This dataset contains a comprehensive collection of data collected durring additive and subtractive manufacturing operations. it is design to facilitae research and development in manufacturing for the optizimation of production process paramters. The dataset includes images of 3D printed workpieces, as well as detailed CNC Milling machine data in both csv and pkl formats.

Dataset Contents:

1. 3D printed workpiece images: A collection of 3D printed workpiece images made from different materials, captured after the CNC milling operations. These images provide a visual representation of the workpieces quality after the subtractive manufactrring operations .
2. Dataset.csv: A CSV file containing data related to the milling operations performed on individual materials. This file includes various parameters recorded during the milling process, such as feed rate, spindle speed, Gcode parameters and data about temperature and power.
3. Dataset.pkl: A PKL file ontaining data related to the milling operations performed on individual materials. This file provides a detailed logs of machine state change durring the cnc machining operations that can be loaded into Python for analysis.

Note : This dataset is published as part of our recent work-in-progress paper (A Multi-Material and Multi-Scenario Dataset for
Additive and Subtractive Manufacturing Operations), which has been accepted at the IEEE ETFA 2024 - IEEE International Conference on Emerging Technologies and Factory Automation. This is the first version of the dataset, and we are working collecting data from other manufacturing operations. Any modifications or updates to this dataset will be included in future versions.

Data created and collected to address the Energy Consumption use case. It comprises measurements collected from a specific set of sensors during machining processes.

This dataset is an extensive production data set for a five-axis CNC milling process. Three geometrically different products were manufactured and production data from the machine was recorded. The recorded manufacturing process contains the preparation of the machine for the next product (changeover) as well as the machining process (production). An experimental manufacturing was organized with the aid of a changeover matrix to ensure that all possible changeover combinations for the three products were considered. The production was repeated five times, resulting in 30 manufacturing sessions and five complete changeover matrices. The data set was recorded from a Siemens 840D-SL machine control on a five-axis milling machine tool of type "Spinner U5-620" in a laboratory environment. A rich feature set is provided including rich supplementary material i.e. the NC-codes of the products, tool information, and a Jupyter notebook to illustrate the usage of the dataset.

The supplementary material can be found at GitHub: https://github.com/ElMoe/Production-Data-Set-for-Five-Axis-CNC-Milling-with-Multiple-Changeovers" target="_blank" rel="noopener">Link

The corresponding data descriptor is available here: Link

i.e. dry run or milling

This data set contains milling itineraries and granulometric properties of the resulting powders obtained from a collection of by-products from crops (flax fibres, hemp core, rice husk, wheat straw) representative of currently used lignocellulosic biomass. The data are gathered in four tables. Table 1 provides a precise description of milling itineraries associated with produced powder samples. Table 2 reports the granulometric characteristics of all produced powder samples. The kinetics of particle size reduction for a subset of produced powder samples are gathered in Table 3. In Table 1, a milling itinerary is described by a set of unit operations and associated with a unique numerical identifier (column Experience number). Each unit operation of a given itinerary is described on a separate line and associated with a unique numerical identifier (columns Experience number + Process step number). Numerical value associated with Process step number column indicates temporal order (e,g step 1 is before step 2). In case of parallel unit operations, a dot is used (e,g step 1.1 is parallel to step 1.2). The following information are reported: date; biomass nature and name (Biomass) ; processing operation type (Treatment); drying mode/moisture content (Treatment); biomass quantity; equipment name and type (Material); rotation speed; vibration frequency; mass of balls; sieving grid size (sieving size); treatment duration; temperature; output solid quantity (Output solid constituent quantity); output solid yield (Output solid constituent quantity), characterized sample product name (Sample Product1). In Table 2, are found the data concerning particles and powders characterization: characterized sample product name (Sample Product); biomass name and nature (Biomass); d50 calculation method; d50 in volume; d50 in number; d10 calculation method; d10 in volume; d10 in number; d90 calculation method; d90 in volume; d90 in number; span; specific surface area calculation method; specific surface area value. Table 3 has the same structure that Table 2 extended by an additional column (Sampling time) indicating the time at which a sample has been extracted during the milling for granulometric characterisation. Table 4 contains the whole set of full particle size distributions of all samples of Table 3. An additional file describres SOP (Standard Operating Procedure) parameter conditions used for granulometric characterization.

The data set contains raw data (Pxxx_Fyy_Czz.csv files) and processed data (a file with designated features - FeatureAndMetadata_Milling.csv) from the full life cycle of 14 cutting tools used in the milling process. The tools performed 968 milling cycles. The data contain vibration signals (8 measuring channels from the spindle and work table) and current signals (12 measuring channels from the spindle and work table).

A metadata file is also available, in which each cycle is assigned process data (e.g. tool number, sample number, sample hardness)

The data set is useful for work on the classification of tool condition or estimation of their service life.

It is possible to use only FeatureAndMetadata_Milling.csv and work with calculated features or download all files and work with raw data.

This data set contains milling itineraries and granulometric properties of the resulting powders obtained from a collection of by-products from woods (pine wood pellets, pine bark, pine sawdust, Douglas shavings, chesnut tree sawdust) representative of currently used lignocellulosic biomass. The data are gathered in four tables. Table 1 provides a precise description of milling itineraries associated with produced powder samples. Table 2 reports the granulometric characteristics of all produced powder samples. The kinetics of particle size reduction for a subset of produced powder samples are gathered in Table 3. In Table 1, a milling itinerary is described by a set of unit operations and associated with a unique numerical identifier (column Experience number). Each unit operation of a given itinerary is described on a separate line and associated with a unique numerical identifier (columns Experience number + Process step number). Numerical value associated with Process step number column indicates temporal order (e,g step 1 is before step 2). In case of parallel unit operations, a dot is used (e,g step 1.1 is parallel to step 1.2). The following information are reported: date; biomass nature and name (Biomass) ; processing operation type (Treatment); drying mode/moisture content (Treatment); biomass quantity; equipment name and type (Material); rotation speed; vibration frequency; mass of balls; sieving grid size (sieving size); treatment duration; temperature; output solid quantity (Output solid constituent quantity); output solid yield (Output solid constituent quantity), characterized sample product name (Sample Product1). In Table 2, are found the data concerning particles and powders characterization: characterized sample product name (Sample Product); biomass name and nature (Biomass); d50 calculation method; d50 in volume; d50 in number; d10 calculation method; d10 in volume; d10 in number; d90 calculation method; d90 in volume; d90 in number; span; specific surface area calculation method; specific surface area value. Table 3 has the same structure that Table 2 extended by an additional column (Sampling time) indicating the time at which a sample has been extracted during the milling for granulometric characterisation. Table 4 contains the whole set of full particle size distributions of all samples of Table 3. An additional file describres SOP (Standard Operating Procedure) parameter conditions used for granulometric characterization.

During the current experimental testing, sensor data was collected to assess the condition of a machine tool via a 'fingerprint routine' that could be run at regular intervals, and a milling machining process of an aluminium workpiece. Physically simulated faults and errors were introduced to detect these in the collected signals. Machine tool failure modes:

Heavy tool– Load a significantly heavier tool than the baseline tool. Unbalanced– Load a tool that has a lower balancing classification than the baseline tool. Feedrate-adjusted– Conduct the fingerprint routine with a set of marginally reduced feed rate and spindle speed overrides (corresponding to an even spread of 6-10% reduction).

Machining process failure modes:

Misalignment – Tilt machine tool’s bed by A: 0.27°, B: 0.27°, C: 0.32°. Surface cracks – Drill 1.84mm diameter bores into the part, on the cutting path, before recorded trials. Tool wear – Wear the cutting tool severely before recorded trials.

The machining trials consisted of straight up-milling face cuts on 24 workpieces of aluminium with dimensions 200 x 120 x 85 mm held on a LANG vice inside a DMG Mori DMU 40 eVo linear CNC 5-axis milling machine. The ‘fingerprint routine’ consisted of isolated and combined movements of the X-, Y-, and Z-axes, as well as rotation of the spindle. Further details about the experimental procedures and the research can be found in the following publication: "The application of machine learning to sensor signals for machine tool and process health assessment", https://doi.org/10.1177/0954405420960892

The Dataset has been created in the context of the use case "Intelligent control of interconnected manufacturing infrastructures (i-CNC)" funded by the TrialsNet project (Horizon-SNS-JU-2022 - GA 101095871). The i-CNC use case has been conducted by CNC Solutions, LMS Lab/ University of Patras, and Fogus Innovations & Services P.C. The dataset is composed of two files. Each file includes measurements (vibration data) from sensors mounted on CNC Machines in Paiania (CNC Solutions machinery) and Patras (LMS Lab), respectively. Each measurement is associated with an indicator on the existence or not of the chatter phenomenon during the milling process. The indicator has been produced after applying an AI chatter detection mechanism on the collected data.

Dataset recorded in the process learning factory CiP at the Institute for Production Management, Technology and Machine Tools, TU Darmstadt for the purpose of demonstrating and testing InterQ developed solutions. It was used to demonstrate the framework's effectiveness in a case study, involving process monitoring of a real-world Computer Numerical Control milling process. The dataset includes accelerometer data and multiple types of realistic concept drifts. The uploaded file includes the link and password for accessing the dataset in PTW's cloud infrastructure.

The data set described encompasses features extracted from the machine control of a five-axis milling machine across thirteen series of productions. Each production series entails a setup changeover to prepare the machine for another product type. Alongside timestamps and twenty features derived from Numerical Control (NC) variables, the data set includes labels denoting various production phases. These labels, up to 23 in total, are structured around a generalized milling process. Comprising thirteen .csv files, each corresponding to a series production, the dataset was gathered within a production company operating in the contract manufacturing sector. These components are tied to actual series orders within ongoing industrial production.

The complete description of the data set is published here: https://doi.org/10.3390/data9050066

Shoulder Milling Tools Market Outlook

The global shoulder milling tools market size was estimated at USD 3.5 billion in 2023 and is projected to reach USD 5.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.8% during the forecast period. This robust growth trajectory is driven by advancements in manufacturing processes and the increasing demand for high precision parts in industries such as automotive and aerospace. The market's expansion is further fueled by the rising trend of automation in industrial operations and the continuous need to enhance productivity and efficiency in machining processes.

One of the key growth factors contributing to this expansion is the widespread adoption of shoulder milling tools in the automotive and aerospace sectors. These industries require high precision and reliable tools to manufacture complex parts, which has significantly driven the demand for advanced milling tools. The automotive sector, in particular, is witnessing a shift towards electric vehicles, requiring new and innovative designs that rely heavily on precise and efficient manufacturing tools. This shift is fostering increased investments in high-performance shoulder milling tools designed to meet the specific needs of these evolving markets.

Moreover, technological advancements in tool materials and coatings are playing a pivotal role in propelling the shoulder milling tools market forward. Innovations such as advanced carbide grades and multi-layer coatings have enhanced tool life and performance, enabling the machining of harder materials at higher speeds. These technological developments are crucial in reducing overall machining costs, thereby driving adoption across various end-user industries. Furthermore, the integration of smart technologies in milling tools, such as sensors for real-time data collection and analysis, is enhancing operational efficiencies and process optimization, further fueling market growth.

Another significant factor influencing market growth is the surge in the construction and metalworking industries across emerging economies. As these regions continue to industrialize, there is a growing need for efficient and durable machining tools. Shoulder milling tools are critical in the construction of machinery and other metal structures, thereby experiencing increased demand. Additionally, government initiatives promoting infrastructure development in countries like India and China are creating substantial opportunities for market expansion, as these initiatives necessitate advanced tools for construction and metalworking applications.

Regionally, the Asia Pacific region is expected to exhibit the highest growth rate in the shoulder milling tools market. This growth can be attributed to the rapid industrialization and urbanization in countries like China, India, and Japan. The presence of large manufacturing bases and the increasing investments in automotive and aerospace sectors are key factors driving the market in this region. Furthermore, supportive government policies and initiatives to boost domestic manufacturing capabilities are anticipated to further propel market growth in Asia Pacific.

Product Type Analysis

The shoulder milling tools market is segmented into indexable shoulder milling tools, solid carbide shoulder milling tools, and high-speed steel shoulder milling tools. Indexable shoulder milling tools hold a significant share of the market due to their versatility and cost-effectiveness. These tools allow for the replacement of worn-out inserts without the need to replace the entire tool body, making them highly economical for various applications. The flexibility they offer in terms of changing inserts for different materials and applications makes them indispensable in sectors like automotive and metalworking, where cost control and efficiency are paramount.

