Standard y Utrzymania Zimowego Cezary Michalski – Menedżer ds. Utrzymania
Jerzy DOBRODZIEJ, Jacek WOJUTYŃSKI, Jerzy RATAJSKI, Tomasz SUSZKO, Jerzy MICHALSKI
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Transcript of Jerzy DOBRODZIEJ, Jacek WOJUTYŃSKI, Jerzy RATAJSKI, Tomasz SUSZKO, Jerzy MICHALSKI
Jerzy DOBRODZIEJ, Jacek WOJUTYŃSKI,
Jerzy RATAJSKI, Tomasz SUSZKO,
Jerzy MICHALSKI
INSTITUTE FOR SUSTAINABLE TECHNOLOGIES – NATIONAL RESEARCH INSTITUTEPOLAND
COMPUTER-AIDED PROJECTING OF GAS NITRRIDING PROCESSES WITH UTILIZATION OF SIMULATION AND
METHODS OF ARTIFICIAL INTELLIGENCE
PRESENTATION PLAN
PROBLEMS TO SOLVE
METHODS OF SOLVING
EXPERT SYSTEM
MODULE DESIGN OF DYNACMICS CHARACTERISTIC OF NITIRIDING PROCESSES
MODULE OF OPTIMISATION – EVOLUTIONARY ALGORITHMS
MODULE OF DATABASES
MODULE OF NEURAL NETWORK
Computer-aided processes of layers creationComputer-aided processes of layers creation – How it to do ? – How it to do ?
Classical approach – empirical methods of trial and error
PROBLEMS TO SOLVE
Process milieuSubstrate material
Su
bs
tra
t m
ate
ria
l
Material with a layer
layerthickness=2.8m
Ma
teria
l with
a la
ye
r
Material selection
Selection and inspection of control parameters
Selection of the layer’s properties
Forecasted propertiesof a layer
Process milieuSubstrate material
Material with a layer
APPLIED MODELS
Artificial neural networks
Fuzzy logic(expert systems)
Evolutionary algorithms
Data mining models – detection of similarities
and differences in processes
Analytical models: thermodynamic, statistical,
heuristic
METHODS OF SOLVING
Archival data
Measurements on-line Measurements off-line
Input parameters Output parameters
DATABASE
Computer-aided design of layers creation
MODULE OF DATABASES - INFORMATION STRUCTURE
Process
Parameter nameValueParameter type
Parameter nameValueParameter type
Parameter nameValueParameter type
...
Devices MaterialsEffects of the process (economical, ecological, innovative, etc.)
Stages of the process
Parameters for the whole
process
Substrate (before the
process)
Materials with layers
(after the process)
Parameter nameValueParameter type
Parameter nameValueParameter type
Parameter nameValueParameter type
Parameter nameValueParameter type
Parameter nameValueParameter type
Device 1
Device m
Stage 1
Stage n
...
Archival process In-situ process
MODULE OF DATABASES - APPLICATIONLocal 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 databaseData 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,Removing data from database
Data coping
Aggregating dispersed data from local databases
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.
Assuring accomplishing transactions such as adding, removing, modyfing and selecting/searching data
Transaction synchronisation with the concurrent access and creation of appropriate blocades while simultaneous modyfing the same data by many usersData coherence, that is inviolability of data integrity rules
Replicationality (data repetitiveness, reverse copy)
Concurrent access for many users
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
EXPERT SYSTEM - STRUCTURE OF EXPERT SYSTEM
User interaction module
Selection of input and output
parameters set
Formulation of database
query
Creation of the fuzzy logic function
Knowledge bases generation
DATABASE
Database integration module
Set of processes
Inference module
Fuzzification of input parameters
values Rules congregation
Defuzzification
Optimisation module
Knowledge bases optimisation
INFERENCE RESULTS:LAYER PARAMETERS VALUES
(output parameters)
12/16
EXPERT SYSTEM - APPLICATION
TASKPrediction of layers properties manufacturedin nitriding and PVD processes.
Support for designing the nitriding processes
technologies on the basis of substrate
and process milieu parameters.
System propertiesInference versatility Inferencing with diverse parameters.
Flexibility and coherence of inferencing Inferencing 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, 31.10.200713/16
EXPERT SYSTEM - VALIDATION IN THE FIELD OF NITRIDING PROCESSES
Process 1 Process 2 Process 3
Process duration [min] 570 60 480
Mean nitride potential [atm½] 4.75 3.25 6
Temperature [°C] 530 570 530
Amount of N2 in the atmosphere [%] 60 20 60
Amount of NH3 in the atmosphere [%]
40 60 40
Substrate material 40 HMJ 40 HMJ 38 HMJ
Fuzzification method triangle triangle triangle
Parameter name
Process 1 Process 2 Process 3
Obtained Predicted
Obtained Predicted
Obtained Predicted
Effective thickness g400 [mµ] 0.185 0.1767 0.17 0.16210 0.2 0.1913
Effective thickness g500 [mµ] 0.09 0.086 0.07 0.06680 0.1 0.0957
Effective thickness gr+50 [mµ] 0.345 0.3295 0.24 0.22890 0.3 0.2870
Grey area thickness [mµ] 5 4.7750 4 4.18480 4.5 4.3047
Nitride layer thickness [mµ] 10 10.4500 12.5 13.0775 10.5 10.0443
Maximum hardness HV 551 575.795 538 562.8556
552 575.9568
Surface hardness HV1 659 629.345 692 723.9704
644 671.9496
Surface hardness HV10 642 670.890 630 600.8940
625 652.125
Surface hardness HV0.5 519 495.645 512 535.6544
532 555.0888
Surface hardness HV5 544 519.520 559 533.1742
562 537.6092
Process milieu and substrate
Results
10,6710,37 10,49
9,36 9,578,95
7,1 7,377,81
4,49 4,62 4,34
0
2
4
6
8
10
12
Err
or
va
lue
[%
]
22 23 24 25 26 27
Number of rules
Dependence of the error value on the number of rules in knowledge base
Process 1 Process 2 Process 3
MODULE DESIGN OF DYNACMICS CHARACTERISTIC OF NITIRIDING PROCESS
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.
Designing of process environment characteristics
MODULE DESIGN OF DYNACMICS CHARACTERISTIC OF NITIRIDING PROCESS
temperature changes
potential changes
nitrogen concentration
profiles
concentration on phase borders
nitrides area thickness 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.
MODULE OF NEURAL NETWORK
Result
PurposePrediction of micro hardness distribution in the function of:Process durationTemperature Nitridning potential
Module properties Optimal structure of neuron network.
Generalization option.
Possibility of adapting for diverse materials substrates.
MODULE OF OPTIMISATION – EVOLUTIONARY ALGORITHMS
Result: process parameters
Purpose Temperature 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.
CONCLUSIONS
Modification and development of technologies, particulary working out new technological solutions.
Precise planning of processes and obtaining surface layers described by set parameters
Designed system enables:
Reduction in energy and material consumption, as a result of processes duration shortening.
The system might be used for:
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