Analysis of Critical Factors for Automatic Measurement of OEE

The increasing digitalization of industry provides means to automatically acquire and analyze manufacturing data. As a consequence, companies are investing in Manufacturing Execution Systems (MES) where the measurement of Overall Equipment Effectiveness (OEE) often is a central part and important reason for the investment. The purpose of this study is to identify critical factors and potential pitfalls when operating automatic measurement of OEE. It is accomplished by analyzing raw data used for OEE calculation acquired from a large data set; 23 different companies and 884 machines. The average OEE was calculated to 65%. Almost half of the recorded OEE losses could not be classified since the loss categories were either lacking or had poor descriptions. In addition, 90% of the stop time that was classified could be directly related to supporting activities performed by operators and not the automatic process itself. The findings and recommendations of this study can be incorporated to fully utilize the potential of automatic data acquisition systems and to derive accurate OEE measures that can be used to improve manufacturing performance

A model for linking shop floor improvements to manufacturing cost and profitability

Manufacturing units in the so called high-cost countries are struggling under fierce competition on the global market. In order to survive, the factory needs to generate profit to its owners. Profitability can be reached in many different ways apart from only lowering the employees' salaries. It can be improved through increased profit margins (sales in relation to costs) or with an increased capital turnover rate. Finding ways to free capacity and to improve flexibility in order to increase sales is often more interesting to the manufacturing companies than cutting the direct salary costs. A model for analysing profitability of a manufacturing unit is proposed. It is found on a production system analysis and combines in-depth production engineering analysis with economical accounting analysis of the factory. The manual work tasks are of special interest and the productivity of selected bottleneck work areas are analysed thoroughly. The model is intended for use by two industrial analysts during a one-week study. Simulation of different improvement scenarios is carried out and presented to the factory management at the end of the profitability study. A software implementation is required in order to generate the model, collect data and make simulation within the intended time. The implementation is made in spread sheet software using Visual Basic to program interfaces and automatic functions. The primary area of application is the electronics industry in Sweden where the model is used in a research project to strengthen the competitiveness of that industry

High Angular Resolution Stellar Imaging with Occultations from the Cassini Spacecraft II: Kronocyclic Tomography

We present an advance in the use of Cassini observations of stellar occultations by the rings of Saturn for stellar studies. Stewart et al. (2013) demonstrated the potential use of such observations for measuring stellar angular diameters. Here, we use these same observations, and tomographic imaging reconstruction techniques, to produce two dimensional images of complex stellar systems. We detail the determination of the basic observational reference frame. A technique for recovering model-independent brightness profiles for data from each occulting edge is discussed, along with the tomographic combination of these profiles to build an image of the source star. Finally we demonstrate the technique with recovered images of the {\alpha} Centauri binary system and the circumstellar environment of the evolved late-type giant star, Mira.Comment: 8 pages, 8 figures, Accepted by MNRA

Sequential Monte Carlo Instant Radiosity

The focus of this thesis is to accelerate the synthesis of physically accurate images using computers. Such images are generated by simulating how light flows in the scene using unbiased Monte Carlo algorithms. To date, the efficiency of these algorithms has been too low for real-time rendering of error-free images. This limits the applicability of physically accurate image synthesis in interactive contexts, such as pre-visualization or video games. We focus on the well-known Instant Radiosity algorithm by Keller [1997], that approximates the indirect light field using virtual point lights (VPLs). This approximation is unbiased and has the characteristic that the error is spread out over large areas in the image. This low-frequency noise manifests as an unwanted 'flickering' effect in image sequences if not kept temporally coherent. Currently, the limited VPL budget imposed by running the algorithm at interactive rates results in images which may noticeably differ from the ground-truth. We introduce two new algorithms that alleviate these issues. The first, clustered hierarchical importance sampling, reduces the overall error by increasing the VPL budget without incurring a significant performance cost. It uses an unbiased Monte Carlo estimator to estimate the sensor response caused by all VPLs. We reduce the variance of this estimator with an efficient hierarchical importance sampling method. The second, sequential Monte Carlo Instant Radiosity, generates the VPLs using heuristic sampling and employs non-parametric density estimation to resolve their probability densities. As a result the algorithm is able to reduce the number of VPLs that move between frames, while also placing them in regions where they bring light to the image. This increases the quality of the individual frames while keeping the noise temporally coherent — and less noticeable — between frames. When combined, the two algorithms form a rendering system that performs favourably against traditional path tracing methods, both in terms of performance and quality. Unlike prior VPL-based methods, our system does not suffer from the objectionable lack of temporal coherence in highly occluded scenes

