Machine tools degrade during operations, yet knowledge of degradation is elusive; accurately detecting degradation of linear axes is typically a manual and time-consuming process. Manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. A method was developed to use data from an inertial measurement unit (IMU) for identification of changes in the translational and angular errors due to axis degradation. A linear axis testbed, established for the purpose of verification and validation, revealed that the IMU-based method was capable of measuring geometric errors with acceptable test uncertainty ratios.

Simple vibration-based metrics are, in many cases, insufficient to diagnose machine tool spindle condition. These metrics couple defect-based motion with spindle dynamics; diagnostics should be defect-driven. A new method and spindle condition estimation device (SCED) were developed to acquire data and to separate system dynamics from defect geometry. Based on this method, a spindle condition metric relying only on defect geometry is proposed. Application of the SCED on various milling and turning spindles shows that the new approach is robust for diagnosing the machine tool spindle condition.

The digital thread links disparate systems across the product lifecycle to support data curation and information cultivation and enable data-driven applications, e.g., digital twin. Realizing the digital thread requires the integration of semantically-rich, open standards to facilitate the dynamic creation of context based on multiple viewpoints. This research develops such an approach to link as-planned (ISO 6983) to as-fabricated (MTConnect) product data using dynamic time warping. Applying this approach to a production part enabled the designer to make a more optimal decision from the perspective of the product lifecycle that would have otherwise been challenging to identify.

All measurements performed in science and industry are based on the International System of Units, the SI. It has been proposed to revise the SI following an approach which was implemented for the redefinition of the unit of length, the metre, namely to define the SI units by fixing the numerical values of so-called defining constants, including , , , and . We will discuss the reasoning behind the revision, which will likely be put into force in 2018. Precision engineering was crucial to achieve the required small measurement uncertainties and agreement of measurement results for the defining constants.

The accuracy of multi-axis machine tools is affected to a large extent by the behavior of the system's axes and their error sources. In this paper, a novel methodology using circular inertial measurements quantifies changes in squareness between two axes of linear motion. Conclusions are reached through direct utilization of measured accelerations without the need for double integration of sensor signals. Results revealed that the new methodology is able to identify squareness values verified with traditional measurement methods. The work supports the integration of sensors into machine tools in order to reach higher levels of measurement automation.

Metal powder particle size distribution (PSD) is a critical factor affecting powder layer density and uniformity in additive manufacturing processes. Among various existing measurement methods, dynamic image analysis (DIA) instruments are very appealing for measuring PSD. However, the 'black box' nature and complex measurement process inherent to DIA make quantification of uncertainty challenging. A method to establish DIA-based measurement uncertainty based on calibrated powder samples via a scanning electron microscope is described. Uncertainty analysis was performed taking into account uncertainties associated with the calibration of the sample as well as non-similarities of the calibrated sample and the measured sample.

Laser powder bed fusion systems use a high-power laser, steered by two galvanometer (galvo) mirrors to scan a pattern on metal powder layers. Part geometric tolerances depend on the positioning accuracy of the laser/galvo system. This paper describes an in-situ calibration technique utilizing a camera coaxially aligned with the laser imaging a dimensional reference artefact. The laser positions are determined from the images captured by the camera while scanning the artefact. The measurement uncertainty is estimated using simulations. The in-situ calibration results are compared with the results obtained from the typical 'mark and measure' galvo calibration method.

Continued advancement of sensors has led to an ever-increasing amount of data of various physical nature to be acquired from production lines. As rich information relevant to the machines and processes are embedded within these "big data", how to effectively and efficiently discover patterns in the big data to enhance productivity and economy has become both a challenge and an opportunity. This paper discusses essential elements of and promising solutions enabled by data science that are critical to processing data of high volume, velocity, variety, and low veracity, towards the creation of added-value in smart factories of the future.

In the modelling of machining operations, constitutive models must consider the material behavior subject to high plastic strains, high strain rates, high temperatures and high heating rates. A new material model for AISI 1045, which captures time-dependent plastic response associated with interrupted austenite transformation under short (sub-second) heating times, is deployed to simulate orthogonal cutting experiments. High speed video and digital image correlation measurements are used to capture chip behavior. The new model, which also includes complex strain hardening and dynamic strain aging effects, show better agreement with experiments at high cutting speeds compared with a basic Johnson-Cook material model from the literature.