INDUSTRIAL ENGINEERING

The goal of this project is to meet the urgent need for dependable extended backup power solutions designed with refrigeration applications in mind. One of the biggest challenges is keeping refrigeration systems running continuously, especially in areas where there are frequent power outages or unstable electrical supplies. A complex 1.5KVA inverter-based smart system that incorporates cutting-edge Internet of Things (IoT) technology is created and put into place to address this problem. The principal aim is to provide a stable and effective backup power supply for refrigeration systems, protecting perishable commodities and enhancing the general standard of living, particularly in neglected rural regions and essential establishments such as hospital blood banks.
The approach used in the creation of this ground-breaking solution include the painstaking
design and integration of essential parts with an emphasis on maximizing overall effectiveness and performance. To ascertain the best specifications for the inverter system, a thorough modeling and evaluation process comprised the first step. Ensuring compliance with the enhanced refrigeration needs while preserving maximum energy efficiency was essential. Furthermore, a charge controller was included to enable smooth integration with solar panels and maximize the system's ability to use renewable energy sources. The system design and component selection might be improved iteratively to match the project's unique goals.
A strong and adaptable smart inverter system with extended backup power for refrigeration applications is the best result of our effort. By using Internet of Things technology, customers can remotely keep an eye on and control vital metrics like battery life, temperature, and system performance in real time. This project intends to improve the resilience of refrigeration systems and aid in the preservation of necessary perishable goods by tackling the inherent difficulties of power outages and unstable electricity supplies. This will have a noticeable effect on community well-being and sustainable development

The integrity of welded structures is affected by weld defects, induced stress as well as its
resistance to varying impacts during and after fabrication. This study explores the application of
the Adaptive Neuro-Fuzzy Inference System (ANFIS) in optimizing and predicting the impact
toughness of Tungsten Inert Gas (TIG) welded mild steel joints aimed at enhancing weldment
quality and overall structural integrity by determining the influence of key welding process
parameters on the impact toughness of the resultant weldment. The research seeks to optimize
predict these relevant factors thereby addressing challenges such as induced stress and failures
resulting from impacts on weldment.
Central Composite Design (CCD) was employed for experimental design having current, voltage
and gas flow rate as weld process generating twenty (20) experimental runs. Mild steel plates were
cut and welded using a TIG welding equipment to produce weld samples using the varying process
parameters. A digital impact testing machine was used to measure the impact toughness of the
weldments. The experimental data was then analyzed using ANFIS, which integrates neural
networks and fuzzy logic for predicting and optimizing the investigated response.
The ANFIS model effectively trained and tested the experimental data after which an optimal
result having having current of 175 amps, voltage of 23.5 volts, and a gas flow rate of 15.5
liters/min would yield a maximal impact toughness values of 95.7 J. Post experimental results
shows high correlation values with the optimal result thereby serving as validation. These findings
underline the potential of ANFIS as a robust tool for advancing production engineering processes.
This result improves the reliability of welded structures and supports the advancement of
production engineering practices