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Optimization of machining processes is of primary importance for increasing machining efficiency and economics.
Cutting parameter optimization of machining processes is crucial for green manufacturing and needs to take energy consumption, cutting force and processing time into consideration.
The primary objective for modeling of machining processes is to develop a predictive capability of machining performance in order to facilitate effective planning of machining operations.
The problem of stability in the machining processes is very important and is strictly connected with the final quality of the product.
Generated residual stress in the machining processes is one of the most important factors which affects significantly service quality and component life.
The primary objective of machining processes is to machine a part with required size, shape and surface finish accuracies at a minimum production cost or a minimum production time by determining optimum process parameters.
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Within the context, machining tasks, machining resources, and machining processes are represented by data attributes.
Noting that the surfaces from machining processes are essentially derived from severe thermomechanical conditions akin to those explored here, the implications of subgrain size-maps for controlling surface microstructures on components manufactured by machining processes are discussed.
After the part is printed, machining processes are often employed to remove the supports from the solid part.
The risk assessment, exposure and understanding of the release of embedded carbon nanotubes (CNTs) and carbon nanofibers (CNFs) from commercial high performance composites during machining processes are yet to be fully evaluated and quantified.
To overcome this, non-traditional machining processes are often used.
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