Gas Nitriding Processes Simulation with AI Methods

1. 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

2. 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

3. 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 ?

4. 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

5. 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

6. 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

7. 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

8. 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

9. EXPERT SYSTEM - VALIDATION IN THE FIELD OF NITRIDING PROCESSES Process milieu and substrate Results

10. 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.

11. 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.

12. 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.

13. 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.

14. 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: