In a survey from August 2019, ** percent of global advertisers said they preferred short-term results of campaigns (such as increase in sales or engagement) to determine ROI, while the same was true for ** percent of agencies and ** percent of media companies.

Measurements were taken on days of experiment (DOE) that are signified by X.

Market Overview:

The global inner diameter measurement system market is projected to grow at a CAGR of 6.5% from 2022 to 2030. The market growth is attributed to the increasing demand for inner diameter measurement systems in various applications such as automotive, manufacturing, pharmaceuticals & healthcare, construction and others. In terms of type, the optical method segment is expected to lead the global inner diameter measurement system market during the forecast period owing to its advantages such as accuracy and precision.

Product Definition:

The inner diameter measurement system is a type of measurement system that uses a helical or spiral groove to measure the inside diameter of objects. This system has several advantages over other types of measurement systems, including its ability to accurately measure small diameters and its low cost. Additionally, the inner diameter measurement system is often used in conjunction with other measurement systems, such as the outside diameter measurement system, to create a complete description of an object's dimensions.

Optical Method:

The optical method is a non-destructive testing technology that uses light to measure the internal diameter of tubes and pipes. It provides highly accurate results in a short period as compared to other conventional methods. The optical method can be used for both, thin-walled as well as thick-walled tubes and pipes.

Triangulation Method:

The Triangulation method is used to measure the inner diameter of a pipe or tube. It uses three points to determine the size of an object. The method can be used for any shape and is not limited to round objects only. It measures the largest point on one side, then the middle point and finally measures from that point on the other side using the Law of Cosines property which gives you an angle measurement to calculate the inner diameter.

Spectral Interference Method:

The spectral interference method is a type of inner diameter measurement system that uses the principle of interference to determine the size of an object. This system is advantageous because it is relatively easy to use and can be used in a variety of environments. Additionally, this method is accurate and can be used to measure objects of a variety of shapes and sizes.

Application Insights:

The automotive application segment accounted for the largest share of over 30.0% in 2019 and is expected to continue its dominance over the forecast period. The system is widely used in vehicle manufacturing facilities, engine production, body shops and others to measure the dimensional accuracy of various components such as gears, shafts, bearings etc., which are critical to ensure a safe and reliable functioning of a component or an entire assembly.

The pharmaceutical & healthcare application segment accounted for nearly 19.0% share in 2019 owing to high demand from medical device manufacturers who require consistent quality standards across all processes involved with product development & manufacturing including raw material procurement & distribution as well as component fabrication activities at different levels throughout the supply chain network. In addition, stringent regulatory requirements related to patient safety along with rising awareness among end-users regarding process reliability are further anticipated to propel the market growth during the forecast period.

Regional Analysis:

The Asia Pacific dominated the global inner diameter measurement system sales in 2015 and is expected to continue its dominance over the forecast period. This can be attributed to increasing automotive production, high manufacturing volume of electronics products, rapid urbanization and infrastructure development in this region. The growing construction industry due to the rising population is also anticipated to drive product demand over the next eight years.

North America accounted for a significant share of global market revenue owing to early adoption by manufacturers in this region coupled with high R&D investments by universities & research institutes for developing innovative sensors technology. Europe accounted for the second-largest share of global market revenue owing to the presence of major automobile manufacturers such as Daimler AG; BMW Group; Fiat Chrysler Automobiles N.V.; Ferrari S.pA.; Mercedes-Benz AMG GmbH; Porsche AG etc., which are continuously investing in new technologies such as lightweight materials, electric motors and batteries, etc.

This classification contains the codes for the different types of measurement. To create this classification we have used the proposal used by Google, in its standard Dataset Publishing Language (DSPL).

We designed two new samplers for monitoring airborne particulates, including fungal and fern spores and plant pollen, that rely on natural wind currents (Passive Environmental Sampler) or a battery operated fan (Active Environmental Sampler). Both samplers are modeled after commercial devices such as the Rotorod® and the Burkard® samplers, but are more economical and require less maintenance than commercial devices. We compared our two new samplers to Rotorod® samplers using Xyleborus spp. boring dust (frass) known to contain fungi responsible for Rapid Ohia Death. The comparison was done in a large outdoor field cage to determine relative effectiveness of the three samplers for capturing windblown boring dust. The dataset contains measurements of boring dust particles that were captured by the three types of samples over the course of twelve trials.

Form Measurement Equipment Market Outlook

According to our latest research, the global form measurement equipment market size reached USD 2.67 billion in 2024, exhibiting robust expansion driven by technological advancements and the increasing need for precision engineering across key industries. The market is poised to grow at a CAGR of 5.8% from 2025 to 2033, with revenues forecasted to reach USD 4.48 billion by 2033. Growth in the sector is primarily fueled by escalating demand for quality assurance in manufacturing, rising automation, and the integration of digital technologies in metrology processes.

The primary growth factor for the form measurement equipment market is the accelerating adoption of advanced manufacturing practices, particularly in sectors such as automotive, aerospace, and electronics. As manufacturers strive for higher product quality and compliance with stringent international standards, the need for precise dimensional and geometric measurements has intensified. This has led to increased investments in coordinate measuring machines, optical measurement systems, and surface roughness testers, which are essential for ensuring product reliability and reducing operational inefficiencies. Furthermore, the proliferation of Industry 4.0 and smart factory initiatives has amplified the integration of form measurement equipment with digital platforms, enabling real-time data acquisition, analysis, and feedback for continuous process improvement.

Another significant driver is the growing emphasis on research and development activities across various industries. With innovation becoming a key differentiator, companies are heavily investing in R&D to develop new materials, components, and products. This trend has elevated the role of form measurement equipment in laboratories and research centers, where high-precision instruments are required to validate design specifications and support prototyping. Additionally, the increasing complexity of modern components, especially in the medical device and electronics sectors, has necessitated the use of advanced measurement technologies capable of capturing intricate geometries and surface profiles with exceptional accuracy.

The market's expansion is also supported by the rising focus on quality control and regulatory compliance. Industries such as energy, power, and medical devices are subject to rigorous standards that mandate comprehensive inspection and validation of components before deployment. Form measurement equipment plays a pivotal role in ensuring that products meet these requirements, thereby minimizing the risk of recalls, failures, and legal liabilities. The integration of software-driven measurement solutions further enhances the efficiency and traceability of quality assurance processes, enabling organizations to maintain a competitive edge in global markets.

Regionally, Asia Pacific continues to dominate the form measurement equipment market, accounting for the largest share in 2024, driven by rapid industrialization, expanding manufacturing bases, and increasing investments in automation technologies. North America and Europe also represent significant markets, characterized by high adoption rates of advanced metrology solutions and a strong focus on innovation. Emerging economies in Latin America and the Middle East & Africa are witnessing steady growth, supported by government initiatives to boost industrial productivity and infrastructure development. The regional outlook remains positive, with Asia Pacific expected to maintain its leadership position throughout the forecast period, while other regions are projected to exhibit healthy growth rates as they embrace digital transformation in manufacturing.

Product Type Analysis

The product type segment of the form measurement equipment market encompasses a diverse range of instruments, including coordinate measuring machines (CMMs), optical measurement systems, surface roughness testers, roundness measuring instruments, and other specialized tools. Among these, CMMs hold a substantial share, owing to their versatility in measuring complex geometries and their widespread adoption in automotive and aerospace industries. These machines provide high precision and repeatability, making them indispensable for quality assurance and inspection tasks. Optical measurement systems are gaining traction due to their non-contact nature, which is ideal for delicate or small components, and their ability

The dataset is presented in the paper:

Building and analysing a labelled Measure While Drilling dataset from 15 hard rock tunnels in Norway, by T.F. Hansen, Z. Liu, J. Torressen

The paper has a preprint on SSRN: http://dx.doi.org/10.2139/ssrn.4729646 and is under review in a peer-reviewed journal.

The dataset is utilised in a machine learning analysis in the paper:

Predicting rock type from MWD tunnel data using a reproducible ML-modelling process, by T.F. Hansen, Z. Liu, J. Torressen

The paper is published in the journal Tunnelling and Underground Space Technology:

https://doi.org/10.1016/j.tust.2024.105843

Description of the dataset:

Measure While Drilling (MWD) is a technique in rock drilling, mainly used in drill and blast tunnelling, where data about the rock mass is registered by sensors while drilling. The extensive and geologically diversified dataset contains corresponding MWD-data and rock mass mappings for 5205 blasting rounds from 15 hard rock tunnels in Norway. MWD-data are presented as tabular data. 10 different rocktypes are the corresponding labels.

Four files are given:

A csv-file of the training dataset - with outliers removed

A csv-file of the testing dataset (split train/test 0.75/0.25) - with outliers removed

A csv-file with the full unsplitted dataset, cleaned and with outliers removed

A csv-file with the raw dataset, before cleaning, processing and outlier removal

The author gratefully acknowledge the tunnel software/hardware company Bever Control, which have facilitated data from the clients Bane NOR, Statens Vegvesen, Nye Veier, and the contractor AF-Gruppen.

NOTE: The dataset is only available for research, no commercial use.

In this paper, we generalize the notion of measurement error on deterministic sample datasets to accommodate sample data that are random-variable-valued. This leads to the formulation of two distinct kinds of measurement error: intrinsic measurement error, and incidental measurement error. Incidental measurement error will be recognized as the traditional kind that arises from a set of deterministic sample measurements, and upon which the traditional measurement error modelling literature is based, while intrinsic measurement error reflects some subjective quality of either the measurement tool or the measurand itself. We define calibrating conditions that generalize common and classical types of measurement error models to this broader measurement domain, and explain how the notion of generalized Berkson error in particular mathematicizes what it means to be an expert assessor or rater for a measurement process. We then explore how classical point estimation, inference, and likelihood theory can be generalized to accommodate sample data composed of generic random-variable-valued measurements.

Form Measurement Equipment Market Outlook

According to our latest research, the global Form Measurement Equipment market size reached USD 2.21 billion in 2024, demonstrating robust growth driven by increasing demand for precision engineering and quality assurance across industries. The market is set to expand at a CAGR of 5.9% from 2025 to 2033, with the total market value projected to reach USD 3.91 billion by 2033. This growth is primarily fueled by advancements in manufacturing automation, stringent quality control standards, and the rapid evolution of high-precision industries such as automotive, aerospace, and electronics.

One of the primary growth factors for the Form Measurement Equipment market is the ongoing evolution of advanced manufacturing technologies. Industries are increasingly adopting automation and digitalization, which demand high-precision measurement tools to ensure product quality and compliance with international standards. The proliferation of smart factories and Industry 4.0 initiatives has intensified the need for sophisticated metrology solutions, including coordinate measuring machines and optical measurement systems. These technologies enable manufacturers to enhance their operational efficiency, minimize defects, and reduce waste, thereby driving the adoption of form measurement equipment globally.

Another significant driver is the rising emphasis on quality assurance and regulatory compliance, particularly in highly regulated sectors such as automotive, aerospace, and medical devices. These industries require stringent adherence to dimensional accuracy and surface finish standards to ensure safety, performance, and reliability. As a result, companies are investing heavily in state-of-the-art form measurement equipment to meet these rigorous requirements. The integration of advanced metrology software and hardware solutions allows organizations to conduct real-time inspections, streamline quality control processes, and maintain competitive advantage in a rapidly evolving marketplace.

Additionally, the increasing complexity of electronic components and miniaturization trends in the semiconductor industry are fueling demand for high-precision measurement solutions. As electronic devices become smaller and more intricate, manufacturers must rely on advanced form measurement equipment to achieve the necessary tolerances and surface characteristics. This trend is particularly pronounced in Asia Pacific, where the electronics manufacturing sector is experiencing exponential growth. Furthermore, ongoing research and development activities in material science and nanotechnology are generating new opportunities for the adoption of innovative measurement technologies, further propelling the market forward.

From a regional perspective, Asia Pacific continues to dominate the Form Measurement Equipment market, accounting for the largest share in 2024, followed by North America and Europe. The strong presence of automotive, electronics, and industrial manufacturing hubs in countries such as China, Japan, South Korea, and India is a key factor driving market expansion in this region. North America and Europe are also witnessing steady growth, supported by technological advancements, high investments in research and development, and stringent quality standards. Meanwhile, emerging markets in Latin America and the Middle East & Africa are gradually adopting advanced metrology solutions as they enhance their manufacturing capabilities and infrastructure.

The Roundness Measuring Machine plays a pivotal role in ensuring the precision and quality of components where circularity is crucial. Industries such as automotive and aerospace heavily rely on these machines to verify the roundness and cylindricity of parts like bearings, shafts, and engine components. The accuracy of these measurements is vital for the performance and longevity of mechanical systems, making roundness measuring machines indispensable in high-stakes manufacturing environments. As technology advances, these machines are becoming more sophisticated, integrating with automated systems to provide real-time data and enhance production efficiency. Their ability to deliver precise measurements with minimal human intervention is driving their adoption across various sectors, underscoring their importance in modern manufacturing

Global Ultrasonic Body Scale Market By Type,By Application,By Region - Trends, Analysis and Forecast till the course of 2029

The National Measurement Office provides a range of type approval services designed to enable manufacturers gain access to European and global markets for weighing and other regulated measuring instruments. Where a manufacture's instrument complies with the regulation, European Directives and International Recommendations (OIML), a certificate of conformity is issued. These data sets show which companies have been issued a certificate and which product it relates to.

Home Health Care Agency Ratings

Quality Measurements, Types of Services and More

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides a list of all Home Health Agencies registered with Medicare. Contained within this dataset is information on each agency's address, phone number, type of ownership, quality measure ratings and other associated data points. With this valuable insight into the operations of each Home Health Care Agency, you can make informed decisions about your care needs. Learn more about the services offered at each agency and how they are rated according to their quality measure ratings. From dedicated nursing care services to speech pathology to medical social services - get all the information you need with this comprehensive look at U.S.-based Home Health Care Agencies!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Are you looking to learn more about Home Health Care Agencies registered with Medicare? This dataset can provide quality measure ratings, addresses, phone numbers, types of services offered and other information that may be helpful when researching Home Health Care Agencies.

This guide will explain how to use the data in this dataset to gain a better understanding of Home Health Care Agencies registered with Medicare.

First, you will need to become familiar with the columns in the dataset. A list of all columns and their associated descriptions is provided above for your reference. Once you understand each column’s purpose, it will be easier for you to decide what metrics or variables are most important for your own research.

Next, use this data to compare various facets between different Home Health Care Agencies such as type of ownership, services offered and quality measure ratings like star rating or CMS certification number (from 0-5 stars). Collecting information from multiple sources such as public reviews or customer feedback can help supplement these numerical metrics in order to paint a more accurate picture about each agency's performance and customer satisfaction level.

Finally once you have collected enough data points on one particular agency or a comparison between multiple agencies then conduct more analysis using statistical methods like correlation matrices in order to determine any patterns that exist within the data set which may reveal valuable insights into topic of research at hand

Research Ideas

• Using the data to compare quality of care ratings between agencies, so people can make better informed decisions about which agency to hire for home health services.
• Analyzing the costs associated with different types of home health care services, such as nursing care and physical therapy, in order to determine where money could be saved in health care budgets.
• Evaluating the performance of certain agencies by analyzing the number of episodes billed to Medicare compared to their national averages, allowing agencies with lower numbers of billing episodes to be identified and monitored more closely if necessary

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description | |:----------------------------------------...

Rail Transport Statistics: Goods transported by destination. Annual. National.

T1DiabetesGranada

A longitudinal multi-modal dataset of type 1 diabetes mellitus

Documented by:

Rodriguez-Leon, C., Aviles-Perez, M. D., Banos, O., Quesada-Charneco, M., Lopez-Ibarra, P. J., Villalonga, C., & Munoz-Torres, M. (2023). T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus. Scientific Data, 10(1), 916. https://doi.org/10.1038/s41597-023-02737-4

Background

Type 1 diabetes mellitus (T1D) patients face daily difficulties in keeping their blood glucose levels within appropriate ranges. Several techniques and devices, such as flash glucose meters, have been developed to help T1D patients improve their quality of life. Most recently, the data collected via these devices is being used to train advanced artificial intelligence models to characterize the evolution of the disease and support its management. The main problem for the generation of these models is the scarcity of data, as most published works use private or artificially generated datasets. For this reason, this work presents T1DiabetesGranada, a open under specific permission longitudinal dataset that not only provides continuous glucose levels, but also patient demographic and clinical information. The dataset includes 257780 days of measurements over four years from 736 T1D patients from the province of Granada, Spain. This dataset progresses significantly beyond the state of the art as one the longest and largest open datasets of continuous glucose measurements, thus boosting the development of new artificial intelligence models for glucose level characterization and prediction.

Data Records

The data are stored in four comma-separated values (CSV) files which are available in T1DiabetesGranada.zip. These files are described in detail below.

Patient_info.csv

Patient_info.csv is the file containing information about the patients, such as demographic data, start and end dates of blood glucose level measurements and biochemical parameters, number of biochemical parameters or number of diagnostics. This file is composed of 736 records, one for each patient in the dataset, and includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Sex – Sex of the patient. Values: F (for female), masculine (for male)

Birth_year – Year of birth of the patient. Format: YYYY.

Initial_measurement_date – Date of the first blood glucose level measurement of the patient in the Glucose_measurements.csv file. Format: YYYY-MM-DD.

Final_measurement_date – Date of the last blood glucose level measurement of the patient in the Glucose_measurements.csv file. Format: YYYY-MM-DD.

Number_of_days_with_measures – Number of days with blood glucose level measurements of the patient, extracted from the Glucose_measurements.csv file. Values: ranging from 8 to 1463.

Number_of_measurements – Number of blood glucose level measurements of the patient, extracted from the Glucose_measurements.csv file. Values: ranging from 400 to 137292.

Initial_biochemical_parameters_date – Date of the first biochemical test to measure some biochemical parameter of the patient, extracted from the Biochemical_parameters.csv file. Format: YYYY-MM-DD.

Final_biochemical_parameters_date – Date of the last biochemical test to measure some biochemical parameter of the patient, extracted from the Biochemical_parameters.csv file. Format: YYYY-MM-DD.

Number_of_biochemical_parameters – Number of biochemical parameters measured on the patient, extracted from the Biochemical_parameters.csv file. Values: ranging from 4 to 846.

Number_of_diagnostics – Number of diagnoses realized to the patient, extracted from the Diagnostics.csv file. Values: ranging from 1 to 24.

Glucose_measurements.csv

Glucose_measurements.csv is the file containing the continuous blood glucose level measurements of the patients. The file is composed of more than 22.6 million records that constitute the time series of continuous blood glucose level measurements. It includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Measurement_date – Date of the blood glucose level measurement. Format: YYYY-MM-DD.

Measurement_time – Time of the blood glucose level measurement. Format: HH:MM:SS.

Measurement – Value of the blood glucose level measurement in mg/dL. Values: ranging from 40 to 500.

Biochemical_parameters.csv

Biochemical_parameters.csv is the file containing data of the biochemical tests performed on patients to measure their biochemical parameters. This file is composed of 87482 records and includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Reception_date – Date of receipt in the laboratory of the sample to measure the biochemical parameter. Format: YYYY-MM-DD.

Name – Name of the measured biochemical parameter. Values: 'Potassium', 'HDL cholesterol', 'Gammaglutamyl Transferase (GGT)', 'Creatinine', 'Glucose', 'Uric acid', 'Triglycerides', 'Alanine transaminase (GPT)', 'Chlorine', 'Thyrotropin (TSH)', 'Sodium', 'Glycated hemoglobin (Ac)', 'Total cholesterol', 'Albumin (urine)', 'Creatinine (urine)', 'Insulin', 'IA ANTIBODIES'.

Value – Value of the biochemical parameter. Values: ranging from -4.0 to 6446.74.

Diagnostics.csv

Diagnostics.csv is the file containing diagnoses of diabetes mellitus complications or other diseases that patients have in addition to type 1 diabetes mellitus. This file is composed of 1757 records and includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Code – ICD-9-CM diagnosis code. Values: subset of 594 of the ICD-9-CM codes (https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes).

Description – ICD-9-CM long description. Values: subset of 594 of the ICD-9-CM long description (https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes).

Technical Validation

Blood glucose level measurements are collected using FreeStyle Libre devices, which are widely used for healthcare in patients with T1D. Abbott Diabetes Care, Inc., Alameda, CA, USA, the manufacturer company, has conducted validation studies of these devices concluding that the measurements made by their sensors compare to YSI analyzer devices (Xylem Inc.), the gold standard, yielding results of 99.9% of the time within zones A and B of the consensus error grid. In addition, other studies external to the company concluded that the accuracy of the measurements is adequate.

Moreover, it was also checked in most cases the blood glucose level measurements per patient were continuous (i.e. a sample at least every 15 minutes) in the Glucose_measurements.csv file as they should be.

Usage Notes

For data downloading, it is necessary to be authenticated on the Zenodo platform, accept the Data Usage Agreement and send a request specifying full name, email, and the justification of the data use. This request will be processed by the Secretary of the Department of Computer Engineering, Automatics, and Robotics of the University of Granada and access to the dataset will be granted.

The files that compose the dataset are CSV type files delimited by commas and are available in T1DiabetesGranada.zip. A Jupyter Notebook (Python v. 3.8) with code that may help to a better understanding of the dataset, with graphics and statistics, is available in UsageNotes.zip.

Graphs_and_stats.ipynb

The Jupyter Notebook generates tables, graphs and statistics for a better understanding of the dataset. It has four main sections, one dedicated to each file in the dataset. In addition, it has useful functions such as calculating the patient age, deleting a patient list from a dataset file and leaving only a patient list in a dataset file.

Code Availability

The dataset was generated using some custom code located in CodeAvailability.zip. The code is provided as Jupyter Notebooks created with Python v. 3.8. The code was used to conduct tasks such as data curation and transformation, and variables extraction.

Original_patient_info_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with patient data. Mainly irrelevant rows and columns are removed, and the sex variable is recoded.

Glucose_measurements_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with the continuous glucose level measurements of the patients. Principally rows without information or duplicated rows are removed and the variable with the timestamp is transformed into two new variables, measurement date and measurement time.

Biochemical_parameters_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with patient data of the biochemical tests performed on patients to measure their biochemical parameters. Mainly irrelevant rows and columns are removed and the variable with the name of the measured biochemical parameter is translated.

Diagnostic_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with patient data of the diagnoses of diabetes mellitus complications or other diseases that patients have in addition to T1D.

Get_patient_info_variables.ipynb

In the Jupyter Notebook it is coded the feature extraction process from the files Glucose_measurements.csv, Biochemical_parameters.csv and Diagnostics.csv to complete the file Patient_info.csv. It is divided into six sections, the first three to extract the features from each of the mentioned files and the next three to add the extracted features to the resulting new file.

Data Usage Agreement

The conditions for use are as follows:

You confirm that you will not attempt to re-identify research participants for any reason, including for re-identification theory research.

You commit to keeping the T1DiabetesGranada dataset confidential and secure and will not redistribute data or Zenodo account credentials.

You will require

These original measurement data relate to the publication: J. Schaude, A. C. Gröschl, T. Hausotte: Effect of a Misidentified Centre of a Type ASG Material Measure on the Determined Topographic Spatial Resolution of an Optical Point Sensor, Metrology 2(1), p. 19-32, 2022, https://doi.org/10.3390/metrology2010002. Please refer to this open access publication for a detailed description of the measurement setup and procedure.

All data are in ASCII-format. Each file contains four columns, where column one to three are the x, y, and z-coordinates of the positioning system and column four is the signal of the photodetector.

Content of the folders

10_Plane: Axial probings on 18 points just outside the grooves.

20_Edges: Lateral probings from each point just outside the grooves in the direction of the roughly determined centre of the material measure.

30_PlaneArea: Repeated axial probing on a plane area near the material measure.

40_RadialMeasurement: Radial measurement of the material measure by lateral (radial) probings conducted on different heights (referring to the distance to the plane fitted to the measuring points of 10_Plane), and radii (referring to the centre of the circle fitted to the edges determined in 20_Edges). Each radial probing has its own data file, with the name of the data file being “radial_probing {radius in m} {height in m} {data and time of probing}.txt”.

