Paper Title
IDLE TIME DETECTION IN MANUFACTURING INDUSTRY-USING MACHINE LEARNING ALGORITHMSAbstract
Our day-to-day commodities truly depend on the industrial sector, which is expanding at a rapid rate along with the growing population. The production of goods needs to be accurate and rapid. Thus, for the present research, idle time detection in manufacturing industry using machine learning algorithms. When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. Smart manufacturing is reshaping the manufacturing industry by boosting the integration of information and communication technologies and manufacturing process. As a result, manufacturing companies generate large volumes of machine data which can be potentially used to make data-driven operational decisions using informative computerized algorithms. In the manufacturing domain, it is well-known that the productivity of a production line is constrained by throughput bottlenecks. The operational dynamics of the production system causes the bottlenecks to shift among the production resources between the production runs. Therefore, prediction of the throughput bottlenecks of future production runs allows the production and maintenance engineers to proactively plan for resources to effectively manage the bottlenecks and achieve higher throughput.
KEYWORDS : Machine learning algorithm, smart manufacturing, communication technologies, bottlenecks, operational decisions