ArticleMaterials (Basel, Switzerland)2025
Dimensional Accuracy and Measurement Variability in CNC-Turned Parts Using Digital Vernier Calipers and Coordinate Measuring Machines Across Five Materials.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Microengineered bone models: advances and applications of bone-on-a-chip technology.Journal of biological engineering · 2026Review
- Influence of thermal and mechanical properties on surface integrity in CNC turning across multiple engineering materials.Scientific reports · 2026Article
- A Dual-Mean Statistical and Multivariate Framework for Machinability Evaluation in CNC Turning: Gradient and Stiffness Analysis Across Five Materials.Materials (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Attaining dimensional accuracy in CNC-machined parts is essential for high-precision manufacturing, especially when working with materials that exhibit varying mechanical and thermal characteristics. This research provides a thorough experimental comparison of manual and automated metrological systems, specifically the Digital Vernier Caliper (DVC) and Coordinate Measuring Machine (CMM), as applied to five different engineering alloys through five progressively machined axial zones. The study assesses absolute error, relative error, standard deviation, and measurement repeatability, factoring in material hardness, thermal conductivity, and surface changes due to machining. The results indicate that DVC performance is significantly affected by operator input and surface irregularities, with standard deviations reaching 0.03333 mm for Bronze C51000 and relative errors surpassing 1.02% in the initial zones. Although DVC occasionally showed lower absolute errors (e.g., 0.206 mm for Aluminum 6061), these advantages were countered by greater uncertainty and poor repeatability. In comparison, CMM demonstrated enhanced precision and consistency across all materials, with standard deviations below 0.0035 mm and relative errors being neatly within the 0.005-0.015% range, even with challenging alloys like Stainless Steel 304. Furthermore, a Principal Component Analysis (PCA) was conducted to identify underlying measurement-property relationships. The PCA highlighted clear groupings based on sensitivity to error in manual versus automated methods, facilitating predictive classification of materials according to their metrological reliability. The introduction of multivariate modeling also establishes a new framework for intelligent metrology selection based on material characteristics and machining responses. These results advocate for using CMM in applications requiring precise tolerances in the aerospace, biomedical, and high-end tooling sectors, while suggesting that DVC can serve as an auxiliary tool for less critical evaluations. This study provides practical recommendations for aligning measurement techniques with Industry 4.0's needs for accuracy, reliability, and data-driven quality assurance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.