Production processes for large module gears are characterized by small batch sizes and high workpiece costs.
Nowadays machine tool control, for small batch sizes, suffers from a lack of flexibility at machine level.
In fact, our tests revealed that for small batch sizes used in instantaneous mixtures, the performance of the AV method using kurtosis penalty does not improve compared to the pure AV method.
Today's machining shop floors, characterized by a large variety of products in small batch sizes, require dynamic process planning capabilities that are responsive and adaptive to the rapid changes of production capacity and functionality.
Today's machining shop floors, characterized by large variety of products in small batch sizes, require dynamic control and real-time monitoring capabilities that are responsive and adaptive to the rapid changes of production capability and functionality.
This paper proposes a generic feature association method and a detailed framework that can unify product and process models in order to satisfy customer orders with small batch sizes and high variations.
Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch sizes, requiring real-time monitoring for dynamic distributed decision making, and adaptive control capabilities that are able to handle, in a responsive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality.
While being an impressive tool for next generation high-throughput experiments, such a system is still expensive and not suitable for the relatively small batch sizes and constantly changing protocols encountered in the daily work of characterizing biological entities and developing new methods.
This will see Industry 4.0 technologies not only increase efficiency and reduce waste, but also enable smaller batch sizes, more personalised products and greater product innovation.
