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Brick Machine Learning

Brick Machine Learning is the solution that, simulating various configurations used in automated production lines, recommends the optimal mix to achieve the maximum performance.

Brick Machine Learning

BACKGROUND

The overall performance of an automated line depends on an extremely high number of variables, the behaviour of which is influenced by the performance of the individual devices or machines that make up the line, from the planning of production set by the end customer, to scheduled maintenance and extraordinary interventions (to name just a few of the key factors that have the most influence on the system).

Imagine a system in which the information collected in the past across various automated lines that have already been implemented, makes it possible to identify the perfect mix of machinery and devices that ensure the overall performance expected by the customer:

  • The performance data associated with previously implemented lines is collected and organised into a centralised system;
  • Designers identify the list of phases (or processes) required on the new line being developed;
  • The simulator analyses the new scenario on the basis of previously collected data and recommends the optimal mix of machinery to perform the steps specified by the system designers;
  • The simulator forecasts the overall future performance according to the recommended mix.

All this is possible thanks to the Brick Machine Learning solution.

The solution

Dashboards through which the user can, for each individual production line process, define the best “asset mix” and OEE (Overall Equipment Effectiveness) indicator.

The solution makes it possible to simulate various configurations used in automated production lines, in order to recommend the optimal mix of devices required to achieve the overall performance requested by the end customer.

Focus On

Automated production line manufacturers have to contend with the variety of automation devices available. One of their key goals is to identify the perfect mix of devices and machinery needed to optimise production lines, making full use of their potential and predicting their productivity. Their customers want to know in advance the expected overall performance of the new automated line they have purchased.

The monitoring capability offered by the Brick platform facilitates the real time collection of all data from assembly lines that have already been installed. Brick Machine Learning (BML) controls, cleans and models the collected data, in order to predict the performance of each new machinery combination. The new solution is designed to define the best combination of machinery for a specific production setup.


The current scenario - Critical aspects

Overall performance estimates are typically based on complex simulation models driven by discrete events and by the expertise and experience of design engineers:

  • The modelling of the line requires the analysis of a significant amount of data, often fragmented across different databases and difficult to reconciliate;
  • The overall performance of the line is estimated on the basis of tests carried out over medium/long periods of time, only once the line has already been implemented;
  • The actual overall performance is calculated retrospectively, following a long production analysis period (typically a year or more).


The use cases developed

A Simulation and a Machine Learning module has been developed, which facilitates the configuration and the execution of simulations designed to predict the overall efficiency of an automated line by integrating with Hermes Reply’s Brick platform (in particular with its Monitoring component).

Through front-end web development users can:

  • Select the list and sequence of work phases that make up the line being simulated;
  • Query the predictive model capable of inspecting and evaluating data sets previously collected in the field;
  • View the optimal mix of machinery corresponding to the work phases specified, which will be proposed by the system;
  • View the expected overall performance based on the proposed mix of machinery.

By integrating with Microsoft's Azure Machine Learning cloud services, the platform:

  • Generates various proposals of optimal mixes of machinery by analysing the performance of all the machinery capable of performing the steps indicated by the designers;
  • For each proposal, the solution calculates the overall performance of the line based on analysis of the individual component (or machine) performance.


The solution architecture

The Brick Machine Learning technology solution consists of a web application published on the Microsoft Azure cloud platform and integrated with a Machine Learning algorithm implemented on Azure and consumable through a web-service.

The application architecture is implemented using the following components:

  • A modern HTML 5 and JavaScript web front-end, based on the AngularJS technology;
  • A Machine Learning layer implemented on the Azure platform and consumable by invoking REST (Representational State Transfer) web services;
  • A data persistence layer consisting of a relational MS SQL database.


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