An Effective Algorithm Model for a Soft

PIN System

Madhu Sharma[1]

, O.P. Rishi[2]

,Ram Prakash[3]

, Durgesh Kumar Mishra[4]

[1]

CMJ University,Meghalaya,jpr.madhu@gmail.com [2]

Associate Prof., Univ.of Kota, Kota-INDIA,dr.oprishi@uok.ac.in [3]

Reader, BIT, Mesra,Jaipur Centre-INDIA,ramprakash@bitmesra.ac.in [4]

Professor & Head,Dept. of Computer Science & Engineering,

Sri Aurobindo Institute of Technology, Indore-MP

drdurgeshmishra@gmail.com

Abstract-In this paper concept of implementation of the soft

computing approach is introduced for the environment friendly

steel hardening processing unit, i.e. for Plasma Ion Nitriding

(PIN) System. In Plasma Ion Nitriding system, a proper and

efficient mechanism is required to meet the problem of finding

process parameters involved in the surface hardening treatment

of different types of steel. The algorithmic model proposed here

is meant to facilitate the prediction of values of the process

parameters required to be identified for the surface hardening of

the steel in the PIN system. Here, an effort has been made to

provide an effective and efficient algorithmic solution model for

the soft computing based Plasma Ion Nitriding system.

Keywords: Material Engineering; Case Based Reasoning (CBR;, Soft

Plasma Ion Nitriding (SPIN) System; K-Nearest Neighbor (KNN)

Algorithm

I INTRODUCTION

Almost all major engineering arenas are benefited with the

advancements in the research involving the merger of soft

computing technique with them. Here our proposal

amalgamates the Knowledge-Based Reasoning technique with

the material engineering to facilitate the development of an

efficient model for Plasma Ion Nitriding system.

Plasma Nitriding System is an industrial surface hardening

system for metals. This system involves the Plasma Nitriding

process, thus also known as Ion Nitriding or Plasma Ion

Nitriding (PIN) System. In Plasma Ion Nitriding process,

nitrogen is introduced onto the surface of steel while

maintaining the temperature range of 5000 to 5700 oC in the

furnace of the unit. In vacuum, high-voltage electrical energy

is supplied in the furnace to form plasma, providing the

nitrogen ions being accelerated to intrude on the target steel

workpiece, resulting the heating and cleaning of the steel

workpiece with the release of the active nitrogen to make iron-

nitride compounds which then diffuses to the target area to

harden the surface up to the required level [1]

This hardening process requires the maintenance of the

controlled and specific temperature and the ratio of the gases

(Nitrogen and Hydrogen), within the furnace for a fixed

duration of time to achieve the target surface hardness of the

steel workpiece. For a number of different types of steel

materials, the values of the process parameters, temperature,

time and gas composition are already set up through a number

of hit and trial methods, experiments and high cost processes

and thus acting as a significant data base for the future use.

Our problem arises in case, when a new steel material for

which the values of process parameters are not available in our

database, is sent from the industry to the Plasma Ion Nitriding

unit and a particular hardness value with some specified case

depth is demanded by the Industry, then a hit and trial kind of

existing practice being in use till today, is time consuming,

difficult and an expensive solution.

Here we have proposed the unification of the Material

Engineering and Computer Engineering for the development

of an efficient as well as an effective mechanism for the

Plasma Ion Nitriding system.

The solution introduced here mainly involves the modeling

approach based on the Case-Based Reasoning (CBR). The

CBR is the computer-based reasoning that could be used to

solve new problems based on the explications of the similar

past problems [2]. At the greatest level of generality, a simple

CBR framework can be explained by Retrieve, Reuse, Revise

and Retain processes. Here, we have to retrieve the most

similar cases, reuse the knowledge and information available

with those cases, revise the proposed solution as per the

problem case and retain the resultant solution and information

as a new case with the set of previously existing cases in

memory [2]. Here, we do have the information to be

considered in the form of cases as per the conceptual

relevancy of the problem with the concept of Case Based

Reasoning [3].

