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This allows the engineers to avoid surprises and to identify the parameters that could dominate machine performance in each case.
In high-volume production, machining parameters have amplified impacts on the machine performance in terms of productivity (cycle time), reliability (tool life), and product quality (surface finish).
This paper develops analytical methods for predicting the torsional stiffness of capstan drives to enable designers to better assess machine performance in the design study phase.
In their research on machine performance in the CTL harvesting system, Nurminen et al. (2006) determined the duration of individual operations with the use of the counter in a camcorder, reaching a very high accuracy of 1/24 s.
Modeling complications result from the sensitivity of machine RVs to different settings of machine operating parameters and the difficulty to estimate machine performance in terms of NP under stochastic running conditions.
Motivated by the large gap between human and machine performance in such conditions we take inspirations from the organization of the mammalian auditory cortex in the design of our features.
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Cutting orientation does not influence the machining performance in both cases.
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 comparison results show that the machining performance in the laser beam cutting process has been remarkably improved through proposed approach.
Additionally, the coating was tested for its machining performance in continuous turning of AISI 4340 hardened steels and AISI 4140 pre-hardened steels at representative application conditions, and compared to corresponding industrial benchmarks.
Dual-layer graded composite diamond coated (t = 14.7 min) and mono-layer MCD coated (t = 13.5 min) tools showed superior machining performance in comparison to that of the dual-layer composite diamond coated tool (t = 9.8 min).
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