Active Learning of Piecewise Gaussian Process Surrogates

Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisition schemes in machine learning. In this paper, we develop a method for active learning of piecewise, Jump GP surrogates. Jump GPs are continuous within, but discontinuous across, regions of a design space, as required for applications spanning autonomous materials design, configuration of smart factory systems, and many others. Although our active learning heuristics are appropriated from strategies originally designed for ordinary GPs, we demonstrate that additionally accounting for model bias, as opposed to the usual model uncertainty, is essential in the Jump GP context. Toward that end, we develop an estimator for bias and variance of Jump GP models. Illustrations, and evidence of the advantage of our proposed methods, are provided on a suite of synthetic benchmarks, and real-simulation experiments of varying complexity.Comment: The main algorithm of this work is protected by a provisional patent pending with application number 63/386,82

Effects of Information Content in Work Instructions for Operator Performance

Operators remain as important resources in complex final assembly. To sustain a multi-variant production, it is necessary for operators to manage high demands from a cognitive workload perspective. In such situations, work instructions can support operators cognitively. However, work instructions are often insufficient or unused in final assembly. In this paper, results from testbed experiments are presented where assembly work was supported by different types of work instructions with differing information content. Results indicate that operator performance in terms of perceived cognitive workload and information quality are affected by the presented content of information in work instructions

ADOPTION OF MASS MEDIA TECHNOLOGY ON INDUSTRY 4.0 PERSPECTIVE

Industry 4.0 has influenced the process of human communication from face-to-face to virtual. The communication process aligns with the Cyber-Physical System (CPS) concept. The CPS concept shows what is physically represented in cyberspace and vice versa. This has resulted in content production and distribution in the mass media adopting technological developments summarized in the CyberPhysical and Smart Factory systems. The adoption of technology that the mass media should carry out is a challenge for the sustainability of the media industry to carry out the function of the press. Thus, this article offers forms of technology adoption in the mass media using the literature review method in previous studies. The literature review results in this article offer technology adoption in the mass media from the perspective of industry 4.0. The form of technology adoption is the process of computational journalism as a production action based on Smart Factory. Furthermore, news automation is a production act and a CPS-based news distribution. This production and distribution process should involve the mass media workforce, which requires an approach to technology adoption by humans in the future mass media

Augmented reality in complex manufacturing systems as an informational problem: A human-centered approach

The informational complexity that characterizes future manufacturing environments raises new problems in the Information Science and Information Management fields. New facets of the information overload problem are being revealed e.g., as textual and "smart data" from the manufacturing processes are continuously generated and pushed to the workers, beyond their cognitive capabilities. The challenge of making use of augmented reality in manufacturing pro- cesses, empowering the human-worker, has not yet been addressed by Information Science as an information organization and retrieval problem. Furthermore, manufacturing processes are more and more knowledge-intensive, so knowledge codification, transfer, and use are another challenge not addressed so far from the information management point of view. Therefore, the objective of this doctoral research project is to study the combination of augmented reality technology (as a way to convey real/virtual visual information), centered in the human-worker (as the crucial key user) as an information organization/retrieval problem, from the theoretical perspectives of Information Field. In this poster, we present the research design and the preliminary results of the literature review

