Renishaw (Ireland)

By substituting two-photon cross-correlation in a wide-bandgap photodiode for the coherent gating conventionally used in dual-comb ranging, two-photon dual-comb LiDAR exchanges data-intensive interferometric acquisition for a single time-stamp from which an absolute distance can be inferred. Here, we report the application of two-photon dual-comb LiDAR to obtain real-time ranging to three independent targets with only a single silicon-photodiode detector. We show precisions of 197-255 nm (2 seconds averaging time) for static targets, and real-time simultaneous ranging to three dynamic targets driven by independent sinusoidal, saw-tooth and square waveforms. Finally, we demonstrate multi-target ranging to three points on a rigid body to provide simultaneous pitch and yaw angular measurements with precisions of 27.1 arcsec (130 µrad) on a 25 mm baseline.

ABSTRACT Organized by SMTA Europe Improving operational efficiencies has reached the point where only marginal gains can be achieved and therefore full business system and process transformations are required in order for businesses to stay competitive. Increased autonomy, connected devices & data, and digitalisation must be leveraged by businesses to become more agile and efficient, and by utilising the data and autonomy effectively this can lead to increased productivity, increased flexibility of products and reduced product quality variability. Businesses are at different stages on their Industry 4.0 journeys and there are a number of solutions available on the market to support businesses. In partnership with its members, the Manufacturing Technology Centre (MTC) in the UK has been implementing a Smart Factory demonstration test bed for Electronics Assembly, where upgrading legacy equipment with smart capability has been the primary focus, using these off-the-shelf solutions where possible, and open source tools like the IPC-CFX standard as the machine to machine communication language, that can save huge amounts of time and investment. Topics included: Overview of the MTC Overview of Industry 4.0 IPC-CFX and Hermes Focus on Smart Factory for legacy equipment Future project opportunities Who should watch this? Production Engineering, Process Engineering, Business Improvement, Continuous Improvement, Automation Engineering, Digital Engineering/Transformation Engineers. On Demand Webinar

Large scale scene generation is a computationally intensive operation, and added complexities arise when dynamic content generation is required. We propose a system capable of generating virtual content from non-expert input. The proposed system uses a 3-dimensional variational autoencoder to interactively generate new virtual objects by interpolating between extant objects in a learned low-dimensional space, as well as by randomly sampling in that space. We present an interface that allows a user to intuitively explore the latent manifold, taking advantage of the network's ability to perform algebra in the latent space to help infer context and generalize to previously unseen inputs.

Coordinate measuring machines (CMMs) are complex measuring systems that are widely used in manufacturing industry for form, size, position, and orientation assessment. In essence, these systems collect a set of individual data points that in practice is often a relatively small sample of an object. Their software then processes these points in order to produce a geometric result or to establish a local coordinate system from datum features. The subject of CMM evaluation is a broad and multifaceted one. This paper is concerned with the uncertainty in the coordinates of each point within the measuring volume of the CMM. Therefore, a novel method for measurement uncertainty evaluation using limited-size data sets is conceived and developed. The proposed method is based on a Bayesian regularized artificial neural network (BRANN) model consisting of three inputs and one output. The inputs are: the nominal coordinates; the ambient temperature; and the temperature of the workpiece. The output is the measured (actual) coordinates. An algorithm is developed and implemented before training the BRANN in order to map each nominal coordinate associated with the other inputs to the target coordinate. For validation the model is trained using a relatively small sample size of ten data sets to predict the variability of a larger sample size of ninety data sets. The calculated uncertainty is improved by more than 80% using the predicted variability compared to the uncertainty from the limited sample data set.