting point closer to the control intersection, reducing linear-axis compensation for large blisk machining.

Measured cut edge features are aggregated with machine and material inputs to recommend laser cutting parameters with less operator judgment.

Pseudo-ruled surface regions guide 5-axis barrel toolpaths, enabling precise and efficient flank milling on non-ruled geometries.

A lid-integrated camera detects and corrects workpiece position with the laser plotter open or closed, improving setup speed and usability.

By comparing singular point distances across rotary axes, this controller avoids abrupt angle changes near singularities and protects surface quality.

Gaussian-curvature mapping finds pseudo-ruled regions so a 5-axis barrel tool can flank mill non-ruled surfaces with precise curvature matching.

Prebuilt actuator program elements are assembled from an action chart to cut control-program development time for automated manufacturing machines.

Controlled pre-motion vibration spreads lubricating oil on guide surfaces, cutting start-up friction, torque load, and drive wear.

Per-operation acceleration and deceleration tuning cuts tool-change energy use while keeping drive-shaft cycle time within limits.

Machine-learned offset prediction automates gas turbine component recontouring, cutting iterative machining time while maintaining accuracy.

Superimposed vibration in semi-closed loop motion releases internal stress, improving machine tool stop accuracy without extra sensors.

AprilTag imaging tracks 3D position and angle shifts in frames, machining units, and conveyors to correct assembly deviation and process errors.

By replacing rapid retraction with timed cutting stops during deep-hole boring, this case reduces machine vibration, tool wear, and accuracy loss.

Pre-machining the joint region lets an eccentrically joined workpiece pass through the support section for smoother continuous machining.

Approach-phase advance-retreat vibration releases internal stress, improving machine tool stop accuracy under semi-closed loop control without extra sensors.

By validating preset resumable blocks before restart, this NC control approach prevents tool-workpiece interference and operator setup errors.

Pre-cut finish allowance calculation predicts uncut portions after vibration cutting, helping avoid excessive tool load in finish-cutting.

Separating tool holder IDs from assembled tool IDs prevents compensation overwrite, cuts manual renumbering, and preserves tool change records.

By simulating NC commands, servo feedback, and drive shaft motion together, this case improves machining accuracy and time evaluation.

Visual shape edits are converted into machining condition changes, reducing manual tuning burden while preserving machining accuracy.

Calculating an optimal phase command lets polygon machining align workpiece and tool axes faster without stopping for phase correction.

Independently driven spindles pre-accelerate the standby tool to cut tool change time, energy use, and collision risk in compact CNC machining.

Independent spindle control pre-accelerates the next tool to mask change time while cutting energy use and collision risk in compact CNC turrets.

By reversing the feed axis before stroke endpoints, honing control keeps tool rotation continuous, reducing friction and improving surface finish.

Measured correction pieces feed dimensional offsets back into a dental cutting machine to counter aging drift and keep machining on spec.

Real-time resistive force sensing lets the cutter detect wall penetration and adjust depth to open varied or deformed containers without damaging contents.

By stopping tool vibration during approach and presetting contact phase, threading starts faster without unnecessary vibration.

Multiple optical measuring zones capture bending angles along the full bend line without mechanical movement, improving accuracy and speed.

Real-time spindle vibration monitoring adjusts machining parameters to maintain workpiece surface quality without manual re-sampling.

An attached 3D identifier links each workpiece to the right measurement data, preventing input errors in machining planning and reducing extra handling costs.

A separated help unit guides machining setup by user level while blocking machining commands from help screens to prevent accidental starts.

A force sensor placed between upper and lower tool arms captures cutting force directly, improving machining accuracy and reducing tool wear.

Sensor-driven machine learning replaces fixed motor thresholds to adjust field weakening in power tools for changing loads and kickback.

Wired MEMS spindle accelerometers convert x-y vibration into cutting-force plots, improving tool status detection despite machining disturbances.

Reference thrust measured before cutting enables feed-coordinate breakage detection in small-diameter hole machining with fewer false alarms.

Matching on-screen operation sections to the physical order of machining heads helps prevent operator confusion and unintended head selection.