50_LineMeasurement: Lateral probings conducted on different heights (referring to the distance to the plane fitted to the measuring points of 10_Plane), different lateral offsets (referring to the centre of the circle fitted to the edges determined in 20_Edges) and on two angles (on the groove (0°) and the adjacent top level (10°)). Please refer to sec. 5.2 of the aforementioned publication for a detailed description. Each lateral probing has its own data file, with the name of the file being “lateral probing {angle in °} {offset in m} {height in m} {data and time of probing}.txt”.

Acknowledgement

This project 20IND07 TracOptic has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. Funder name: European Metrology Programme for Innovation and Research (EMPIR); Funder ID: 10.13039/10001413

Roundness Measuring Machine Market Outlook

The global roundness measuring machine market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 2.5 billion by 2032, with a compound annual growth rate (CAGR) of 8.5%. The market's growth is driven by the increasing demand for precision engineering, particularly in high-precision industries such as automotive and aerospace, where the accuracy of components is critical.

One of the key growth factors for the roundness measuring machine market is the continuous advancement in manufacturing technologies. Industries are increasingly adopting automation and precision engineering to enhance the quality and efficiency of their production processes. The need for highly accurate measurements has made roundness measuring machines indispensable in quality control processes. Additionally, the integration of advanced software solutions with these machines has improved their functionality, making them more user-friendly and efficient.

Another significant growth driver is the rising demand in the automotive sector. With the automotive industry constantly evolving to meet stringent emission norms and safety standards, the need for precision-engineered components is higher than ever. Roundness measuring machines play a crucial role in ensuring the accuracy of these components, thus bolstering their adoption in the sector. Moreover, the increasing production of electric vehicles, which require high precision in components, is expected to further drive market growth.

The aerospace industry also contributes significantly to the market's expansion. The sector's high demand for precision and reliability in components makes roundness measuring machines essential. As aerospace manufacturers strive to meet the high standards of safety and performance, the adoption of these machines is expected to surge. Additionally, the growing emphasis on reducing waste and improving material efficiency in aerospace manufacturing processes further supports market growth.

Regionally, the Asia Pacific region is expected to witness substantial growth in the roundness measuring machine market. The region's robust manufacturing base, particularly in countries like China, Japan, and South Korea, drives the demand for advanced measurement solutions. Additionally, the increasing investments in the automotive and aerospace sectors in these countries contribute to market expansion. North America and Europe also present significant growth opportunities due to the presence of established automotive and aerospace industries and the continuous advancements in manufacturing technologies.

Product Type Analysis

When analyzing the roundness measuring machine market by product type, it is essential to consider the two main categories: contact type and non-contact type. Contact-type roundness measuring machines utilize tactile probes to measure the roundness of an object by physically touching it. These machines are highly accurate and are widely used in applications where high precision is required. Their reliability and ease of use make them a popular choice in various industries, including automotive and aerospace.

Non-contact type roundness measuring machines, on the other hand, employ optical or laser-based systems to measure the roundness of objects without physically touching them. These machines are advantageous in situations where the object being measured is delicate or has a surface that could be damaged by contact. Non-contact measurements are also faster and can be more suitable for high-volume production environments. The advancement in laser and optical technologies has significantly improved the accuracy and reliability of non-contact measuring machines, making them increasingly popular in industrial manufacturing and medical applications.

The choice between contact type and non-contact type roundness measuring machines often depends on the specific requirements of the application. For instance, in the automotive industry, where components such as engine parts and transmission gears require high precision, contact type machines are preferred for their accuracy. In contrast, non-contact machines are more suitable for measuring medical devices and components that are sensitive to physical contact.

Overall, both product types are expected to witness significant growth during the forecast period. The continuous advancements in measurement technologies and the increasing demand for high-precision components across various ind

Track Measurement Market size was valued at USD 2.1 Billion in 2024 and is projected to reach USD 4.3 Billion by 2032, growing at a CAGR of 8.5% during the forecast period 2026-2032.Track Measurement Market is driven by growing railway infrastructure projects, increasing focus on safety and maintenance efficiency, and rising adoption of advanced sensing and monitoring technologies.