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Machine Learning Applications for Industry 4.0
Published in Vijaya Kumar Manupati, Goran D. Putnik, Maria Leonilde Rocha Varela, Smart and Sustainable Manufacturing Systems for Industry 4.0, 2023
Vaibhav Shah, D.E.B. Costa, S.F. Moreira, J.F. Lima, Maria Leonilde Rocha Varela, Goran D. Putnik
Metrology systems must have their precision evaluated. Measurement errors must be identified and evaluated. These measurement errors are caused by the process itself. Factors like methods, equipment and operator skills can influence measurements, so it is very important to understand which factor(s) most influence measurement errors. Therefore, it is necessary to carry out an analysis of the measurement system (MSA) with the collection of measurement and analysis data through control charts. For the measurement of FPI, measurement samples were collected with the new system, and the new system was compared with the instruments already used, considering factors of process variation. Sampling trees were made to plan FPI measurements and mouthpieces, as shown in Figure 5.12.
Mounting Large, Vertical-Axis Mirrors
Published in Paul Yoder, Daniel Vukobratovich, Opto-Mechanical Systems Design, 2017
Optical metrology is the subdiscipline of optical engineering in which performance-related characteristics such as surface figure, radius of curvature, focal length, reflected wavefront quality, and modulation transfer function of optical components and systems are measured to a high degree of accuracy. These measurements are typically made at various in-process stages during manufacture and upon completion of the optics; the measured values are compared with previously established standards to determine acceptability.
Introduction to Optics
Published in Rajpal S. Sirohi, Introduction to OPTICAL METROLOGY, 2017
Metrology is the science and technology of making measurements and drawing significant conclusions from a set of data. Optical metrology uses light-based techniques for measurement. The majority of measurements involve length in one form or other and hence most of the techniques reported in this book will relate to length measurement. Besides, there are some physical parameters of direct relevance in optics and hence the techniques of measurement of these parameters will also be included in the text.
Using Technology to Enhance PD Performance: A Comparative Case Study 3-D Scanning Technology Deployment
Published in Engineering Management Journal, 2021
The use of metrology is a fundamental part of the product development process. Metrology, defined by Merriam Webster as the ‘science of measurement’, provides data used in the design and development of parts and components. Popular metrology tools used in manufacturing industries include the coordinate measuring machines (CMM) and more recently, three-dimensional scanning (3DS). Our literature search in this niche area included all relevant papers that included 3DS for metrology purposes but omitted some papers that used the technology in the archeological space as this did not need to have the precision of what is typically needed in manufacturing or assembly operations. The popularity of three-dimensional scanning (3DS) is evident by its widespread coverage in the product development trade literature (Boehler & Marbs, 2004; Galantuccia et al., 2016; Hobart, 2008; Jans, 2008; Li et al., 2014). The advantages of 3DS are frequently compared to the more traditionally ubiquitous coordinate measurement machine (CMM). However, 3DS takes quicker measurements, is far less expensive, and is therefore much more cost effective to operate (Ameen & Hammad, 2018). In addition to its portability, its numerous advantages make 3DS technology a potential replacement for CMM technology in the PD world that allows for more frequent and broader use. The advantages and limitations of this technology are compiled from the above literature and is summarized in Exhibit 1.
Letter From the Editor
Published in NCSLI Measure, 2018
The VIM defines metrology as measurement science and its application, noting that it “includes all theoretical and practical aspects of measurement, whatever the measurement uncertainty and field of application.” Metrology crosscuts nearly all industrial, consumer, and technical fields. Recognizing metrology’s breadth and depth, Measure seeks to encompass the entire metrology audience: scientists, engineers, technicians, laboratory managers, statisticians, and all the other practitioners and researchers with whom they interact. It seems akin to addressing a measurement's entire set of error sources and their correlations (interactions).
A rigorous physics-based enhanced parameter estimation (EPE) methodology for calibration of building energy simulations
Published in Science and Technology for the Built Environment, 2021
Kris Subbarao, Srijan K. Didwania, T. Agami Reddy, Marlin Addison
Calibration is a well-established term in metrology; it essentially means modifying the raw output of a measuring device with a previously-determined corrective correlation to obtain an improved or more realistic measurement JCGM (2008). The metrology-type calibration process is shown in Figure 1. The figure depicts the process that uses the previously determined calibration function say by comparing the instrument raw readings against those from a more accurate reference standard. During training, the known output is used to determine the calibration function. We can think of a simulator such as EnergyPlus as the measuring device, and, its simulated energy use and indoor temperature time series as raw outputs. In this case, there cannot be a simple one-to-one relationship between the raw outputs and calibrated outputs. A generalization is depicted in the Figure 2. EnergyPlus raw output is used as a starting point to empirically obtain improved predictions. For example, using weather and indoor temperature time series as well as energy use time series predicted from a simulation program such as EnergyPlus as inputs, we can train a neural net using measured energy use; this can then be used for improved predictions. This approach must be contrasted with the commonly adopted method of training a neural net with weather and indoor temperature time series as inputs wherein no simulation is involved. A good review of machine learning for building energy estimation is given by Seyedzadeh et al. (2018). The neural network parameters have no direct physical interpretation. We will not pursue this metrology-like calibration approach. What we propose and formulate in detail is an enhanced parameter estimation approach we call EPE (Figure 3). The term “calibration” is ubiquitous for any reconciliation method; we will use that term loosely.