BMC-optimizer (v1.1)

Bayesian Optimization python program to suggest new bulk moulding compound recipes for bio-based composites. This program is designed in collaboration with NPSP B.V.

Overview

This python scripts first builds a Gaussian process surrogate model given the data provided. Subsequently, several new recipes are proposed within the search space boundaries.

Contents of this readme:

• Files
• Getting started
• Database file
• Configuration file
• Objective file
• Edit the sourcecode
• Available commands
• Available parameters

The files in this repository

• bmc_requirements.txt: Pip requirements file. No need to change this.
  
• config.txt: Configuration file with model paramaters. Parameters are loaded up at the start of the program.
  
• help.txt: File for which the contents are displayed if you are executing help within the python program.
  
• input_fake.xlsx: Artifically generated data used as a placeholder for the bio-based composite database.
  
• objective.txt: File that states the different objectives, their weights and if they are to be minimized or maximized
• model.py: The python model to execute.
  
• calculator_for_model.xlsx: Excel tool that converts mass-based recipes to the 3 parameter-based format and back.
  

Getting started

The following guidelines will help you get started with using the optimization model.

Linux command line

Step 1) Clone the repository: git clone https://github.com/mpvanderschelling/BMC-optimizer.git
Step 2) Put a database excel file in the /files/ folder
Step 3) Navigate to the /Linux/ folder within the repository: cd <path of forked repository>/Linux
Step 4) Run the model by executing ./model

Windows

Step 1) Download the repository as .zip file.

Click the green 'Code' button at the top of the window and then 'Download ZIP'

Step 2) Unzip the file somewhere locally.
Step 3) Put a database excel file in the /files/ folder
Step 4) Run the model by double-clicking model_windows.exe

Database file

You have to provide your own database file. The database file consists of some necessary input paramaeter columns. Each row contains a new sample.

The database file input_fake.xlsx is artificially generated data to serve as a placeholder. This data should not be used for experiments

name	type fiber	type filler	fiber ratio	filler ratio	dry ratio
FlaxOli50	Flax	Olive stone	0.0995	0.1542	0.6532
ReedPeach50	Reed	Peach stone	0.0732	0.1375	0.5592
...	...	...	...	...	...

Other columns are selected as output columns

testable?	density	impact	stiffness	flex. strength	stiffness
yes	1.7187	2.1	9.7	28.8	4770
yes	1.4654	2.3	9.5	32.1	5647
...	...	...	...	...	...

(v1.1) The testable? column is optional; if a plate has failed and is not up for testing, the output columns can be left blank and the testable? cell is filled with the value no. Subsequently, a huge penalty is given to the total score of the plate, making it unlikely that the model will explore in that region. The value of the huge penalty can be set with the badplatepenalty parameter.

Note: The composition of the premix is hard-coded and should be the same over all the inputs.

Configuration file

The config.txt file contains the parameters you want to use in the model. For each line, specify the parameter, followed by a space and end with the value of that parameter. Lines proceding a # act as comments and will not be imported.

For more information on the optimization model parameters, consult the scikit-optimize documentation.

# search space boundaries
fiber_lb 0.05
fiber_ub 0.25
filler_lb 0.0
filler_ub 1.0
dry_lb 0.4
dry_ub 0.75

# Optimization model parameters
acq_func EI
strategy cl_min

# Recipe parameters
max_recipes 6
total_mass 2800.0

# Score parameters
badplatepenalty 2.0

author Martin van der Schelling

You can alter the configuration file and save it locally to quickly load up custom parameters. A detailed description of the available parameters is found here.

Objective file

This model uses is a single-objective optimization method. However, by combining multiple output parameter to a single objective, more mechanical properties of the bio-based composite can be considered in the search.

For every output element, a normalized value relative to the best and worst properties of the fiber-natural filler combination is taken. The values are multiplied by an individual weight and added together to form a bio-based composite penalty 'score'. The objective is to find the recipe with the smallest penalty.

Every line in the objective.txt file starts with the output parameter (same as one of the columns in the database file). Lines preceding with a # are ignored. The second element is either min or max.

• min: the requested parameter has to be minimized (e.g. density)
• max: the requested parameter has to be maximized (e.g. flexural strength)

Next, a weight is added. If no weight is given, the normalized value is multiplied by 1.0. The final objective file looks something like this:

density min 0.3
stiffness max
impact max 0.6
flex. strength max 0.3

Currently, it is not possible to edit the objective values within the program. They have to be altered and saved in the objective.txt file before running the model.

If output data is missing for specific recipes, the influence of that parameter will not be influencing the final score of the recipe.

Edit the sourcecode

You need to have a Python interpreter installed like Anaconda for this method.

Step 1) Clone the repository: git clone https://github.com/mpvanderschelling/BMC-optimizer.git
Step 2) Make sure you are using Python 3.6+
Step 3) Navigate to the /Python/ folder
Step 4) Install the required packages: pip install -r bmc_requirements.txt

Alternatively, you can install the required packages yourself: scikit-optimize, pandas, datetime, xlrd.

Step 5) Put a database excel file in the /files/ folder
Step 6) Open, edit and run the model /Python/model.py in any python interpreter

Available commands

The commands are divided into 4 categories:

• show for showing parameters and data on the screen.
• set for setting parameters to a certain value.
• ask for asking the surrogate model for proposed recipes.
• print for writing the config parameters to a file or exporting the suggested recipes.

SHOW
show show the config parameters
show config show the config parameters
show data show the entire database. If no data is important, it will ask to set data.
show <param> show the requested parameter

SET
set config import the config parameters from the config file again

Warning: this neglects any changes of config parameters during the current session

set now set the variable now to the current time
set data import a database (*.xlsx) and set to variable data
set materials specify the materials you want to investigate (fiber, natural filler)
set output calculate the weighted single-objective penalty score for each selected BMC
set batch specify the amount of BMC doughs you want to make
set <param> <value> set the requested parameter to the requested value

ASK
ask model ask the optimization model for new recipes

PRINT
print config save the altered config parameters to the config.txt file
print model print the suggested recipes to a .csv file
print scores print the penalty scores for each output column per row to a .csv file

MISC
help/? show the available commands
exit exit program

Available parameters

The following parameters can be used by set and show

Setting fiber_t and filler_t is more convenient by calling set materials

name	type	description
acq_func	string	Type of acquisition function used. More info
author	string	Name that will be printed on the recipes
badplatepenalty	float	Penalty that is given to plates that cannot be tested (>= 1.0)
batch	int	Number of recipes to be generated
config	list	Copy of the imported config.txt file
data	DataFrame	Imported BMC database
databasename	string	Filename of the imported database
dry_lb	float	Lower bound of the dry materials parameter
dry_ub	float	Upper bound of the dry materials parameter
fiber_lb	float	Lower bound of the fiber parameter
fiber_ub	float	Upper bound of the fiber parameter
fiber_t	string	Name of the fiber to investigate
filler_lb	float	Lower bound of the natural filler parameter
filler_ub	float	Upper bound of the natural filler parameter
filler_t	string	Name of the natural filler to investigate
max_recipes	int	Maximum number of recipes to request
now	datetime	Current time at start of program
output	Series	Calculated total penalty score of each selected entry in the database
scores	DataFrame	Calculated penalty score for each individual output of each selected entry in the database
strategy	str	Parallel Bayesian Optimization strategy. More info
total_mass	float	Total mass of each BMC dough

Made by Martin van der Schelling