Design of Simulations
This project aims at the optimization of the design of the bushing blocks assembled into a specific type of external gear pump.
The bushing block is one of the key parts of a gear pump. Its optimization depends on a large amount of parameters and has to fulfil multiple economical, functional and mechanical requirements.
The proper optimization of the blocks is a crucial element of the pump design. In the specific case here analyzed, due to important economical needs and to constraints coming from the production process, a re-design of many key geometrical characteristics of the blocks is required.
The purpose of this study is to optimize several responses simultaneously. These responses come from different mathematical models and are related to performance, noise level and cost of the pump.
The optimization is carried out using statistical definition of the process tolerances and DFSS (Design For Six Sigma) tools, such as Monte Carlo simulation, which is performed through @RISK.
The presentation discusses how automation can be applied to the process measurements (using CMM - Coordinate Measuring Machine - data acquisition) of the block dimensions, in order to define the process natural variation and the type of distribution better approximating the real data.
These evaluations are then used as input of a Monte Carlo simulation with multiple models and responses.
The above mentioned techniques will lead to a combined optimization of the design parameters. This optimization will enable to achieve all the targets and to satisfy both economical requirements and process constraints.
Simulations with mtbEngine
A probable Excel, Minitab, @Risk and mtbEngine Chat
Simulations with @Risk
The video is areal model, implemented through the @Risk5 use. The additional module shows the spatial analysis of data correlation. It is a freeware and it can be used with @Risk. vers >=4.5.
Simulations with Crystall Ball
The same example using Crystal Ball insted of @Risk
Simulations with Champion of Italy
The case concerns an injection molding process in which three inputs or factors are involved (Mold Temperature, Cycle Time and Hold Pressure), that jointly contribute to determine the output variability (the variable Length).
Simulations with Champion of Italy
A plastic compound is a mixture of 4 polymers + some additivies. To achieve predictable and adeguate impact properties in the final compound, polymer A must be controlled to a level of a (50 ± 2) % wt. Given the inherent variation in the feeding process will the compound meet our requirements ?
Simulations with Champion of Italy
An engineer at an automobile design center needs to specify components for piston and cylinder assemblies that work well together. To do this, he must perform an optimal stack tolerance analysis, where he calculates the dimensions of the components to be within certain tolerance limits. Given the variability in the statistical dimensions of seven separate parts, the engineer must choose optimal tolerance levels that meet the assembly gap design criteria...