The quality of laser cladding is strongly influenced by process parameters, which interact in complex and often nonlinear ways. existing literature primarily focuses on the influence parameters surface properties. However, limited research has explored relationship between properties, their optimization. To bridge this gap, study introduces a novel parameter modeling optimization approach using Catch Fish Optimization Algorithm (CFOA). Key including power, scanning speed, powder feeding...

The manufacture of inertial-grade and tactical-grade diamond micro electro mechanical systems (MEMS) hemispherical resonator gyroscopes is limited by the manufacturing accuracy monocrystalline silicon concave mold, on which gyroscope shell formed. To solve this problem, paper presents a new method for micro-ultrasonic machining microhemispherical molds using an ultraprecise ceramic entire-ball tool (UCET). study wear mechanism UCET influence surface integrity form mathematical model with...

This research proposes an optimized magnetic abrasive machining process that uses ultra-high-speed system to perform precision on a workpiece. The can several microns of material, either for surface roughness or workpiece precise micro-diameter. stainless steel workpieces have been machined using (UHSMAM) process. experiments were performed analyzing the accuracy diameter, response methodology. results obtained after analyzed determine effect different parameters such as speed, time,...

The ultra-high-speed magnetic abrasive machining (UHSMAM) process is a surface improvement technique, which has been widely used to minimize the accuracy and change precision morphology of difficult-to-machine materials. Surface integrity plays an important role in process, because it evaluate high stress loaded components on machined surface. It plastically deformed layers ultra-precision material. However, usual plastic strains are significantly difficult consider. In this paper, technique...

The paper proposes a new ultra-high-precision machining technique, based on magnetic abrasive finishing, to achieve both high dimensional accuracy and surface of microscale-diameter materials that are difficult or impossible machine conventionally. Microscale-diameter zirconia ceramic bars were used as the workpieces, machined by means proposed technique at different workpiece revolution speeds (1,000, 10,000, 20,000, 35,000 rpm). Machining depth increased with speed, was greatest for...

The research aims to focus on the smooth surface of material, due development high technologies, in which a is finished by removing material form microchips abrasive particles presence magnetic field finishing zone.The inner Al 6063 pipe was used as workpiece and through new improved mini lathe using four experimental methods.The (MAF) process some different methods such as, mixed radios, rotational speeds, pole arrangements shapes, best combination this study can be found analysis variance...

An ultra-high-speed micro machining process is a micro-machining technique, which used to minimize the surface roughness and change precision morphology of difficult-to-machined materials.Surface integrity very important process, evaluate highly stress loaded components.It plastically deformed layers in machined process.However, typical plastic strains are significantly difficult measure.In this paper, equipment critical magnetic abrasive tool were applied process.SUS 304 bars widely many...

Abstract Monocrystalline silicon is one of the most important semiconductor materials, widely used in chip manufacturing, solar panels. Slicing first step making chips and surface quality wafers directly affects later processing accounts for a large proportion manufacturing cost. Ultrasonic vibration assisted wire saw (UAWS) an effective sawing process cutting hard brittle materials such as monocrystalline Si, which can significantly improve wafers. In order to further study formation...

In semiconductor chemical mechanical polishing, the material removal rate (MRR) is key indicator of productivity. It thus crucial to predict MRR for wafer-to-wafer quality control during process. this article, we propose a data-driven model evaluate relationship between process variables and MRR. We consider feature ensemble which combines learned features handcrafted prediction. A novel deep learning topology proposed representation learning. Also, time usage neighbor are extracted overcome...