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2 Journal of Achievements in Materials and Manufacturing Engineering

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Development of key performance selection index model

Kaganski S., Toompalu S.

Journal of Achievements in Materials and Manufacturing Engineering

2017

Vol. 82, nr 1

33--40

Purpose: The main idea of this paper is to introduce the refined model for selection of the Key performance indicators (KPI). The KPI selection model can be considered as a tool for analysis of the enterprise, which should be able to simplify the choice of the right metrics for the company, where study has been conducted. The Enterprise analysis model (EAM) will provide the information regarding weak spots on the production and provide further steps to the management. Those actions will save time and reduce resources that are necessary to implement metrics in company. Design/methodology/approach: Main activities performed include: optimization of EAM; Fuzzy AHP and SMARTER criteria’s for ranking the KPIs; reliability analysis and weights appointment to questions and KPIs. In addition, the expert group has participated in the analysis of this work and has made a high impact on the results. Findings: The main result of this work is the final version of the KPI selection model. Research limitations/implications: The future research should be focused on optimization of the model and in adding additional module for automatic data collection. The Production Monitoring System (PMS) that should help to collect data about the status of the machine park, taking into account the downtime, overall equipment efficiency (OEE) and etc. Practical implications: The proposed model can be used in SME (small and medium enterprises) in order to improve the productivity. The concept was tested in particular company. Originality/value: The KPI selection model combine different methodologies into one general approach. Due to this fact, the process of finding right metrics can be reduced significantly. The proposed approach allows saving resources for the research of metrics.

Material parameters identification by use of hybrid GA

Majak J., Toompalu S., Pohlak M.

Journal of Achievements in Materials and Manufacturing Engineering

2008

Vol. 27, nr 1

63-66

Purpose: of this paper is to develop material parameters identification algorithm for yield criterion BBC2003 using global optimization techniques. Design/methodology/approach: An algorithm proposed is based on use of error minimization function, which allows considering over-constraining. Due to strong nonlinearity of the problem considered a number of solutions is available. In order to determine global extreme two stage GA (global optimization technique) is treated. Findings: Numerical material parameters identification algorithm is developed. An approach provided allows reducing significantly the dimension of the nonlinear system before its numerical solution. Convergence to global extreme can be expected due to global optimization technique employed. Research limitations/implications: An analysis is done by keeping formability analysis in mind and only material parameters involved in yield criterion in space of principal stresses are considered. Thus the results can be generalized by including terms corresponding to shear stresses. Practical implications: Advanced yield criteria like BBC2003 are still not used extensively due to the complexities accrued: increasing number of material parameters (additional tests), a complex non-linear programming problem. An algorithm proposed simplifies the material parameters identification process for considered yield criteria BBC2003. The formability analysis of the 6000 series aluminium alloy sheet AA6181-T4 is considered as a case study and used for testing the algorithm proposed. Originality/value: In the case of posed optimization problem the dimension of the design space is reduced from six to two. Over-constraining and under-constraining are considered in algorithm (situations, where number of unknown parameters is not equal with the number of given constraints, are covered).