Browse Topic: Manufacturing processes
The automotive industry is facing unprecedented pressure to reduce costs without compromising on quality and performance, particularly in the design and manufacturing. This paper provides a technical review of the multifaceted challenges involved in achieving cost efficiency while maintaining financial viability, functional integrity, and market competitiveness. Financial viability stands as a primary obstacle in cost reduction projects. The demand for innovative products needs to be balanced with the need for affordable materials while maintaining structural integrity. Suppliers’ cost structures, raw material fluctuations, and production volumes must be considered on the way to obtain optimal costs. Functional aspects lead to another layer of complexity, once changes in design or materials should not compromise safety, durability, or performance. Rigorous testing and simulation tools are indispensable to validate changes in the manufacturing process. Marketing considerations are also
The development of advanced high-strength steels has become essential in the production of lightweight, safe, and more economical vehicles within the context of the automotive industry. Among the advanced high-strength steels, complex phase steels stand out, characterized by their high formability and high energy absorption and deformation capacity. Laser welding is a technique that applies laser using high energy density as a heat source. It has the advantages that the high welding speed and low heat input compared to other welding methods cause a decrease in deformation, and the narrow width of the weld bead and heat-affected zone allows for the welding of complex parts that would be difficult for other welding methods. Based on a study of a complex phase steel, an analysis was made of the microstructures observed by optical microscopy, the grain boundaries and certain phases contained in this microstructure, as well as the microstructures of each area in the laser welding region
The goal of this work is to increase the accuracy and efficiency of hose cutting operations in small scale industries is by designing and building an automatic hose-cutting equipment. The device uses a computer-controlled system to autonomously cut pipes of various sizes and lengths. By means of a stepper motor-driven, rapidly spinning blade, the cutting process is accomplished. Additionally, the machine has sensors that measure the hose's length and modify the cutting position as necessary. Premium components and materials are used in the machine's construction; these are chosen for their performance and longevity. The device is able to boost cut precision and raise industry production all around from 100% to 190% efficient system thereby decreasing labor and time needed for hose cutting operations
This specification covers a corrosion- and heat-resistant nickel alloy in the form of bars, forgings, and flash-welded rings up to 4.00 inches (101.6 mm), exclusive, in least distance between parallel sides (thickness) or diameter, and stock of any size for forging or flash-welded rings
Wire Electrical Discharge Machining (WEDM) is an essential manufacturing process used to shape complex geometries in conductive materials such as cupronickel, which is valued for its corrosion resistance and electrical conductivity. The aim of this explorative study is to enhance the efficiency and precision of machining by creating a specialized predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for cupronickel material. The study examines the intricate correlation between process variables of the WEDM (Wire Electrical Discharge Machining) technique, such as pulse-on time (Ton), pulse-off time (Toff), and discharge current, and crucial machining responses, including surface roughness, material removal rate. Data is collected through systematic experimentation in order to train and validate the ANFIS predictive model. The ANFIS model utilizes the collective learning capabilities of neural networks and fuzzy logic systems to precisely forecast machining responses by
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has become a revolutionary technology for creating intricate shapes using different materials. Polylactic Acid (PLA) is a biodegradable thermoplastic that is commonly used in additive manufacturing (AM) because of its environmentally friendly properties, affordability, and ease of use. The objective of this study is to optimize the FDM parameters for PLA material and create predictive models using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast printing performance. An investigation was carried out through experimental trials to examine the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes such as dimensional accuracy, surface finish, and mechanical properties. The utilization of design of experiments (DOE) methodology enabled a methodical exploration of parameters. A predictive model using ANFIS was created to
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has become a highly promising method for creating intricate shapes using different materials. Polyethylene Terephthalate Glycol (PETG) is a highly utilized thermoplastic that is recognized for its exceptional strength, resistance to chemicals, and effortless processing. This study aims to optimize the process parameters of the FDM technique for PETG material using Taguchi Grey Relational Analysis (GRA). An empirical study was carried out to examine the impact of various FDM process parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on important outcome variables like dimensional accuracy, surface quality, and mechanical properties. The Taguchi method was used to systematically design a series of experiments, while GRA was used to optimize the process parameters and performance characteristics. The results unveiled the most effective parameter combinations for attaining
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