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COMPUTER-AIDED PROJECTING OF GAS NITRRIDING PROCESSES WITH UTILIZATION OF SIMULATION AND METHODS OF ARTIFICIAL INTELLIGENCE. Jerzy DOBRODZIEJ, Jacek WOJUTYŃSKI, Jerzy RATAJSKI, Tomasz SUSZKO, Jerzy MICHALSKI. IN STITUTE FOR SUSTAINABLE TECHNOLOGIES – NATIONAL RESEARCH INSTITUTE POLAND.

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COMPUTER-AIDED PROJECTING OF GAS NITRRIDING PROCESSES WITH UTILIZATION OF SIMULATION AND METHODS OF ARTIFICIAL INTELLIGENCE

Jerzy DOBRODZIEJ, Jacek WOJUTYŃSKI,

Jerzy RATAJSKI, Tomasz SUSZKO,

Jerzy MICHALSKI

INSTITUTE FOR SUSTAINABLE TECHNOLOGIES – NATIONAL RESEARCH INSTITUTE

POLAND

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PROBLEMS TO SOLVE

METHODS OF SOLVING

MODULE OF DATABASES

EXPERT SYSTEM

MODULE DESIGN OF DYNACMICS CHARACTERISTIC OF NITIRIDING PROCESSES

MODULE OF NEURAL NETWORK

MODULE OF OPTIMISATION – EVOLUTIONARY ALGORITHMS

PRESENTATION PLAN

slide3

layer

thickness=2.8mm

Material with a layer

Substrat material

Material with a layer

Process milieu

Substrate material

Material selection

Selection of the

layer’s properties

Selection and inspection

of control parameters

PROBLEMS TO SOLVE

Classical approach – empirical methods of trial and error

Computer-aided processes of layers creation – How it to do ?

slide4

Material with a layer

Substrate material

Process milieu

Measurements on-line

Measurements off-line

APPLIED MODELS

Fuzzy logic

(expert systems)

Artificial neural networks

Evolutionary algorithms

Computer-aided design

of layers creation

Forecasted properties

of a layer

Data mining models – detection of similarities and differences in processes

Analytical models: thermodynamic, statistical, heuristic

METHODS OF SOLVING

DATABASE

Archival data

Output parameters

Input parameters

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MODULE OF DATABASES -INFORMATION STRUCTURE

Archivalprocess

In-situ process

Process

Parameter name

Value

Parameter type

Parameter name

Value

Parameter type

Stages of the process

Parameters for the whole process

Materials

Devices

Effects of the process (economical, ecological, innovative, etc.)

Stage 1

Materials with layers (after the process)

Substrate (before the process)

Device 1

Parameter name

Value

Parameter type

Parameter name

Value

Parameter type

Parameter name

Value

Parameter type

Parameter name

Value

Parameter type

...

...

Stage n

Device m

Parameter name

Value

Parameter type

Parameter name

Value

Parameter type

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Local database

Collection of data in local databases

Operational tasks

Registration of a new process by defining process structure and saving the created structure into the database

Assuring accomplishing transactions such as adding, removing, modyfing and selecting/searching data

  • Data modification
  • parameters set which describes process,
  • data of technological stages,
  • device data,
  • material or layer data,
  • dynamic characteristics of the process (or stage),
  • graphical data concerning results of layer structures tests,

Transaction synchronisation with the concurrent access and creation of appropriate blocades while simultaneous modyfing the same data by many users

Data coherence, that is inviolability of data integrity rules

Replicationality (data repetitiveness, reverse copy)

Removing data from database

Data coping

Concurrent access for many users

Aggregating dispersed data from local databases

  • Providing multi-level security systems against access to data:
  • setting accounts for users
  • setting system rights
  • assigning access rights to objects in database
  • guaranting access to tables and atributes in tables

Making access to data via the Internet according to users rights

  • Data search
  • SQL queries,
  • ranking search,
  • fuzzy search for data mining requirements and artificial intelligence models.

MODULE OF DATABASES-APPLICATION

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DATABASE

EXPERT SYSTEM -STRUCTURE OF EXPERT SYSTEM

User interaction module

Database integration module

Selection of

input and output

parameters

set

Formulation

of database

query

Creation of the fuzzy logic function

Set of processes

Knowledge bases generation

Inference module

Optimisation module

Knowledge bases optimisation

Fuzzification

of input parameters

values

Rules

congregation

INFERENCE RESULTS:

LAYER PARAMETERS VALUES

(output parameters)

Defuzzification

12/16

slide8

EXPERT SYSTEM - APPLICATION

TASK

Prediction of layers properties manufactured

in nitriding and PVDprocesses.

Support for designing the nitriding processes

technologies onthe basis of substrate

and process milieu parameters.

System properties

Inference versatilityInferencing with diverse parameters.

Flexibility and coherence of inferencingInferencing on the basis of different

domains parameters:continue (e.g. temperature

in time function), discrete (e.g. value of layer

resistance to corrosion), nominaly ordered

(e.g. type of mechanical treatment used for substrate surface).Inference adaptation and self-learning

Using data referring to new and completed processes

as well as created layers in order to improve inference quality.

IFHTSE 2007 Congress Adam Mazurkiewicz, 31.10.2007

13/16

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EXPERT SYSTEM - VALIDATION IN THE FIELD OF NITRIDING PROCESSES

Process milieu

and substrate

Results

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nitrides area thickness

temperature changes

nitrogen concentration profiles

concentration

on phase borders

potential changes

MODULE DESIGN OF DYNACMICS CHARACTERISTIC OF NITIRIDING PROCESS

Designing of process environment characteristics

Purpose Designing of atmospheres for gas nitriding process.

Module properties

Two- and tree-component atmospheres:

Nitriding potential model on the basis of isoconcentrative characteristics or established by the designer.

Model of dissociation level.

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nitrides area thickness

temperature changes

nitrogen

concentration

profiles

concentration

on phase borders

potential changes

MODULE DESIGN OF DYNACMICS CHARACTERISTIC OF NITIRIDING PROCESS

PurposeSimulation of layer growth kinetics.

Simulation of nitrogen concentration profiles on phases borders.

System properties

Short time of calculations.

Additional software for mathematical calculations not required.

Possibility of layer growth in time animation.

Possibility of concentrations on phase border animation.

Possibility of concentration profiles on phase border animation.

slide12

Result

MODULE OF NEURAL NETWORK

PurposePrediction of micro hardness distribution in the function of:

Process duration

Temperature

Nitridning potential

Module properties

Optimal structure of neuron network.

Generalization option.

Possibility of adapting for diverse materials substrates.

slide13

Result: process parameters

MODULE OF OPTIMISATION – EVOLUTIONARY ALGORITHMS

PurposeTemperature and nitriding potential prediction in order to obtain the projected

micro hardness distribution

System properties

Determining optimal average values of temperature and potential

in successive gas nitriding process.

Possibility of adapting for diverse materials substrates.

slide14

CONCLUSIONS

Modification and development of technologies, particulary working out new technological solutions.

Reduction in energy and material consumption, as a result of processes duration shortening.

Competitiveness’ enhancement of SMEs operating in surface treatment area by improving en end product quality

Designing of new properties profiles, for instance, toward development of extremely hard layers with high adhesion in aim to increase their life by surface hardness enhancement, wear resistance (pitting, micro-pitting and scuffing) and endurance of machine and tools’ elements

Creating new SMEs which are consultants in the area of surface treatment, i.e. selection of single treatment or joint treatment and their parameters for certain applications

Precise planning of processes and obtaining surface layers described by set parameters

Designed system enables:

The system might be used for:

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