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Artificial Neural Networks Based Real-time Modelling While Milling Aluminium 6061 Alloy
Published in Amar Patnaik, Vikas Kukshal, Pankaj Agarwal, Ankush Sharma, Mahavir Choudhary, Soft Computing in Materials Development and its Sustainability in the Manufacturing Sector, 2023
Shaswat Garg, Satwik Dudeja, Navriti Gupta
Milling is a machining process that progressively removes material from a workpiece, by the use of rotating multi-point cutting tools, to produce custom-designed components. For geometrically complex part designs, precise prediction of surface roughness and material removal rates ensure efficient time utilization and good product quality [1]. Aluminium 6061 alloy is one of the most commonly used aluminium alloys which finds purpose in several heavy industry applications like aircraft components, automobile parts, weapon casings, and high vacuum chambers. Ensuring low allowance values with utmost precision is desirable in these respective manufacturing industries. During milling, operators use their experience to find the optimal set of parameters to have a satisfactory product finish. Adjusting the milling parameters to ensure the optimum finish just on the basis of experience can compromise the productivity of the process. The ANN is a significant approach for the prediction of such milling parameters as it has the ability to decode complex relationships, similar to a neural system [2].
Milling Operations and Machines
Published in Zainul Huda, Machining Processes and Machines, 2020
Milling is a machining operation that involves the removal of material from a work-part by use of a rotating cylindrical tool with multiple cutting edges – a milling cutter. In milling, the work has a feed motion, whereas the cutter has speed motion (Bray, 2004). Milling machines are primarily used for machining both metallic and non-metallic solids (e.g., metal, wood, plastics, etc.). These machines find widespread applications in diversified engineering industries. For example, the aerospace industry relies on machinery with a high level of precision and accuracy to manufacture aircraft parts that fit the exact specifications of aeronautical design. Biomedical manufacturing companies produce life-saving devices that are used in hospitals and clinics across the world. The manufacture of these life-saving devices and many other engineering components involve a great deal of milling operations. In particular, gears and many machine elements can be manufactured by milling operations (see Chapter 10).
Machining of Metals
Published in Sherif D. El Wakil, Processes and Design for Manufacturing, 2019
Figure 11.41 indicates methods of estimating the different machining parameters during milling operations. These parameters include the cutting speed, the feed, and the metal-removal rate. The cutting speed is the peripheral velocity at any point on the circumference of the cutter. The allowable value for the cutting speed in milling is dependent upon many factors, including the cutter material, material of the workpiece, diameter and life of the cutter, feed, depth of cut, width of cut, number of teeth on the cutter, and the type of coolant used. The feed in milling operations is the rate of movement of the cutter axis relative to the workpiece. It is expressed in inches (or mm) per revolution or inches (or mm) per minute. It can also be expressed in inches (or mm) per tooth, especially for plain and face milling cutters.
Surface integrity investigation and VIKOR optimisation during the milling of aluminium–lithium alloy using uncoated and PVD-coated carbide tools
Published in Canadian Metallurgical Quarterly, 2023
Vikas Marakini, Srinivasa P. Pai, Udaya K. Bhat, DineshSingh Thakur, Bhaskara Achar
Machining processes, such as milling, drilling and turning, are widely considered in the manufacturing industries to enhance the surface characteristics of a material. Milling is a popular choice owing to its several advantages such as high speed ability and flexibility compared with other machining processes. A material surface integrity (SI) investigation usually requires focus on four major characteristics, namely, surface roughness, surface hardness, residual stresses and microstructure. Material surface roughness affects the material surface integrity in terms of corrosion resistance, paintability and appearance [4]. The lower the surface roughness, the better the material’s corrosion resistance [5]. Surface hardness impacts the material surface integrity from its ability to improve wear and corrosion resistance. The harder the surface, the better the material’s wear and corrosion resistance [6]. Residual stress in compressive nature is known to be beneficial to material surface integrity in terms of improving the material’s fatigue life [7]. Finally, the microstructure is most crucial in deciding the surface integrity of the material as defects from machining may harm the component’s life. Thus, it is important to focus on the integrity of the alloy surface and there are very limited studies regarding the surface integrity of the aluminium–lithium alloy.
Modelling and online training method for digital twin workshop
Published in International Journal of Production Research, 2023
Litong Zhang, Yu Guo, Weiwei Qian, Weili Wang, Daoyuan Liu, Sai Liu
ELDTA is the general name of basic DTA units, including EDTA, ODTA and MDTA. EDTA is the representation of all kinds of equipment in the DT workshop. EDTA can be divided into machine tools, robots, conveying equipment, process equipment, IoT equipment, etc. Each of them contains subclasses, for example, machine tools including lathe, milling machine, drilling machine, grinding machine, gear machining machine, CNC machining centre and special machine. An EDTA composed of its DTI can be expressed as EDTAj = {e1, e2, … , ei, … ,en}(1 ≤ i ≤ n). Material is the general name of all materials transferred in the production line of DT workshop, including fuel, spare parts, semi-finished products, outsourcing parts, leftover materials and wastes inevitably produced in the production process. The mapping of the above DTI in a MDTA is expressed as MDTAj = {m1, m2, … , mi, … ,mn}(1 ≤ i ≤ n). Operators are an important part of the production line and can be classified according to their duties, posts and other standards. Based on the production characteristics of manufacturing, this paper collects the important characteristic attributes of operators and defines its DTA as a moveable unit that can perform certain operations, that is, an ODTA can be expressed as ODTAj = {o1,o2, … ,oi, … ,on}(1 ≤ i ≤ n).
Comparison and evaluation of alumino-silicate samples as a dual source of alumina and potash values
Published in Canadian Metallurgical Quarterly, 2023
The comparative analysis of the alkali thermal, milling treatment, and preliminary estimate for the consumption of reagents, acid, and energy for the optimal processing conditions are shown in Table 3. The alkali route is desirable for processing hard minerals such as diaspore, illite, and microcline. High reagent consumption and accumulation of silica impurity in the final product are the major challenges posed by the alkali process. In comparison, the milling route is preferable for the muscovite containing samples such as mica and sericite. The high energy consumption during prolonged milling is a disadvantage and cannot be optimized further. The process comprises a planetary ball mill for milling, a furnace set up for thermal treatment, stirred tank for acid and water leaching, and a settling tank for solid/liquid separation. The mild acid concentration is strategically investigated to prevent multi-element dissolution, reduce acid consumption, and minimize equipment corrosion. The developed process relies majorly on conventional equipment and holds the potential for scale-up investigation. The overall combined process flowsheet proposed for all the investigated samples is shown in Figure 10.