Multi-objective optimization algorithm for analysis of hardened steel turning manufacturing process

•Cost and tool life of a hardened steel turning of are properly characterized as typical poisson random variables.•Objective functions are simultaneously optimized coupled with their respective variances.•The proposed method reduces the dimension of the multi-objective optimization problem.•Proposed...

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Bibliographic Details
Published in:Applied Mathematical Modelling 2022-06, Vol.106, p.822-843
Main Authors: Amorim, Leandro Framil, de Paiva, Anderson Paulo, Balestrassi, Pedro Paulo, Ferreira, João Roberto
Format: Article
Language:English
Subjects:
Algorithms
Ceramic tools
Chromium molybdenum vanadium steels
Confidence Ellipse
Cutting speed
Decision making
Error functions
Factor analysis
Feed rate
Hot work tool steels
Inserts
Intersections
Manufacturing
Multi-objective optimization
Multiple objective analysis
Normal boundary intersection
Optimization algorithms
Pareto optimization
Response surface methodology
Tool life
Turning (machining)
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Summary:•Cost and tool life of a hardened steel turning of are properly characterized as typical poisson random variables.•Objective functions are simultaneously optimized coupled with their respective variances.•The proposed method reduces the dimension of the multi-objective optimization problem.•Proposed confidence ellipse for Pareto points supports a decision-making based on variability and mean shift.•A multivariate robust setup for steel turning process is obtained according to the fuzzy decision-maker. This paper presents a multi-objective optimization algorithm that combines Normal Boundary Intersection method with response surface models of equimax rotated factor scores in order to simultaneously optimize multiples sets of means and variances of manufacturing processes characteristics. The algorithm uses equimax factor rotation to separate means and variances in individual and uncorrelated functions and afterwards combines them in a mean squared error function. These functions are then optimized using Normal Boundary Intersection method generating a Pareto frontier. The optimal solutions found are then filtered according to a 95% non-overlapping confidence ellipses for the predicted values of the responses and posteriorly they are assessed by a Fuzzy decision-maker index established between the volume of each confidence ellipsoid and the Mahalanobis distance between each Pareto point and its individual optima for a given weight. In order to illustrate the practical implementation of this approach, two cases involving the multi-objective optimization of the hardened steel turning process were considered: (a) the AISI 52100 hardened steel turning with CC6050 mixed ceramic inserts and (b) the AISI H13 hardened steel turning with CC 670 mixed ceramic tools. For both cases, the best setup for cutting speed (V), feed rate (f) and depth of cut (d) were adjusted to find the minimal process cost (Kp) and the maximal tool life (T), both responses with minimal variance. The suitable results achieved in these case studies indicate that the proposal may be useful for similar manufacturing processes.
ISSN:0307-904X
1088-8691
0307-904X
DOI:10.1016/j.apm.2022.02.011