General & Guides

Digitalising Production in a Marble Workshop — CNC Data Logging and Industry 4.0

✍️ USEL Engineering📅 01 May 20265 min read

Industry 4.0 is the digitalisation of industrial production. Marble and CNC workshops start out among the traditional sectors, but data collection and analysis multiply their output. This guide sets out the first steps of digital transformation.

Why Does Digitalisation Matter?

  • OEE MEASUREMENT — put a number on your production efficiency (an hourly percentage for every machine)
  • DOWNTIME ANALYSIS — which machine stands idle, for how long, and why?
  • MAINTENANCE PREDICTION — detection from sensor data before a breakdown
  • PRODUCTION TRACKING — the order → workshop → delivery chain becomes visible
  • QUALITY TRACKING — the defect rate plus root cause analysis
  • OPERATOR PERFORMANCE — comparison on a shift-by-shift basis
  • SPARE PARTS MANAGEMENT — the consumption trend captured automatically

CNC Data Logging — What Is Collected?

  • MACHINE STATE — on / off / idle / running / fault
  • RUNNING HOURS — spindle running time plus idle waiting time
  • ALARM RECORDS — code plus time plus frequency
  • PRODUCTION COUNT — finished parts (program completions)
  • PROGRAM EXECUTION — which G-code, and for how long
  • TOOL USAGE — which tool, and for how long (with an ATC)
  • TEMPERATURE AND VIBRATION — spindle and axis sensors
  • POWER CONSUMPTION — instantaneous kVA plus the daily total

OEE — Overall Equipment Effectiveness

OEE is the gold standard of industrial efficiency measurement. It is the product of 3 factors:

  • AVAILABILITY — the time the machine was on / the time planned
  • PERFORMANCE — the actual production rate / the theoretical maximum rate
  • QUALITY — accepted parts / total parts produced
  • OEE = Availability × Performance × Quality
  • The world benchmark: above 85% is excellent, 60% is average, below 40% is poor
A Worked Example
Take a CNC bridge saw — 8 hours were planned, it was on for 7 hours (7/8 = 87.5% availability), it actually cut for 5 hours (5/7 = 71% performance), and of 10 parts produced 9 were accepted (9/10 = 90% quality). OEE = 0.875 × 0.71 × 0.90 = 56%. There is room for improvement.

Data Collection — The Hardware

  • THE CNC OUTPUT — modern CNC controls (TEX, Fagor, Beckhoff) provide a standard Ethernet/OPC output
  • IoT SENSORS — can be retrofitted to an older machine (vibration, temperature, current)
  • BARCODE/RFID — work order plus product tracking
  • AN OPERATOR PANEL — a tablet or touchscreen (start of shift plus entry of downtime reasons)
  • A GATEWAY — transfers all the data to a cloud or local server

The Software Ecosystem

  • MES (manufacturing execution system) — workshop management software
  • ERP — orders plus stock plus invoicing, integrated
  • SCADA — visualisation of CNC and IoT data
  • BI (business intelligence) — reporting with Power BI or Tableau
  • CLOUD-BASED — modern solutions (PTC ThingWorx, Siemens MindSphere)
  • Türkiye: Logo, Mikro, Eta — local ERP integration

Predictive Maintenance

Detecting from sensor data that a machine is "becoming unwell" before it breaks down:

  • VIBRATION SENSOR — spindle bearing wear → vibration rises
  • TEMPERATURE SENSOR — a bearing in distress → the temperature trend moves upwards
  • CURRENT SENSOR — a motor under strain → the current fluctuates
  • OIL ANALYSIS — periodic laboratory checks (metallic particles)
  • AI/ML — pattern detection from historical failure data
  • The result: 40-60% of breakdowns are detected in advance and dealt with through planned maintenance

Digitalising the Workshop — A Phased Approach

Phase 1 — Data Collection

  • Setting up the Ethernet connection on the CNC control
  • An OPC server — to receive the data
  • A simple dashboard — visualising on / running / stopped
  • A 6-12 month period — building the habit plus basic measurement

Phase 2 — Analysis

  • OEE calculation plus reporting
  • Downtime analysis — the reasons categorised
  • An operator panel — the operator enters the downtime reasons
  • Monthly improvement meetings
  • A 12-18 month period

Phase 3 — Advanced

  • IoT sensor retrofit
  • Predictive maintenance (the critical machine first)
  • Full ERP integration — order → workshop → delivery
  • A customer portal — seeing the status of an order live
  • AI/ML projects
  • A 2-3 year period

The Approach to Investment

  • TO START — Excel plus manual records (free, but not sustainable)
  • MID-RANGE — an off-the-shelf SaaS solution (an annual subscription), for a small workshop
  • LARGE — a bespoke ERP plus MES plus SCADA installation (a large workshop)
  • CLOUD — low capital outlay, subscription-based, practical for most workshops
  • EU grants and the digital transformation support programmes in Türkiye are worth following

Practical First Steps in an SME Workshop

  1. Check the Ethernet output on the CNC machine
  2. A simple OEE spreadsheet template — the operator fills it in at the end of the shift
  3. A monthly downtime analysis report
  4. After 6 months — visualisation with simple BI software
  5. After 1 year — the decision on a sensor retrofit
  6. Activation of remote diagnostics with the authorised service provider
Frequently Asked Questions

COMMON
QUESTIONS

Even a 20-year-old machine can have sensors added by retrofit — vibration, current, temperature. If the CNC control has no output, the data is collected with external sensors. The cost of that investment feeds into the decision on whether to replace the machine.