Browse Topic: Machining processes
In modern defense manufacturing, achieving technological superiority hinges on both rapid decision-making and unparalleled precision engineering. Advanced machining systems, such as 5-axis CNC machines, play a pivotal role by enabling the production of intricate, free-form geometries with micron-level accuracy. However, these advances often necessitate deep domain expertise for optimal tool selection and machining parameter configuration. This paper introduces GraphLLM, a model-agnostic approach that integrates structured knowledge graphs with large language models (LLMs) to enhance the accuracy and reliability of technical responses. By automatically extracting domain-specific entities and relationships from documents, GraphLLM mitigates LLM hallucinations and improves performance, especially in technically challenging or out-of-distribution queries. Experimental evaluations across various LLaMA models demonstrate significant uplifts of 25%, highlighting the framework’s potential to
The Electroimpact Automatic Fan Cowl Riveter uses two novel drill processes to control exit burr height and achieve the required hole quality in CRES (Corrosion-Resistant Steel, also called stainless steel) material stacks. Both processes use piloted cutters on the OML (Outer Mold Line, referring to the exterior surface of an airframe) side, and two different tools are used in a backside spindle on the IML (Inner Mold Line, referring to the inside surface of an airframe) side of the component. The first process uses a shallow-angle shave tool in the IML spindle to directly control the exit burr height after it is produced by the OML spindle and is called the “burr shave” technique. The second process uses a countersink tool in the IML spindle and produces an “intermediate countersink” after the pilot hole is drilled by the OML spindle, but before the final hole diameter is drilled. These drill processes were able to achieve the required hole quality in a challenging CRES material stack
Machining metal has its challenges as many shops will attest, but machining glass is another matter – one that Dan Bukaty Jr., President of Precision Glass & Optics (PG&O) is well schooled in. Mr. Bukaty and his 35-person shop manufacture high-end precision glass optics for customers such as IMAX, Intuitive Surgical, Boeing and NASA, to name a few. The products PG&O make can range from the ordinary to the extraterrestrial, such as mirrors that it fabricated for the Hobby–Eberly Telescope to measure dark energy in outer space.
The initial powder used for the manufacturing of NdFeB permanent magnets is usually prepared through rapid cooling, either by melt spinning or strip casting. The powders produced by these two methods are suitable for different applications: while melt-spun powder is a good initial material for bonded and hot-deformed magnets, strip-cast powder is normally used for sintered magnets. To investigate the suitability of using strip-cast powder to manufacture hot-deformed magnets, NdFeB powder prepared by strip casting was hot pressed (without particle alignment) and compared with melt-spun powder prepared under the same conditions (700 °C, 45 MPa, 90 min). Although the processing parameters are the same (pressed in the same mold), the magnetic properties of the magnets made from the two powders are significantly different. Surprisingly, the magnet made from the strip-cast powder (after ball milling) shows comparable magnetic properties to those of isotropic magnets, with coercivity (HcJ) of
Los Angeles-based plastics contract manufacturer Kal Plastics deployed UR10e trimming cobot for a fraction of the cost and lead time of a CNC machine, cut trimming time nearly in half, and reduced late shipments to under one percent — all while improving employee safety and growth opportunities.
The experimental investigation analyzed the performance of three machining conditions: dry machining, cryogenic machining, and cryogenic machining with minimum quantity lubrication (MQL) on tool wear, cutting forces, material removal rate, and microhardness. The outcome of this study presents valuable knowledge regarding optimizing conditions of turning operations for Ti6Al4V and understanding the machinability under cryogenic-based cooling strategies. Based on the experimentation, cryogenic machining with MQL is the most beneficial approach, as it reduces cutting force and flank wear with a required material removal rate. This strategy significantly enhances the machining efficiency and quality of Ti6Al4V under variable feed rates (0.05 mm/rev, 0.1 mm/rev, 0.15 mm/rev, 0.2 mm/rev, 0.25 mm/rev) where cutting velocity (120 m/min) and depth of cut (1 mm) are constant. The effects of the main cutting force, feed force, thrust force, material removal mechanism, flank wear, and
With over 15,000 products, Boston Scientific is a market leader in pacemakers, defibrillators, monitoring equipment, spinal and brain stimulation, stents, catheters, and ablation devices. On one recent cardiac monitoring battery component, the company had an application running year-round on multiple mills, rectangular in shape, consisting of multiple milling operations per part, requiring an operator per mill at all times. Both Mill operations consist of multi-part fixtures as the process involved running Mill OP-1, light hand deburring and prepping the parts for Mill OP-2 fixture & process, following manual deburring step. The overall process was running around seven minutes per part.