Solid carbide shoulder milling tools are gaining traction owing to their superior performance and durability, especially in high-speed machining operations. These tools are preferred when precision and high surface quality are critical, as they can maintain sharpness and integrity under extreme conditions. The aerospace industry, which demands exceptionally high precision and quality, is a major end-user of these tools. With increasing investments in the aerospace sector to cater to rising air travel demands, the adoption of solid carbide milling tools is expected to rise significantly during the forecast period.

High-speed steel (HSS) shoulder milling tools, while witnessing a slower growth compared to

Robotic Pipe Flange Milling Market Outlook

According to our latest research, the global robotic pipe flange milling market size in 2024 stands at USD 425 million, with a robust compound annual growth rate (CAGR) of 7.3% expected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 812 million, driven by rising industrial automation and the growing need for precision in pipeline maintenance across critical sectors. As per our latest research, the surging demand for enhanced operational efficiency and safety in industries such as oil & gas, power generation, and chemical processing is significantly propelling the growth of the robotic pipe flange milling market worldwide.

A key growth factor for the robotic pipe flange milling market is the accelerating adoption of automation technologies in heavy industries. Industries that rely on extensive pipeline networks, such as oil & gas and power generation, face increasing pressure to minimize downtime and ensure the structural integrity of their piping systems. Traditional manual flange milling processes are labor-intensive and prone to human error, which can result in costly operational delays and safety risks. Robotic milling systems offer a high degree of precision, repeatability, and speed, enabling operators to perform maintenance and repair tasks with minimal disruption. The integration of advanced sensors and real-time monitoring capabilities further enhances the efficiency and safety of these systems, making them an attractive investment for companies seeking to optimize their maintenance operations.

Another significant driver fueling the market is the increasing emphasis on workplace safety and regulatory compliance. Stringent regulatory standards in industries such as chemical processing and water & wastewater management mandate the use of reliable and safe maintenance procedures. Robotic pipe flange milling machines reduce the need for human intervention in hazardous environments, thereby lowering the risk of workplace accidents and ensuring compliance with occupational safety guidelines. Furthermore, the ability of these machines to operate in confined or hard-to-reach spaces makes them indispensable for maintenance tasks where manual intervention would be impractical or dangerous. This safety advantage, coupled with the potential for cost savings through reduced labor and downtime, is encouraging a broader adoption of robotic solutions in flange milling applications.

Technological advancements are also playing a pivotal role in shaping the growth trajectory of the robotic pipe flange milling market. Innovations in robotics, artificial intelligence, and machine learning are enabling the development of more sophisticated and user-friendly milling systems. These advancements are making it possible for operators to remotely control and monitor milling operations, further reducing the need for on-site personnel. Additionally, the growing trend toward Industry 4.0 and smart manufacturing is leading to the integration of robotic flange milling machines with digital platforms and enterprise resource planning (ERP) systems. This seamless connectivity enhances workflow automation, data collection, and predictive maintenance, providing end-users with actionable insights to optimize their operations and extend the lifespan of their assets.

Regionally, Asia Pacific is emerging as the dominant market for robotic pipe flange milling, accounting for the largest share in 2024, followed by North America and Europe. The rapid industrialization and infrastructure development in countries such as China, India, and Southeast Asian nations are driving significant investments in pipeline construction and maintenance. Additionally, the presence of a large number of oil & gas, power, and chemical processing facilities in the region is creating a strong demand for advanced milling solutions. North America and Europe are also witnessing steady growth, supported by the modernization of aging infrastructure and the increasing adoption of automation in industrial operations. Meanwhile, the Middle East & Africa and Latin America are expected to offer lucrative growth opportunities, driven by expanding energy and water management sectors.

Product Type Analysis

The product type segment of the robotic pipe flange milling market is bifurcated into portable robotic milling machines and stationary robotic milling mach

End Milling Machine Market Outlook

The global end milling machine market size was valued at approximately $3.2 billion in 2023 and is projected to reach around $5.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.3% during the forecast period. This growth is driven primarily by advancements in manufacturing technologies and increasing demand from various end-use industries.

