With the increasing demands of low noise and high strength from gear transmission system in industry applications, a collaborative optimization considering both geometric and physical performances has been increasingly significant for high-performance complex manufacturing of the hypoid gears.
Recent research reports describe efforts to create automated assembly sequence planners, which will help design and manufacturing engineers analyze increasingly complex and customized products and the dynamic conditions found in modern computer integrated manufacturing (CIM) systems.
As the limitations of the monitoring models become increasingly obvious in face of even more complex manufacturing processes, in this work, the active learning process monitoring (AL-PM) model is developed.
The visualisation and optimisation of multi-mode processes becomes increasingly complex with the requirement to track batches and sub-batches and predict the optimal manufacturing process, with respect to equipment fit, time pressures and cost.
Nevertheless, available product quality data show that pharmaceutical manufacturing and its regulation, in general, are being confronted with new and increasingly complex challenges, such as unacceptably high occurrence of product recalls and drug shortages.
And while advanced robots capable of increasingly complex tasks raise complicated questions about the shape of human labor in a number of fields like manufacturing, in high-risk situations like bomb disposal, those answers seem much simpler.
Increasingly complex market dynamics due to unpredictable forecasts and shorter innovation cycles require maximum flexibility from production companies and their manufacturing systems.
Moreover, the trials have become increasingly complex.
Microsoft's business is increasingly complex.
Increasingly complex moves are often introduced.
