Short answer

When deploying automated production processes or monitoring systems across multiple machines, anticipate and account for inherent variations in machine tool behaviour, even when using identical programming.

Field
Final Production
Source
Procedia CIRP (2023)
Method
Comparative analysis of process signals
Evidence
Strong effect

Executing the same numerical control (NC) code on different machine tools results in variations in timing and positioning, primarily driven by differing control parameters, strategies, and the physical limitations of the machine drives. This final production research insight is drawn from a 2023 study published in Procedia CIRP. Using Comparative analysis of process signals, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When deploying automated production processes or monitoring systems across multiple machines, anticipate and account for inherent variations in machine tool behaviour, even when using identical programming.

Study
Final ProductionRecentStrong effect

Identical NC-code yields divergent machine tool behaviour due to control parameters and physical limitations.

Executing the same numerical control (NC) code on different machine tools results in variations in timing and positioning, primarily driven by differing control parameters, strategies, and the physical limitations of the machine drives.

Procedia CIRP · 2023

01

Key Findings

  • 01Significant differences were observed in traverse speeds, traverse paths, machining feed speeds, and machining feed paths when using identical NC code on different machines.
  • 02Differences were most pronounced during axis acceleration and deceleration phases.
  • 03Control parameters, control strategies, and physical drive limitations were identified as primary sources of variation.
  • 04Accumulated differences over prolonged machining periods can become significant for online monitoring systems.
02

Application

Design takeaway

When deploying automated production processes or monitoring systems across multiple machines, anticipate and account for inherent variations in machine tool behaviour, even when using identical programming.

How to apply

Before deploying a new process monitoring system or automated workflow across multiple machines, conduct comparative tests using identical NC code to map out and understand the expected variations in timing and positioning.

Project actions

  • 01When comparing the performance of different prototypes or manufacturing methods, ensure you use identical input parameters and code.
  • 02Carefully document all machine settings and control parameters used during your tests.
  • 03Consider how small variations in your design or manufacturing process could lead to larger differences in the final product or performance.
03

Method & Evidence

AimTo quantify the similarities and differences in timing and positioning when multiple machine tools execute identical NC code, and to identify the root causes of these variations.
MethodComparative analysis of process signals
ProcedureIdentical NC code was executed on different machine tools. Process signals were recorded and compared to identify variations in process sequences, traverse speeds, traverse paths, machining feed speeds, machining feed paths, tool engagement time, and signal temporal alignment. Differences were attributed to control parameters, strategies, and drive limitations.
ContextManufacturing, Machine Tool Operation, Process Monitoring

Variables

IV["Machine tool identity","NC-code execution"]
DV["Timing of operations","Positioning accuracy","Process signal characteristics (e.g., speed, path, engagement time)"]
CV["NC-code instructions","Material being machined","Tooling"]
04

Strengths & Limitations

Strengths

  • +Direct comparison of identical code on different machines provides clear insights into variability.
  • +Identification of key factors (control parameters, drive limitations) causing differences.

Limitations

The study was conducted on specific machine tools, and the findings might not be universally applicable to all types of machinery. The complexity of control parameters and strategies means that not all potential sources of variation may have been explored.

Reliability & validity

The reliability of the findings depends on the consistency of the machines' performance over multiple runs and the accuracy of the signal acquisition. Validity is supported by the direct comparison of identical inputs and the identification of plausible causes for observed differences.

Think critically

To what extent can machine learning algorithms be trained to adapt to and compensate for these inter-machine variations in real-time, thereby enabling more universal process monitoring?

05

Design Principles

"Standardize process parameters and control strategies where possible, or develop robust monitoring systems that can adapt to machine-specific variations."

This variability is critical for designers and engineers involved in automated manufacturing and process monitoring. Understanding these differences is essential for ensuring the reliability and accuracy of production processes, especially when implementing systems that rely on reference signals for monitoring and fault detection.

06

What This Means for Your Design

Imagine you give the exact same instructions to two different robots. Even though the instructions are identical, the robots might move a little differently, especially when they speed up or slow down, because they are built slightly differently or have different settings. This research shows that these small differences can add up and affect how we monitor the robots during their work.

How to use in your project

  • 1.Reference this study when discussing the challenges of ensuring consistency in manufacturing processes or when justifying the need for machine-specific calibration in your design project.
07

Add to My Project

08

Quick Cite

Paragraph starter

This research by Denkena et al. (2023) demonstrates that identical NC-code executed on different machine tools results in significant variations in timing and positioning, primarily due to differences in control parameters, strategies, and physical drive limitations. These discrepancies, particularly noticeable during axis acceleration and deceleration, accumulate over time and can impact the effectiveness of online monitoring systems, underscoring the need for machine-specific calibration or adaptive process control in automated manufacturing.

09

Source

Procedia CIRP

Identical NC-code on Different Machine Tools – Similarities and Differences in Timing and Positioning

journal · 2023

View source

Questions About This Research

What does the research say about identical nc-code yields divergent machine tool behaviour due to control parameters and physical limitations?
When deploying automated production processes or monitoring systems across multiple machines, anticipate and account for inherent variations in machine tool behaviour, even when using identical programming. Evidence: Procedia CIRP (2023).
Why does "Identical NC-code yields divergent machine tool behaviour due to control parameters and physical limitations." matter for design?
This variability is critical for designers and engineers involved in automated manufacturing and process monitoring. Understanding these differences is essential for ensuring the reliability and accuracy of production processes, especially when implementing systems that rely on reference signals for monitoring and fault detection.
How can designers apply this research?
When deploying automated production processes or monitoring systems across multiple machines, anticipate and account for inherent variations in machine tool behaviour, even when using identical programming.
What were the main findings?
Significant differences were observed in traverse speeds, traverse paths, machining feed speeds, and machining feed paths when using identical NC code on different machines.. Differences were most pronounced during axis acceleration and deceleration phases.. Control parameters, control strategies, and physical drive limitations were identified as primary sources of variation.. Accumulated differences over prolonged machining periods can become significant for online monitoring systems.
What research method was used?
Comparative analysis of process signals.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2023 journal from Procedia CIRP.
What should I do differently in my next project?
Before deploying a new process monitoring system or automated workflow across multiple machines, conduct comparative tests using identical NC code to map out and understand the expected variations in timing and positioning.
What are the limitations?
The study focused on specific types of machine tools and machining operations; results may vary for different machinery or processes. The range of control parameters and strategies explored might not cover all possible variations.