A java language based simulator – SPIN (Soft PIN) system is

developed to predict the process parameters involved in the

surface hardening treatment process of steel and to evaluate

the performance of the proposed algorithm

II THE PROPOSED SPIN MODEL

A. Conceptual Model for SPIN System

Soft PIN System is designed on the basis of guidance obtained

from CBR approach. It involves the prediction of the process

parameters with the help of the CBR solution steps i.e.

Retrieve, Reuse, Revise and Retain. Considering these

outlined steps of CBR as the base for our conceptual model,

the basic procedure for the development of the proposed

model is as follows:

Step1. Trace and collect the relevant data to be considered as

cases with the following details:

- Name of the different steel for which process parameter

values are already found

- Value of percentage composition of all the alloying

elements present in those steels

- Value of surface hardness and case depth of the steels

decided by the industry

- Values of the process parameters viz. temperature, time

and gas composition (Nitrogen: Hydrogen) to be

maintained in the furnace to achieve the desired values of

surface hardness and case depth of the steels.

This data will provide the base for the prediction of the new

cases i.e. the prediction of the process parameters for the new

steels with different percentage of alloying of elements in

them.

Step2. Match or map the composition percentage of the

elements of the already existing cases of steel with the new

steel’s composition percentage of the elements.

Step3. Compare the results and if deviation is unacceptable

then revise the solution with the help of K-Nearest Neighbor

Algorithm[3], [4] and then by fuzzy k-Nearest Neighbor

Approach.

Step4. If the resultant solution is found satisfactory, store the

result in memory as the final solution and as a new case for

the future reference.

B. Computing Model for SPIN System

The proposed algorithmic model for the Soft PIN System is

explained as follows:

Here,

- A CASE consists of a problem, its solution, and an

explanation about how the solution was derived.

- ALL_CASES[][] is the set of all the steels with all the

parameters in percentage, i.e., surface hardness, case

depth, temp, time, gas composition that are relevant to

our target problem case i.e. the base_case.

- BASE_CASE is the steel case or cases which would

help in finding the solution for the target problem case

for which the process parameter values are to be

predicted.

- NEW_CASE is the steel with all the values along with

the obtained process parameter values and to be kept as a

new case for the future reference in the database.

//BEGIN OF THE MAIN ALGORITHM

Step1.RETRIEVE:

Step1.1: Retrieve ALL_CASE[][] values

Step1.2: Read NEW_CASE values

Step2.REUSE - // Mapping of all_cases with the base_case

Step2.1:RESULT_COMPARE_NAME=compare_name

(NEW_CASE_NAME,ALL_CASES_NAMES)

Step2.2:MATCHED_NAME_CASE=steel case which has

same name as of the new case

If (RESULT_COMPARE_NAME != NULL)

Then

BASE_CASE=MATCHED_NAME_CASE

Go to step 2.3

Else

Go to Step 3.1

Step2.3:RESULT=compare_percent_components

(BASE_CASE, NEW_CASE)

If (RESULT==0)

Then

TEMP_NEW_CASE=TEMP_BASE_CASE

TIME_NEW_CASE=TIME_BASE_CASE

GASCOMPOSITION_NEW_CASE=

GASCOMPOSITION_BASE_CASE

Go to Step 4.1

Else

Go to step 3.2

Step3.REVISE/ADAPTATION - This involves adapting the

solution as needed to fit the new case

Step3.1://prediction of parametric values- i.e.

revise/adaptation from ALL_CASES

PREDICTED_PARAMETERS1[]=

KNN_ALL_CASES(ALL_CASE[][],NEW_CASE)

TEMP_PREDICTED=PREDICTED_PARAMETERS1[0]

TIME_PREDICTED=PREDICTED PARAMETERS1[1]

GASCOMPOSITION_PREDICTED=PREDICTED_PARAM

ETERS1[2]

Go To Step 3.3

Step3.2: //prediction of parametric values - i.e. Revision or

adaptation from the BASE_CASE

PREDICTED_PARAMETERS2[]=

MATCH_BASE_CASE(BASE_CASE,NEW_CASE)

TEMP_PREDICTED=PREDICTED_PARAMETERS2[0]

TIME_PREDICTED=PREDICTED_PARAMETERS2[1]

GASCOMPOSITION_PREDICTED=PREDICTED_PARAM

ETERS2[2]