One of the primary growth factors for the end milling machine market is the rising demand for precision engineering across various industries, including automotive, aerospace, and electronics. As industries strive for higher efficiency and tighter tolerances in their manufacturing processes, the need for advanced milling machines that can deliver precision and consistency increases. Innovations in Computer Numerical Control (CNC) technology have further enhanced the capabilities of milling machines, allowing for more complex and accurate machining processes. This technological progress is expected to continue driving market growth over the next decade.

Another significant growth driver is the expanding automotive industry, which heavily relies on milling machines for producing high-precision components. The automotive sector's ongoing evolution towards electric vehicles (EVs) presents new opportunities for milling machine manufacturers. EV production requires specialized components that necessitate advanced machining processes, thereby increasing the demand for high-precision and efficient milling machines. Additionally, the aerospace sector's growth, driven by increasing air travel and the production of new aircraft models, also fuels the demand for end milling machines, which are critical for manufacturing aerospace components.

The increasing emphasis on Industry 4.0 and smart manufacturing is another key factor contributing to the market's expansion. The integration of automation, data exchange, and intelligent manufacturing techniques are transforming traditional manufacturing processes. End milling machines equipped with IoT sensors and data analytics capabilities enable real-time monitoring and optimization of machining processes, leading to higher productivity and lower operational costs. As more industries adopt these advanced manufacturing practices, the demand for state-of-the-art milling machines is expected to rise significantly.

Mini Milling Machines have emerged as a popular choice among small-scale manufacturers and hobbyists due to their compact size and versatility. These machines offer a cost-effective solution for precision machining tasks, allowing users to perform a variety of milling operations in limited spaces. The growing trend of DIY projects and small-scale manufacturing has further fueled the demand for mini milling machines. Their ability to handle intricate and detailed work makes them ideal for producing custom parts and prototypes. As the market for compact and efficient machinery continues to expand, mini milling machines are expected to play a crucial role in catering to the needs of small businesses and individual craftsmen.

Regionally, the Asia Pacific region is anticipated to dominate the end milling machine market due to rapid industrialization and the presence of a robust manufacturing sector in countries such as China, Japan, and India. The region's strong economic growth and substantial investments in the manufacturing sector further bolster market expansion. Additionally, North America and Europe are expected to witness significant growth, driven by advancements in aerospace and automotive industries and the adoption of smart manufacturing technologies. Latin America and the Middle East & Africa are also projected to contribute to the market growth, albeit at a relatively slower pace.

Product Type Analysis

The end milling machine market is segmented by product type into vertical milling machines, horizontal milling machines, universal milling machines, and others. Vertical milling machines hold a significant share of the market due to their versatility and ability to handle various milling operations. These machines are widely used in industries such as automotive, aerospace, and electronics, where precision and flexibility are paramount. The demand for vertical milling machines is expected to continue rising as industries increasingly adopt automation and advanced manufacturing processes.

Horizontal milling machines are also gaining tractio

This dataset contains process data from around six hours of milling operations performed on a three-axis horizontal milling machine (DMC 60 H, Deckel Maho). The data was collected in a laboratory setting using industry relevant components, tools and machining strategies in order to reflect the diversity and variability of practical milling scenarios. Controller-side process signals were acquired from the Siemens SINUMERIK 840D CNC system via the SINUMERIK Edge app "Analyze MyWorkpiece/Capture", which enables high-frequency data export at 500 Hz. Additional force measurements were recorded using a force measurement platform (Kistler Type 9255C), and accelerations were captured using a sensor mounted on the main spindle (PCB Type 356A33). The force platform signals were amplified using a charge amplifier (Kistler Type 5015A1000 K) before being acquired at a sampling rate of 10 kHz useing a data acquisition card (Data Translation DT9836; referred to as DAC). The acceleration sensor was connected to the same DAC via a Kistler coupler (type 5122). Raw data from the force and acceleration sensors is provided in the form of MATLAB timetable files (.mat). The process signals from the Siemens SINUMERIK Edge are stored in JSON format (.json). Preprocessed Edge data is also available as structured CSV files: - hfdata.csv: high-frequency controller signals (e.g., position, current, torque) for the X, Y and Z axes as well as the main spindle - hfblockevent.csv: executed NC code extracted from the machine log - header.csv: recording metadata In addition, each experiment includes a consolidated MATLAB file (.mat), in which all available Edge and DAC signals are synchronized and stored as a single timetable object. To achieve this, the 500 Hz Edge signals were interpolated using the PCHIP (Piecewise Cubic Hermite Interpolating Polynomial) method to match the 10 kHz resolution of the sensor data. This unified file allows signals to be compared directly across all sources and simplifies further analysis. A MATLAB figure (Sync_Plot.fig) is also included to visualize and confirm the successful synchronization between the sensor and Edge signals.