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study focuses on Cupronickel and suggests creating predictive models to anticipate performance metrics in ECM through regression analysis. The experiments are formulated based on Taguchi's principles, and a multiple regression model is utilized to deduce the mathematical equations. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. The proposed prediction technique for Cupronickel is more adaptable, efficient, and accurate in comparison to current models, providing enhanced monitoring capabilities. The updated models have
The aim of this study is to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the Electrochemical Machining (ECM) process using Nimonic Alloy material, with a specific focus on several performance aspects. The optimization strategy utilizes the combination of the Taguchi method and ANFIS integration. Nimonic Alloy is widely employed in the aerospace, nuclear, marine, and car sectors, especially in situations that are susceptible to corrosion. The experimental trials are designed according to Taguchi's method and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This study investigates performance indicators, such as the rate at which material is removed, the roughness of the surface, and geometric characteristics, including overcut, shape, and tolerance for orientation. Based on the analysis, it has been determined that the feed rate is the main component that influences the intended performance criteria. In order to
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study primarily examines the use of Haste alloy in vehicle applications and suggests creating regression models to predict performance parameters in ECM. The experiments are formulated based on Taguchi's ideas, and mathematical equations are derived using multiple regression models. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. ANOVA is employed to evaluate the statistical significance of process parameters that impact performance indicators. The proposed regression models for Haste alloy are more versatile
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, regardless of their level of hardness. Due to the growing demand for superior products and the necessity for quick design changes, decision-making in the manufacturing industry has become increasingly intricate. The preliminary intention of this work is to concentrate on Cupronickel and suggest the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the purpose of predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the target of maximizing material removal rate, minimizing surface roughness, and simultaneously achieving precise geometric tolerances. The ANFIS model suggested for Cupronickel provides more flexibility, efficiency, and accuracy compared to conventional approaches, allowing for enhanced monitoring and control in ECM operations
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely predict critical performance metrics. Experimental experiments were conducted using a WEDM system to mill Invar 36 under diverse machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
The intention of this exploration is to evolve an optimization method for the Electrochemical Machining (ECM) process on Haste alloy material, taking into account various performance characteristics. The optimization relies on the amalgamation of the Taguchi method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Haste alloy is extensively utilized in the aerospace, nuclear, marine, and car sectors, specifically in situations that are prone to corrosion. The experimental trials are organized based on Taguchi's principles and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This examination examines performance indicators, including the pace at which material is removed and the roughness of the surface. It also includes geometric factors such as overcut, shape, and tolerance for orientation. The results suggest that the rate at which the feed is supplied is the most influential element affecting the necessary performance standards
The aspiration of this exploration is to evolve an optimization technique for the Electrochemical Drilling process on Haste alloy material, considering various performance factors. The Taguchi approach, along with Grey Relational Analysis (GRA), forms the basis for optimization. Haste alloy has a wider range of uses in industries such as aerospace, nuclear, and marine, especially in harsh environments. The experimental trials conducted in accordance with Taguchi's approach have utilized three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. When doing this examination, we analyze not only the rate at which material is removed and the roughness of the surface, but also other characteristics that indicate performance, such as overcut, shape, and orientation tolerance. The analytical findings indicate that the feed rate is the primary factor that directly impacts the required performance standards. Regression models are constructed to make predictions
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely forecast important performance metrics. Experimental trials were conducted using a WEDM system to mill Invar 36 under several machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
The objective of this research is to develop an optimization strategy for the Electrochemical Drilling process on Nimonic alloy material, taking into account various performance factors. The optimization strategy relies on the integration of the Taguchi method with Grey Relational Analysis (GRA). Nimonic is extensively utilized in aerospace, nuclear, and marine industries, specifically in situations that are prone to corrosion. The experimental trials are structured based on Taguchi's principle and encompass three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This inquiry examines performance indicators like the rate of material removal, surface roughness, as well as geometric parameters such as overcut, shape, and orientation tolerance. Based on the investigation, it is determined that the feed rate is the primary factor that directly affects the intended performance criteria. In order to enhance the accuracy of predictions, multiple regression
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