Object-oriented Modeling of Manufacturing Resources Using Work Study Inputs

Resources are the core of manufacturing models. They provide information about the people and equipment that perform activities on the shop floor. Comprehensive representations of equipment are common but human resources are often defined to a very limited extent. This paper presents how work study data can be applied as input to detailed modeling of human manufacturing resources. The purpose is to provide a valid representation of manual work tasks on a shop floor level. If implemented in manufacturing models the valid representation will contribute to improve planning, control and execution of production. It also facilitates and encourages production improvement initiatives

MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures

Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray marching that are mismatched to the capabilities of widely deployed graphics hardware. This paper introduces a new NeRF representation based on textured polygons that can synthesize novel images efficiently with standard rendering pipelines. The NeRF is represented as a set of polygons with textures representing binary opacities and feature vectors. Traditional rendering of the polygons with a z-buffer yields an image with features at every pixel, which are interpreted by a small, view-dependent MLP running in a fragment shader to produce a final pixel color. This approach enables NeRFs to be rendered with the traditional polygon rasterization pipeline, which provides massive pixel-level parallelism, achieving interactive frame rates on a wide range of compute platforms, including mobile phones.Comment: CVPR 2023. Project page: https://mobile-nerf.github.io, code: https://github.com/google-research/jax3d/tree/main/jax3d/projects/mobilener

Vox-E: Text-guided Voxel Editing of 3D Objects

Large scale text-guided diffusion models have garnered significant attention due to their ability to synthesize diverse images that convey complex visual concepts. This generative power has more recently been leveraged to perform text-to-3D synthesis. In this work, we present a technique that harnesses the power of latent diffusion models for editing existing 3D objects. Our method takes oriented 2D images of a 3D object as input and learns a grid-based volumetric representation of it. To guide the volumetric representation to conform to a target text prompt, we follow unconditional text-to-3D methods and optimize a Score Distillation Sampling (SDS) loss. However, we observe that combining this diffusion-guided loss with an image-based regularization loss that encourages the representation not to deviate too strongly from the input object is challenging, as it requires achieving two conflicting goals while viewing only structure-and-appearance coupled 2D projections. Thus, we introduce a novel volumetric regularization loss that operates directly in 3D space, utilizing the explicit nature of our 3D representation to enforce correlation between the global structure of the original and edited object. Furthermore, we present a technique that optimizes cross-attention volumetric grids to refine the spatial extent of the edits. Extensive experiments and comparisons demonstrate the effectiveness of our approach in creating a myriad of edits which cannot be achieved by prior works.Comment: Project webpage: https://tau-vailab.github.io/Vox-E

Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields

Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density. However, these grid-based approaches lack an explicit understanding of scale and therefore often introduce aliasing, usually in the form of jaggies or missing scene content. Anti-aliasing has previously been addressed by mip-NeRF 360, which reasons about sub-volumes along a cone rather than points along a ray, but this approach is not natively compatible with current grid-based techniques. We show how ideas from rendering and signal processing can be used to construct a technique that combines mip-NeRF 360 and grid-based models such as Instant NGP to yield error rates that are 8% - 76% lower than either prior technique, and that trains 22x faster than mip-NeRF 360.Comment: Project page: https://jonbarron.info/zipnerf