Jerzy DOBRODZIEJ, Jacek WOJUTYŃSKI, Jerzy RATAJSKI, Tomasz SUSZKO, Jerzy MICHALSKI INSTITUTE FOR SUSTAINABLE TECHNOLOGIES – NATIONAL RESEARCH INSTITUTE POLAND 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 creation – How it to do ? Classical approach – empirical methods of trial and error PROBLEMS TO SOLVE Process milieu Substrate material Substrat material Material with a layer layer thickness=2.8  m Material with a layer Material selection Selection and inspection of control parameters Selection of the layer’s properties

Forecasted properties of a layer Process milieu Substrate 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 name Value Parameter type Parameter name Value Parameter type Parameter name Value Parameter type... Devices Materials Effects 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 name Value Parameter type Parameter name Value Parameter type Parameter name Value Parameter type Parameter name Value Parameter type Parameter name Value Parameter type Device 1 Device m Stage 1 Stage n... Archival processIn-situ process

MODULE OF DATABASES - APPLICATION 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 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, 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 users Data 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 TASK Prediction of layers properties manufactured in nitriding and PVD processes. Support for designing the nitriding processes technologies on the basis of substrate and process milieu parameters. System properties Inference 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, /16

EXPERT SYSTEM - VALIDATION IN THE FIELD OF NITRIDING PROCESSES Process 1Process 2Process 3 Process duration [min] Mean nitride potential [atm ½ ] Temperature [°C] Amount of N 2 in the atmosphere [%] Amount of NH 3 in the atmosphere [%] Substrate material 40 HMJ 38 HMJ Fuzzification method triangle Parameter name Process 1Process 2Process 3 ObtainedPredictedObtainedPredictedObtainedPredicted Effective thickness g400 [mµ] Effective thickness g500 [mµ] Effective thickness gr+50 [mµ] Grey area thickness [mµ] Nitride layer thickness [mµ] Maximum hardness HV Surface hardness HV Surface hardness HV Surface hardness HV Surface hardness HV Process milieu and substrate Results

MODULE DESIGN OF DYNACMICS CHARACTERISTIC OF NITIRIDING PROCESS temperature changes potential changes nitrogen concentration profiles concentration on phase borders nitrides area thickness 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 Purpose Simulation 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 Purpose Prediction 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.

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