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This proof-of-concept project will be conducted in parallel with traditional processes for an ongoing clinical trial.
The ongoing efforts in developing additive manufacturing (AM) processes could promote an increasing competition of AM with traditional processes.
Thus, this manufacturing technique will more and more complement existing production chains and compete with traditional processes.
This is a hybrid process, not energy-intensive and is competitive with traditional processes in which reaction and separation are carried out in different devices.
In general, monolithic and multilayer coatings have significantly better behaviour when compared with traditional processes of protecting functional surfaces for plastic transformation equipment.
Metal fabrication with traditional processes like injection molding, can be noisy, create welding fumes and exhaust, and require the use of lots of chemicals by workers, who are also subject to injury from operating heavy machinery.
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It is shown that additive manufacturing can compete with traditional process chains for small production runs.
Comparing with traditional process, the novel process shows the advantage of higher recovery of WO3, lower consumption of chemicals, and discharge of waste water.
Both sets of image features are combined with traditional process measurements to obtain an inferential model by partial least squares (PLS) regression.
Breaking with traditional process, this research considered the National Green Network model not as a final plan, but as point of departure for an iterative adjustment and redesign process through the charrette and an additional detailed design stage.
This paper presents a multivariate data fusion procedure for design of dynamic soft sensors where suitably selected image features are combined with traditional process measurements to enhance the performance of data-driven soft sensors.
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