The Need for Controlling & Quantifying Variation
Engineering applications are complex and several factors are likely to have an impact on most processes. Engineers need to know which factors are influential in their applications. In order to manufacture robust products, they also need to evaluate how important they are and optimise their settings.
Statistical methods have offered a set of tools designed to control and quantify variation for many years. These methods fall into a two-fold portfolio. First, they offer tools for designing efficient experiments. Second, they provide tools for analysing the experimental data.
Screening & Optimisation Experimental Designs
In order to design experiments efficiently, a wide array of experimental designs is made available to engineers – DoE methodology. The choice of the most efficient experimental design depends on the goal of the experiment. Some designs are designed for identifying influential factors, others are designed to optimise factor settings, other are used whenever factors are constrained.
Controlling & Quantifying Variation in the Data Analysis Phase
Statistical methods offer a wide range of tools for controlling, visualising and quantifying variation in experimental data. These methods allow engineers to understand key interactions in processes.
- Data visualisation tools
- Analysis of designed experiments
- Classic modelling techniques
- Reliability Analysis – modelling time to failure data
Advancements in Data Science & Engineering Applications
The role of statistics in engineering used to be rather predictable, but the situation has changed in recent years due to the advancement of computer technology and the resulting computing power.
Such advances now enable engineers to harness the power of advanced DoE as well machine-learning tools as a modelling tool for capturing and predicting complex relationships in factors.
Advanced, Dynamic & Flexible Experimental Designs
There are experimental situations where constraints limit the use of classic experimental designs. Several new more adapted have become available in recent years: optimal designs, sequential dynamic designs, etc. For instance, they enable engineers to optimise the coverage of the experimental domain with a pre-defined number of runs.
Machine Learning and Large Process Databases
Linear models work well in simple situations. Engineering applications are complex and several factors are likely to have an impact on them. The interactions between factors is often quite complex. Classic linear models are not flexible enough and often fail at capturing some important relationships.
Machine-learning tools offer an array of much more flexible models. For instance, they provide invaluable information for extracting information from observational studies based on historical process data.
Our experts offer state-of-the-art quantitative tools to implement advanced machine-learning based solutions for complex engineering applications.
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