Seung‐Jun Shin

A Smart Manufacturing (SM) system should be capable of handling high volume data, processing high velocity data and manipulating high variety data. Big data analytics can enable timely and accurate insights using machine learning and predictive analytics to make better decisions. The objective of this paper is to present big data analytics modeling in the metal cutting industry. This paper includes: 1) identification of manufacturing data to be analyzed, 2) design of a functional architecture fo

An ultrasmall single-electron transistor has been made by scaling the size of a fin field-effect transistor structure down to an ultimate limiting form, resulting in the reliable formation of a sub-5 nm Coulomb island. The charge stability data feature the first exhibition of three and a half clear Coulomb diamonds at 300 K, each showing a high peak-to-valley current ratio. Its charging energy is estimated to be more than one order magnitude larger than the thermal energy at room-temperature. Th

The ability to predict performance of manufacturing equipment during early stages of process planning is vital for improving efficiency of manufacturing processes. In the metal cutting industry, measurement of machining performance is usually carried out by collecting machine-monitoring data that record the machine tool’s actions (e.g. coordinates of axis location and power consumption). Understanding the impacts of process planning decisions is central to the enhancement of the machining perfor

The open platform communications unified architecture (OPC UA) has received attention as a standard for data interoperability in industries. In particular, OPC UA extends its applicability across various industrial sectors by publishing OPC UA companion specifications created in collaboration with other industrial consortiums. However, OPC UA is limited to ensure the interoperability of data analytics models because the relevant companion specifications have not been developed yet. OPC UA should

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufactu

This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summ

In the metal cutting industry, power consumption is an important metric in the analysis of energy efficiency since it relates to energy consumption of machine tools. Much of the research has developed predictive models that correlate process planning decisions with power consumption through theoretical and/or experimental modeling approaches. These models are created by using the theory of metal cutting mechanics and Design of Experiments. However, these models may lose their ability to predict