A total of, 33 milling experiments are included, covering three component types and eight anomaly types. NC programs, 3D CAD models (.stp), and detailed documentation are provided to support full reproducibility and benchmarking applications.

Documents: - Dataset: All data files organized by component type and trial ID - Descriptive: - Design of Experiment (process parameters and configuration details for each experimental trial) - Cutting Tool information - Part description: NC programs executed during the experiments (G-code) and 3D CAD models (.stp), organised by component type Experimental Setup: - Machine: DMC 60 H (retrofitted 3-axis horizontal milling center) - Materials: S235JR (unalloyed structural steel), Al2007 T4 (aluminum alloy), POM-C (polyoxymethylene copolymer) - Cutting Tools: - HSS-Co8 end mill (TiAlN coating, Ø 20 mm, k10 tolerance) - Solid carbide end mill (TiSi coating, Ø 10 mm, f8 tolerance) - Solid carbide end mill (uncoated, Ø 5 mm, e8 tolerance) - Workpiece blank dimensions: 150x75x50mm

indicating wear and damage in the tool.

The friction surfacing process of two deposited layers on two deposition surface conditions, i.e., a smooth interface and a rough interface, was investigated. The control parameter of the deposition process was the rod feed rate with the use of a conventional milling machine. Analyzes of the surface characterization and microstructural characterization along the produced deposits were performed. The interface strength of the substrate/deposit1 (smooth) and deposit1/deposit2 (rough) was evaluated by bending tests and the micro-hardness profile along the transverse section of the substrate/deposit1 and deposit1/deposit2. The bending tests revealed the presence of smalls delaminations with no evidence of fracture at the rough deposit1/deposit2 interface (D1/D2) with less predominance in the deposition condition 3B. This suggests that the combination of the travel speed of 5.5 mm/s and an increase in the consumable rod feed rate of (≥5.5 mm/s) increases the adhesive strength of the two produced interfaces.

The high-end CNC machine tool market is experiencing robust growth, projected to reach a market size of $438.3 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 7.1% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing automation across diverse industries like automotive, aerospace, and machinery manufacturing necessitates the precision and efficiency offered by high-end CNC machines. The ongoing trend toward Industry 4.0 and smart manufacturing further accelerates demand, as these machines are readily integrated into sophisticated production networks for enhanced data collection and process optimization. Technological advancements, such as improved machining accuracy, faster processing speeds, and advanced material handling capabilities, also contribute significantly to market growth. While increased initial investment costs can act as a restraint for some businesses, the long-term return on investment (ROI) through enhanced productivity and reduced operational expenses makes high-end CNC machines increasingly attractive. The market is segmented by machine type (CNC lathe, CNC milling machine, CNC grinding machine, and others) and application (automotive, machinery manufacturing, aerospace & defense, and others), allowing manufacturers to tailor their offerings to specific industry requirements. This segmentation contributes to market diversification and ensures consistent growth across different sectors. The geographical distribution of the market showcases a strong presence across North America, Europe, and Asia Pacific. North America, benefiting from a robust aerospace and defense sector, and strong automotive manufacturing, is expected to hold a significant market share. Europe, with its advanced manufacturing base and presence of key players like DMG Mori Seiki and Siemens, also contributes substantially to overall market demand. Asia Pacific, led by countries like China, Japan, and India, witnesses rapid growth driven by expanding manufacturing capacity and increasing investments in advanced technologies. The market's competitive landscape is dominated by established players like FANUC, Siemens, DMG Mori Seiki, Yamazaki Mazak, and others, who continuously innovate to maintain their market leadership through product development and strategic partnerships. The ongoing trend of mergers and acquisitions, further solidifies the market position of leading firms and creates opportunities for new technological advancements.