Step3.3:

TEMP_NEW_CASE=TEMP_PREDICTED

TIME_NEW_CASE=TIME_PREDICTED

GASCOMPOSITION_NEW_CASE=

GASCOMPOSITION_ PREDICTED

Go To Step 4.2

Step4. RETAIN

Step4.1: Do Not Store The NEW_CASE As a New Case

Go To Step 4.3

Step4.2: Store the NEW_CASE As a New Case

Step4.3: Print The Values Of The Hardening Parameters-

TEMP_NEW_CASE,

TIME_NEW_CASE,

GASCOMPOSITION_NEW_CASE

Step4.4: End

//END OF THE MAIN ALGORITHM

ALGORITHM FOR MATCH_BASE_CASE()

Step1.//call to compare_components_newcase_basecase()

NO_OF_COMPONENTS_MATCHED=COMPARE_COMP

ONENTS_NEWCASE_BASECASE()

PERCENT_MATCH=(NO_OF_COMPONENTS_MATCHE

D/TOTAL_NO_OF_COMPONENTS)*100

If (PERCENT_MATCH <= 40)

then

PREDICTED_PARAMETERS[]=KNN_ALL_CASES(BASE

_CASE, NEW_CASE)

Else if (PERCENT_MATCH <= 90)

then

//call to KNN algorithm for prediction of parameters

PREDICTED_PARAMETERS[]=KNN_BASE_CASE(BASE

_CASE, NEW_CASE)

Else

PREDICTED_PARAMETERS[]=BASE_CASE_PARAMET

ERS[]

Step 2. Return (PREDICTED_PARAMETERS[])

III RESULT AND PERFORMANCE ANALYSIS

With the help of a set of well known steel varieties along with

the detailed attributes and the relevant parametric values,

cases are formulated to act as the base for predicting

parameter values for new steel. These cases include the details

of percentage compositions of the metals and the process

parameter values. The new case of steel is matched on the

basis of its composition and process parameter values and

compared with the existing cases. Nearest Neighbor

Algorithm an then fuzzy Nearest Neighbor Approach is

applied to retrieve the most nearest and similar case. The

parametric values of the closest steel are taken into

consideration. The Euclidean distance method is used to find

the difference between the old and new cases.

All known steel cases were taken as the cases to test the

system. Say, steel case 1040 was taken as the test case and the

process parameter values calculated through the SPIN System

are 520-547 0C (temperature), 16-18 hrs (time), 25:55 (N2:H2

gas composition), for 500-700 HV (surface hardness) and 0.3-

0.6 mm (case depth) [5]. The proposed model after

development has shown the considerable results in the

estimations of process parameters. For steel 1040 the results

with the correctness of approximately 80% are obtained. The

results found from old cases are the source of motivation to

implement and test the results in the real environment for new

steel cases.

IV CONCLUSION AND FUTURE SCOPE

Since the Knowledge-Based Reasoning technique of Case-

Based Reasoning has been proved as a competent mechanism

to provide good solutions for the problem of finding values

through the previously existing set of data and experimented

knowledge base facts, our model is also expected to be a

promising solution to meet out the solution of the problem as

well as a number of similar kind of problems.

REFERENCES

[1] Luiz Carlos Casteletti, G.E. Totten, Amadeu Lombardi Neto,

“Plasma Nitriding of Stainless Steels”, Article, Ionic Technologies Inc.,Article , March 5, 2008.

[2] R. Bergmann, K. Althoff et. al, “Developing Industrial Case-Based

Reasoning Applications”, the INRECA Methodology, Springer, Volume II. Pg 19-94.

[3] Yue-pok Mack, K-Nearest neighbor estimation, University of

California, 1978, pg 1-11. [4] Richard O. Duda, Peter E. Hart, David G. Stork, Pattern

Classification, 2 nd Edition, Wiley, 2001, pg 183-210

[5] O.P.Rishi, M. Sharma, R. Prakash, “Parameter Prediction through Soft PIN System in a Plasma Ion Nitriding Steel Hardening Unit”,

International Journal of Computer Applications Volume 49, July

2012. Published by Foundation of Computer Science, New York, USA.