flyinginte / toolwear_cncmilling

An investigation of tool wear evolution is undertaken by explorying the CNC Milling Dataset of the University of Michigan SMART LAB.

toolwear_cncmilling

This repo contains some investigations on the CNC Milling Dataset disposed by the University of Michigan Smart Lab. An Exploratory Data Analysis is undertaken, in order to find correlations and come up with a valid model that translates the values of the measured physical variables into the evolution of tool wear.

Description

CNC MILLING DATASET - UNIVERSITY OF MICHIGAN SMART LAB

April 2018

A series of machining experiments were run on 2" x 2" x 1.5" wax blocks in a CNC milling machine in the System-level Manufacturing and Automation Research Testbed (SMART) at the University of Michigan. Machining data was collected from a CNC machine for variations of tool condition, feed rate, and clamping pressure. Each experiment produced a finished wax part with an "S" shape - S for smart manufacturing - carved into the top face, as shown in test_artifact.jpg

The dataset can be used in classification studies such as:

(1) Tool wear detection Supervised binary classification could be performed for identification of worn and unworn cutting tools. Eight experiments were run with an unworn tool while ten were run with a worn tool (see tool_condition column for indication).

(2) Detection of inadequate clamping The data could be used to detect when a workpiece is not being held in the vise with sufficient pressure to pass visual inspection (see passed_visual_inspection column for indication of visual flaws). Experiments were run with pressures of 2.5, 3.0, and 4.0 bar. The data could also be used for detecting when conditions are critical enough to prevent the machining operation from completing (see machining_completed column for indication of when machining was preemptively stopped due to safety concerns).

General data from a total of 18 different experiments are given in train.csv and includes:

Inputs (features)

No : experiment number material : wax feed_rate : relative velocity of the cutting tool along the workpiece (mm/s) clamp_pressure : pressure used to hold the workpiece in the vise (bar)

Outputs (predictions)

tool_condition : label for unworn and worn tools machining_completed : indicator for if machining was completed without the workpiece moving out of the pneumatic vise passed_visual_inspection: indicator for if the workpiece passed visual inspection, only available for experiments where machining was completed

Time series data was collected from 18 experiments with a sampling rate of 100 ms and are separately reported in files experiment_01.csv to experiment_18.csv. Each file has measurements from the 4 motors in the CNC (X, Y, Z axes and spindle). These CNC measurements can be used in two ways:

(1) Taking every CNC measurement as an independent observation where the operation being performed is given in the Machining_Process column. Active machining operations are labeled as "Layer 1 Up", "Layer 1 Down", "Layer 2 Up", "Layer 2 Down", "Layer 3 Up", and "Layer 3 Down".

(2) Taking each one of the 18 experiments (the entire time series) as an observation for time series classification

The features available in the machining datasets are:

X1_ActualPosition: actual x position of part (mm)

X1_ActualVelocity: actual x velocity of part (mm/s)

X1_ActualAcceleration: actual x acceleration of part (mm/s/s)

X1_CommandPosition: reference x position of part (mm)

X1_CommandVelocity: reference x velocity of part (mm/s)

X1_CommandAcceleration: reference x acceleration of part (mm/s/s)

X1_CurrentFeedback: current (A)

X1_DCBusVoltage: voltage (V)

X1_OutputCurrent: current (A)

X1_OutputVoltage: voltage (V)

X1_OutputPower: power (kW)

Y1_ActualPosition: actual y position of part (mm)

Y1_ActualVelocity: actual y velocity of part (mm/s)

Y1_ActualAcceleration: actual y acceleration of part (mm/s/s)

Y1_CommandPosition: reference y position of part (mm)

Y1_CommandVelocity: reference y velocity of part (mm/s)

Y1_CommandAcceleration: reference y acceleration of part (mm/s/s)

Y1_CurrentFeedback: current (A)

Y1_DCBusVoltage: voltage (V)

Y1_OutputCurrent: current (A)

Y1_OutputVoltage: voltage (V)

Y1_OutputPower: power (kW)

Z1_ActualPosition: actual z position of part (mm)

Z1_ActualVelocity: actual z velocity of part (mm/s)

Z1_ActualAcceleration: actual z acceleration of part (mm/s/s)

Z1_CommandPosition: reference z position of part (mm)

Z1_CommandVelocity: reference z velocity of part (mm/s)

Z1_CommandAcceleration: reference z acceleration of part (mm/s/s)

Z1_CurrentFeedback: current (A)

Z1_DCBusVoltage: voltage (V)

Z1_OutputCurrent: current (A)

Z1_OutputVoltage: voltage (V)

S1_ActualPosition: actual position of spindle (mm)

S1_ActualVelocity: actual velocity of spindle (mm/s)

S1_ActualAcceleration: actual acceleration of spindle (mm/s/s)

S1_CommandPosition: reference position of spindle (mm)

S1_CommandVelocity: reference velocity of spindle (mm/s)

S1_CommandAcceleration: reference acceleration of spindle (mm/s/s)

S1_CurrentFeedback: current (A)

S1_DCBusVoltage: voltage (V)

S1_OutputCurrent: current (A)

S1_OutputVoltage: voltage (V)

S1_OutputPower: current (A)

S1_SystemInertia: torque inertia (kg*m^2)

M1_CURRENT_PROGRAM_NUMBER: number the program is listed under on the CNC

M1_sequence_number: line of G-code being executed

M1_CURRENT_FEEDRATE: instantaneous feed rate of spindle

Machining_Process: the current machining stage being performed. Includes preparation, tracing up and down the "S" curve involving different layers, and repositioning of the spindle as it moves through the air to a certain starting point

Note: Some variables will not accurately reflect the operation of the CNC machine. This can usually be detected by when M1_CURRENT_FEEDRATE reads 50, when X1 ActualPosition reads 198, or when M1_CURRENT_PROGRAM_NUMBER does not read 0. The source of these errors has not been identified.

TO DO

• Stablish a correlation among Fourier Transform features for Spindle Power Output signal and Tool Wear/Quality,
• Investigate the influence of both Spindle Current and Voltage output and Tool Wear/Quality,
• Develop a tool wear/quality model, which predicts the result of the process in beforehand by means of a time series analysis of the signals.