The PHM Data Challenge is a competition open to all potential conference attendees. This year the challenge is focused on RUL estimation for a high-speed CNC milling machine cutters using dynamometer, accelerometer, and acoustic emission data.

Both Student and Professional teams are encouraged to enter! Winners of the Student and the Professional categories who attend the conference and submit an invited paper to ijPHM on their technique will be awarded a cash prize. Top scoring participants will be invited to present at a special session of the conference.

The PHM Data Challenge is a competition open to all potential conference attendees. This year the challenge is focused on RUL estimation for a high-speed CNC milling machine cutters using dynamometer, accelerometer, and acoustic emission data.

Both Student and Professional teams are encouraged to enter! Winners of the Student and the Professional categories who attend the conference and submit an invited paper to ijPHM on their technique will be awarded a cash prize. Top scoring participants will be invited to present at a special session of the conference.

Participants will be scored based on their ability to estimate the remaining useful life of a 6mm ball nose tungsten carbide cutter. Winners of the Student and the Professional categories who attend the conference and submit an invited paper to ijPHM on their technique will be awarded a cash prize. Top scoring participants will be invited to present at a special session of the conference.

Additional information can be found on the competition blog, http://www.phmsociety.org/forum/583

Teams Teams may be comprised of one or more researchers. One winner from each of two categories will be determined on the basis of score. The categories are:

Professional: open to anyone (including mixed teams)
Student: open to any team with all members enrolled as full time students during the spring or fall 2010 semesters.

Teams must declare what category they belong to when signing up. There is a cash prize of $1000 for the top entrant from each category, contingent upon:

attending the conference
giving an invited presentation on the winning technique
submitting a journal-quality paper to the International Journal of Prognostics and Health Management (ijPHM) which discloses the full algorithm used.

Additionally, top scoring teams will be invited to give presentations at a special session, and submit papers to ijPHM. Submission of the challenge special session papers is outside the regular paper submission process and follows its own schedule.

The organizers of the competition reserve the right to both modify these rules and disqualify any team at their discretion.

Registration Teams may register by contacting the Competition organizers with their name(s), a team alias under which the scores would be posted, affiliation(s) with address(es), contact phone number (for verification) and competition category (professional or student). Student teams should also send the name of the university and the semesters where they are enrolled full-time. You will be emailed your username and password after verification.

PLEASE NOTE: In the spirit of fair competition, we allow only one account per team. Please do not register multiple times under different user names, under fictitious names, or using anonymous accounts. Competition organizers reserve the right to delete multiple entries from the same person (or team) and/or to disqualify those who are trying to “game” the system or using fictitious identities.

Data There are six individual cutter records, c1…c6. Records c1, c4 and c6 are training data, and records c2, c3, and c5 are test data: cutter#1 cutter#2 cutter#3 cutter#4 cutter#5 cutter#6

The data files are ~800 MB each, and were compressed using the bZip2 algorithm. If your un-zipping software complains, make sure it is bZip2-compatible. 7-Zip is Windows open-source software that works well; Linux users can use the bunzip2 command; Mac users can use Stuffit.

Note that if you downloaded a copy of c3.zip with a wear file in it, this file is incorrect. Please discard it. The data acquisition files are OK.

Each training record contains one “wear” file that lists wear after each cut in 10^-3 mm, and a folder with approximately 300 individual data acquisition files (one for each cut). The data acquisition files are in .csv format, with seven columns, corresponding to: Column 1: Force (N) in X dimension Column 2: Force (N) in Y dimension Column 3: Force (N) in Z dimension Column 4: Vibration (g) in X dimension Column 5: Vibration (g) in Y dimension Column 6: Vibration (g) in Z dimension Column 7: AE-RMS (V)

Some background on the apparatus and experimental setup can be found here, and in the references in that paper. The spindle speed of the cutter was 10400 RPM; feed rate was 1555 mm/min; Y depth...

These data include X-ray diffraction patterns of bulk boron and borophene obtained during ball milling at different rotation speeds, time and mass loadings. The data were collected to investigate the crystal structure of the studied materials.