The answer to that question lies in data science, machine learning and the increasingly arising field of explainable artificial intelligence (XAI). If you are curious how it works, then take a few minutes and read on!
"The branch of mathematics concerned with the analysis of strategies for dealing with competitive situations where the outcome of a participant's choice of action depends critically on the actions of other participants." [1]
Well, the definition from Oxford Dictionaries doesn't seem to help much for our understanding, so let's visualize it with the following example:
Consider you have a football game with 11 players in each team. After thrilling 90 minutes of high-class football, both teams split up 2:1. The principles of game theory could now be used to find out how much each of the players contributed to the end-result. (Basically, how valuable were the individual players for their team.) There are various approaches to calculate the contributions of the individual players. The specific approach we will have a look at in the following lines of this article are Shapley Values (invented by Lloyd Shapley in 1951). Shapley values are used to calculate the average marginal contribution of each individual player - basically the average contribution of each player across all possible orders in which they can be brought into the match. [2]
a) The pressurization in which pressure is applied to the holding furnace which causes the molten aluminum to rise through the riser tube into the mold
b) Filling up the mold during which the pressure is increased to fill the mold in a controlled and uniform way
c) The solidification in which a high pressure is applied to prevent shrinkage in the casted wheel.
The problem faced by our customer in this case were microporosities, blow holes and shrinkage which lead to an increased cost & remelting, excessive emissions and reduced OEE.
To enable operators, shift supervisors, process engineers and foundry managers to proactively take corrective actions in order to avoid scrap, a machine learning (ML) based model can be developed to predict the quality of the casting during the LPDC process. This model then takes real-time data collected within the production process (e.g. temperatures, air cooling rates, pressures etc.) to continuously monitor the casting process in near real-time.
The predictive quality model helps to detect quality deviations as early as possible and enable the engineers to make adjustments and to eliminate the root-causes of the quality deviations. But what if the root-causes and measures to be taken are unknown?
Game Theory - Contribution of Individual Players to a Result of a Game
Let's first clear the dust a bit and have a short look at what "game theory" actually is."The branch of mathematics concerned with the analysis of strategies for dealing with competitive situations where the outcome of a participant's choice of action depends critically on the actions of other participants." [1]
Well, the definition from Oxford Dictionaries doesn't seem to help much for our understanding, so let's visualize it with the following example:
Consider you have a football game with 11 players in each team. After thrilling 90 minutes of high-class football, both teams split up 2:1. The principles of game theory could now be used to find out how much each of the players contributed to the end-result. (Basically, how valuable were the individual players for their team.) There are various approaches to calculate the contributions of the individual players. The specific approach we will have a look at in the following lines of this article are Shapley Values (invented by Lloyd Shapley in 1951). Shapley values are used to calculate the average marginal contribution of each individual player - basically the average contribution of each player across all possible orders in which they can be brought into the match. [2]
Machine Learning in Production - Quality Prediction in Aluminum Casting
Further, let's take the use-case of quality prediction in the casting of aluminum wheels with the low-pressure die casting process. In this, molten and degassed aluminum is stored in the holding furnace of a low-pressure casting machine. The casting process takes place in 3 steps:a) The pressurization in which pressure is applied to the holding furnace which causes the molten aluminum to rise through the riser tube into the mold
b) Filling up the mold during which the pressure is increased to fill the mold in a controlled and uniform way
c) The solidification in which a high pressure is applied to prevent shrinkage in the casted wheel.
The problem faced by our customer in this case were microporosities, blow holes and shrinkage which lead to an increased cost & remelting, excessive emissions and reduced OEE.
To enable operators, shift supervisors, process engineers and foundry managers to proactively take corrective actions in order to avoid scrap, a machine learning (ML) based model can be developed to predict the quality of the casting during the LPDC process. This model then takes real-time data collected within the production process (e.g. temperatures, air cooling rates, pressures etc.) to continuously monitor the casting process in near real-time.
The predictive quality model helps to detect quality deviations as early as possible and enable the engineers to make adjustments and to eliminate the root-causes of the quality deviations. But what if the root-causes and measures to be taken are unknown?
Explainable Artificial Intelligence (XAI) - Determination of the Most Influential Parameters
That's exactly where both of the terminologies "game theory" and "process optimization" come together and the connection of these is explainable artificial intelligence (XAI).
Explainable Artificial Intelligence (XAI) describes a field of research for the development, advancement and improvement of methods to make predictions or classifications of ML-based models interpretable and/or functionally comprehensible.
