International Journal of Advancements in Technology
Open Access

Developing Approaches to Reduce Power Consumption by using Machine Data

Erfaneh Nikzad^{* *}Correspondence: Erfaneh Nikzad, Department of Nuclear and Human Factors Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea, Tel: +9199301175, Email:

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Descripition

The discount of CO₂ way of means of transferring from fossil to renewable electricity reasserts is presently excessive at the time table of many governments. Simultaneously those governments also are forcing the discount of electricity consumption. The number one cognizance of those agendas is on mobility, building, and business sectors. For the latter, electricity-green save flooring and machining techniques help the discount of electricity consumption. Previous studies has centered on electricity-green machining techniques in the course of machining techniques. However, an electricity-green start-up of those machines or their spindle axis start-up has been not noted till now. This paper performance the specification of the machine techniques evaluating the electricity performances, manufacturing time and cost performance of the CNC (Computer Numeric Control) system on the spindle axis. This is carried out by the analyst to excessive-frequency statistics of the system from machining operations this is retrieved through the threshold device. Concepts of statistics analytics and in particular EDA (Electronic Design Automation) had been used to interactively visualize the inter-dependencies and expand results. It is proven that optimized discount of spindle strength come in to the price ends in both: top strength smoothing from 20 kW to 10 kW and decreasing of usual electricity consumption. Moreover, the expenses and manufacturing time are marginally laid low with this optimized discount of spindle strength enter price. Thus, this paper highlights a unique technique from statistics acquisition to technique development closer to electricity-green and sustainable machining.

Energy consumption in the field of industrialization is increasing rapidly. In addition, the industrial sector is the largest contributor to greenhouse gases, accounting for 29.2% of electricity-related CO₂ emissions. Therefore, sustainable energy consumption models, which are attracting attention from an industrial and paper perspective, are needed to reduce these negative effects of energy consumption. Renewable energy is a method that is pervading various industries. We propose a methodology for using renewable energy sources. This paper is based on a simulation approach applied to the machining process of machines.

However, these power supply inconsistencies and unreliability are contrary to their use in industrial applications. It cannot supply enough power for large applications. In addition, changes in renewable resources due to climate change are also large. Fossil fuels currently supply 80% of the world's energy supply, and renewable energy has limited potential to overtake fossil fuels and is still in the analyst stage.

The world's total energy consumption is determined primarily by three sectors: industry, transportation and construction. The largest energy consumption in the industry is due to machining and its sub-operations. From this perspective, it is necessary to design an energy-saving model that not only reduces costs, but also brings sustainability to the energy consumed and stability to the power grid. Network stabilization is also required because the peak power generated during processing, especially at spindle startup, shakes the network when the machine used requires very high energy inputs. This transition state from standby to booting the machine causes a power spike. These power spikes are only a small part of the total power consumption, but they provide energy efficient processing through the smoothing of the power spikes and realize the beneficial energy consumption effect to other sectors. They have to deal with it. Therefore, the goal of this paper is to focus on this niche area of peak smoothing by developing approaches to lower these power peaks and reduce overall power consumption. This is done by analyzing the high frequency machine data acquired during the machining of the part. The concept of data analysis is included to achieve the required visualization and dependencies. Finally, the results produced are evaluated based on energy consumption, production time, and total cost of machined parts. Keep in mind that it is difficult to obtain spindle acceleration because of the high cost and labor intensive work involved. The collection of these experimental data and further EDA will be carried out in this article. These explain the corresponding examination and further response on energy consumption. Next, a research gap is derived, explaining the gap and the examination done in this paper. The most important parameters such as production time, energy and cost are explained. Experimentation and visualization is a critical section that describes the complete process of parts and shapes, machines, gauges, etc. and the careful process of data analysis performed.