Building a Simple Smart Factory

This thesis describes (a) the search and findings of smart factories and their enabling technologies (b) the methodology to build or retrofit a smart factory and (c) the building and operation of a simple smart factory using the methodology. A factory is an industrial site with large buildings and collection of machines, which are operated by persons to manufacture goods and services. These factories are made smart by incorporating sensing, processing, and autonomous responding capabilities. Developments in four main areas (a) sensor capabilities (b) communication capabilities (c) storing and processing huge amount of data and (d) better utilization of technology in management and further development have contributed significantly for this incorporation of smartness to factories. There is a flurry of literature in each of the above four topics and their combinations. The findings from the literature can be summarized in the following way. Sensors detect or measure a physical property and records, indicates, or otherwise responds to it. In real-time, they can make a very large amount of observations. Internet is a global computer network providing a variety of information and communication facilities and the internet of things, IoT, is the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. Big data handling and the provision of data services are achieved through cloud computing. Due to the availability of computing power, big data can be handled and analyzed under different classifications using several different analytics. The results from these analytics can be used to trigger autonomous responsive actions that make the factory smart. Having thus comprehended the literature, a seven stepped methodology for building or retrofitting a smart factory was established. The seven steps are (a) situation analysis where the condition of the current technology is studied (b) breakdown prevention analysis (c) sensor selection (d) data transmission and storage selection (e) data processing and analytics (f) autonomous action network and (g) integration with the plant units. Experience in a cement factory highlighted the wear in a journal bearing causes plant stoppages and thus warrant a smart system to monitor and make decisions. The experience was used to develop a laboratory-scale smart factory monitoring the wear of a half-journal bearing. To mimic a plant unit a load-carrying shaft supported by two half-journal bearings were chosen and to mimic a factory with two plant units, two such shafts were chosen. Thus, there were four half-journal bearings to monitor. USB Logitech C920 webcam that operates in full-HD 1080 pixels was used to take pictures at specified intervals. These pictures are then analyzed to study the wear at these intervals. After the preliminary analysis wear versus time data for all four bearings are available. Now the ‘making smart activity’ begins. Autonomous activities are based on various analyses. The wear time data are analyzed under different classifications. Remaining life, wear coefficient specific to the bearings, weekly variation in wear and condition of adjacent bearings are some of the characteristics that can be obtained from the analytics. These can then be used to send a message to the maintenance and supplies division alerting them on the need for a replacement shortly. They can also be alerted about other bearings reaching their maturity to plan a major overhaul if needed

Use of nearest neighbors (k–nn) algorithm in tool condition identification in the case of drilling in melamine faced particleboard

The purpose of this study was to develop an automatic indirect (non-invasive) system to identify the condition of drill bits on the basis of the measurement of feed force, cutting torque, jig vibrations, acoustic emission and noise which were all generated during machining. The k-nearest neighbors algorithm classifier (k-NN) was used. All data analyses were carried out in MATLAB (MathWorks – USA) environment. It was assumed that the most simple (but sufficiently effective in practice) tool condition identification system should be able to recognize (in an automatic way) 3 different states of the tool, which were conventionally defined as “Green” (tool can still be used), “Red” (tool change is necessary) and “Yellow” (intermediate, warning state). The overall accuracy of classification was 76 % what can be considered a satisfactory result at this stage of studies

Key Factors of Customer-Supplier of Smart Manufacturing Implementation

The paper presents a conceptual framework of the key factors of customers and suppliers in the implementation of smart manufacturing in the company. The field of the study related to competitiveness through cutting-edge technologies related to the Industrial Revolution 4.0. Hence, it shows accurate and effective decision-making in real-time from the smart manufacturing implementation. This comes together with the converging of the actual manufacturing technologies as an aid for the operations and productions. On the conceptual development, the journal articles, conference proceedings, books, dissertations, online news and newspaper, magazines related to smart manufacturing have been analyzed. A critical review creates an appropriate conceptual framework with the relationships of the key concepts of the link between customers and suppliers for the smart manufacturing implementation, as a contribution to the body of knowledge

Case Study of Intralogistics in the Framework of Logistics 4.0

Industry 4.0 has led to changes that have reduced the labor force and created production environments where machines that bring together information technology and industry communicate with each other. Logistics 4.0, which emerged with Industry 4.0, paved the way for improvement in logistics processes. Using information technologies in logistics reduces the labor force costs of enterprises by leading all stages of activities to digitalization. It can be possible to increase customer satisfaction and product quality by reducing human failures with digitalization. This study was performed by planning intralogistics using Logistics 4.0 technological tools, and also the problem of a manufacturing company was elaborated as a case study. This study was carried out by quantitative data analysis in the case study and a large-scale production company in the automotive industry in Turkey providing the intralogistics of the materials from the supplier in the entrance warehouse with RFID (Radio Frequency Identification) technologies. This paper presents the research, development, and application of logistics 4.0 in the intralogistics process from the entrance warehouse to the production lines. The aim of the case study was provided to information about the technologies available within the scope of Logistics 4.0 and contribute to the literature and industry with solution suggestions depending on the result of the application study within the logistics operations. As a result, depending on the case study, it was determined that Logistics 4.0 improved intralogistics operations costs by 13.37%