Numerous developments have been witnessing in manufacturing industries the ability of the machines to change the tools automatically during their wear or damage. In general, tool failure contributes about 7% to the down time of machine centers. Therefore, online monitoring of tool wear is an important phenomenon in producing quality products at reasonable cost. This also increases the production rate in the industries. Tool condition monitoring using the acoustic emission technique (AET) are real methods identified by researchers for online quality assessment of machine tools. The genetic algorithm (GA) is used to optimize the tool wear rate parameters. The practical significance of applying GA to tool wear rate has been validated by means of computing the deviation between predicted and experimentally obtained process parameters. Based on this research work, an experimental setup has been developed for online monitoring of a single point cutting tool using AETs. The experimental tool wear rate results are compared with online measurements using mean acoustic emission parameters (average value, root mean square value and area).

The world is rapidly changing, the resources are getting depleted, and the demand for newer technologies and products is increasing. To keep up with these new advances, highly efficient catalytic routes need to be taken to be sustainable and ensure a drawn-out existence of resources for future generations. Catalysis turns out to be a significantly important field of application when it comes to the era of nanoscience, where all devices and technologies are becoming smaller and smaller in size with improved properties. When deeming the usability of a catalyst, it is of paramount importance to have a good understanding of their properties and their synergistic effect on the other reagents in the reaction. Over the last decade, the field of nanocatalysis has grown rapidly, both in homogeneous and heterogeneous catalysis. Given that nanoparticles have a high surface-to-volume ratio when compared to bulk materials, they are appealing as catalysts. Catalysts accelerate and boost thousands of different chemical reactions on a daily basis, forming the foundation of the multibillion-dollar chemical industry worldwide, a pathway leading to green chemistry, and a novel, yet crucial, environmental protection technology. As a result, in this review, the use of nanocatalysts and the application of their special features in the renewable energy, hydrocarbon processing, and fine chemical synthesis sector was explored. A detailed explanation of the working mechanism of these nanocatalysts, starting from how they are synthesized to the effect of modification of their surface, has been put together. We have tried to collect all the current progresses in these three sectors to the best of our abilities. Furthermore, it is anticipated that this paper would be useful for future researchers and academicians wishing to contribute toward this subject of interest.

This paper examines the impact of welding parameters on tensile shear fracture load, nugget geometry and microstructure of resistance spot welds (RSW) of austenitic stainless steel AISI 316 L and duplex stainless steel 2205 under lap shear loading condition. The macroscopic examination resulted that many of the nugget lengths were nearer to and higher than the AWS recommended value 4√t and failed at higher tensile shear load. Nugget height for DSS 2205 side was higher in comparison with AISI 316 L due to higher thermal conductivity of duplex stainless steel. Three welding parameters mainly welding current of 9 kA, heating cycle of 9 and electrode tip diameter of 6 mm were discovered as most effectual parameters on the tensile shear load and microstructure of weldments. Heterogeneous hardness was observed in the fusion zone due to the transition of equiaxed to columnar grains takes place in the both sides of nugget edge. DSS HAZ nearby BM observed higher hardness and ASS HAZ nearby BM reported lower hardness. WMZ Microstructure confirmed that thickness of austenite layers increased with heat input. Also, an unmixed zone in the microstructure identified as HAZ which contains delta ferrite. Scanning Electron Microscope (SEM) images in the nugget zone for different welding parameters confirmed that Intra-Granular Austenite (IGA) highly developed at higher welding current. SEM fractrograph for the tensile sheared specimens at higher and lower heat input confirmed the ductile type fracture even failed at Inter-Facial (IF) mode. Nugget area and nugget hardness were positively correlated with Tensile Shear Fracture Load (TSFL).

Nanotechnology can improve the mechanical barrier and the antimicrobial (which will not allow the invasion of microorganisms in the food, increasing the food barrier properties, hence can be a very promising material for food packaging. Nanomaterials will keep the food fresh in the food packaging design. Silver nanoparticles and nanoclay represent most of the nanoempowered food packaging available on the market others like zinc oxide and titanium share little of the current market. Zinc oxide enhances nutritional values in food products by adding nutrients. It helps improve the flavour, storage properties, appearance, and texture of the food. Titanium dioxide is used for food safety purposes since it prevents food from spoiling and increases the food’s shelf life. In current food packaging, these nanomaterials are used to grant antimicrobial capacity and further develop hindrance properties, broadening packaged food’s shelf life and newness. Nanofood packaging has many benefits for general wellbeing. The related harmfulness of migration, particularly in acidic conditions, is extensive. The use of nanomaterials because of their physical and chemical properties makes them broadly accessible in numerous areas. This review summarizes the antimicrobial packaging application, nanomaterials synthesis, and nanomaterial properties in food packaging.

One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.