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  • 1 Department of Mechatronics, Faculty of Engineering, University of Debrecen, Ótemető u. 2-4, H-4028 Debrecen, Hungary
  • 2 Institute of Industrial Process Management, Faculty of Engineering, University of Debrecen, Ótemető u. 2-4, Hungary
Open access

Abstract:

The aim of the research is to make a comparison between system integrated measurement technologies in the field of engineering education in order to the students getting more detailed knowledge about the high level problem solving. A comparative case study was conducted with 3 different types of systems, as follows: Beckhoff, National Instruments, and HBM. The criteria of the systems are determined based on experience and the importance level of them was calculated by preference matrix. The ranks of the alternatives are calculated by Kesselring method, which provides the effectiveness value of the systems compared to the benchmark. The result of the paper shows a suitable method for selecting engineering systems.

Abstract:

The aim of the research is to make a comparison between system integrated measurement technologies in the field of engineering education in order to the students getting more detailed knowledge about the high level problem solving. A comparative case study was conducted with 3 different types of systems, as follows: Beckhoff, National Instruments, and HBM. The criteria of the systems are determined based on experience and the importance level of them was calculated by preference matrix. The ranks of the alternatives are calculated by Kesselring method, which provides the effectiveness value of the systems compared to the benchmark. The result of the paper shows a suitable method for selecting engineering systems.

1 Introduction

Today, information is becoming increasingly important in the accelerated world. A great deal of information is available but unfortunately it is not a high standard. It makes a difference what information is available at what time. This kind of advanced intensive information might serve the development of technology. It does not matter what area of life is given as an example, that of a dentist’s, a cinema show, a writer’s year of birth, the current state of the ordered package.

All the important information emerges from a lot of data collection and data processing [1], so it is very important to know from the beginning what tools and methods can be used to extract information. The intensive collection of information in industry is a major challenge, since the quantity and quality of information affects the product be manufactured. It is very important to know who, when and by what means, what tools, built the device in what way. These data are essential for future developments, or even a possible investigation of complaints. That is why it is fundamental part of the education that the students get up to date knowledge in field of measuring systems. Therefore, it is essential to be able to clearly compare desired industrial measuring systems for the production processes [2].

It is possible to compare different aspects/criteria systems with many types of decision making methods.

2 Selected industrial measuring systems

The article introduces classical industrial measurement technology solutions. The basis of the comparison (the smaller one) is provided by the systems applied at the Faculty of Engineering, the University of Debrecen. The article compares the different industrial measuring systems of three different manufacturers without completeness.

2.1 HBM

HBM is the market leader in the test and measurement technology and offers products and services for an extensive range of measurement applications in many industries.

The potential fields of application can be found in every branch of engineering and industry in both virtual and physical test and measurement.

HBM’s product range covers strain gauges, load cells, force sensors, torque sensors, amplifiers and Data Acquisition Systems (DAQ) as well as software for structural durability investigations, tests and analysis.

In the HBM example the central pressure head and three displacement signals are measured (Fig. 1a is the signal amplifier, Fig. 1b is the displacement sensor Fig. 1c is the testing machine) how much the material rises at its two edges.

Fig. 1.
Fig. 1.

Physical devices of the HBM measuring system

Citation: Pollack Periodica Pollack Periodica 15, 2; 10.1556/606.2020.15.2.6

The signals are provided by the force cell and the signal transducer sensors are evaluated using catmanEasy software (Fig. 2). It can parameterize the received signals in catmanEasy software. The resulting values can be monitored continuously. It is possible to export the signals, collected by DAQ in various formats. The catmanEasy software is not suitable for direct machine control.

Fig. 2.
Fig. 2.

CatmanEasy measuring software of the HBM

Citation: Pollack Periodica Pollack Periodica 15, 2; 10.1556/606.2020.15.2.6

2.2 National Instruments

For more than 40 years, National Instruments (NI) has been developing high-performance automated test and automated measurement systems, which help to solve engineering challenges now and well into the future. It is directly present in more than 50 countries. NI prepares engineers and scientists with systems, which accelerate productivity, innovation and discovery.

The main products of NI are the PC-based measurement and control systems, CompactRIO systems, PXI systems, software (for data collection, control, electronic tests, electronic instruments, wireless design and testing) LabVIEW, DIAdem.

An intelligent family house model has been implemented with a National Instruments device. Control and measurement tasks have been implemented (e.g. heating, cooling, access to garage door, irrigation, external as well as internal temperature). The model also provides remote access (Fig. 3) [3].

Fig. 3.
Fig. 3.

Physical devices of the NI measuring system

Citation: Pollack Periodica Pollack Periodica 15, 2; 10.1556/606.2020.15.2.6

2.3 Beckhoff

Since the foundation of the company in 1980, continuous development of innovative products and solutions using PC-based control technology has been the basis for the continued success of Beckhoff. EtherCAT, the real-time Ethernet solution, makes forward-looking, high-performance technology available for a new generation of cutting-edge control concepts.

The company’s main products are Industrial PC, field I/O, servo drives, servo motors and system software.

Simple analogue measurement results were implemented with the Beckhoff device. The measured value is displayed from 0 to 10 V input signal. The flashing command part starts with the digital input (Fig. 4c). Industrial PC was used for the task solution (Fig 4a). These can be seen in the following Fig. 4.

Fig. 4.
Fig. 4.

Physical devices of the Beckhoff measuring system

Citation: Pollack Periodica Pollack Periodica 15, 2; 10.1556/606.2020.15.2.6

3 Main goals

Based on the diversity of excellence between the three manufacturers it seems rather difficult to make comparisons between them. However, owing to the combination of Kesselring and multi-criteria decision making methods clear evidence arises as how to qualify different systems in a measurable way.

The primary goal of the presented method is to apply any technical systems for a standard approach to diverse systems. This might balance out the incongruence of difference systems.

4 Multi-criteria decision making

Multi-Criteria Decision Making (MCDM) analysis is a rapidly growing aspect of operations research and management science.

A decision matrix A is an (M × N) matrix in which element aij indicates the performance of alternative Ai when it is evaluated in terms of decision criterion Cj, (for i=1,2,3,…,M, and j=1,2,3,…,N). It is also assumed that the decision maker has determined the weights of relative performance of the decision criteria (denoted as Wj, for j=1,2,3,…,N).

For example:

Let A = {Ai, for i = 1,2,3,…,M} be a (finite) set of decision alternatives and G = {gi, for j = 1,2,3,…,N} a (finite) set of goals according to which the desirability of an action is judged. Determine the optimal alternative A* with the highest degree of desirability with respect to all relevant goals gi:

  1. 1)Determining the relevant criteria and alternatives;
  2. 2)Attaching numerical measures to the relative importance of the criteria and to the impacts of the alternatives on these criteria;
  3. 3)Processing the numerical values to determine a ranking of each alternative (Table I) [4], [5].

Table I.

A decision matrix

Criteria
C1C2C3CN
Alt.W1W2W3WN
A1a11a12a13a1N
A2a21a22a23a2N
A3a31a32a33a3N
AMaM1aM2aM3aMN

4.1 Kesselring method

The method of system comparison was developed by Fritz Kesselring. This method was used for technical factors assessment that can be calculated by means of a ratio or interval factors. Kesselring developed a simple but very effective decision support method for the design process. Kesselring compared the data of products under investigation with the data of best product of a set ideal value. These data were the highest and got a score of 4 [6], [7]. The value of the parameter is determined on the scale of 0-5 with the actual value of product with comparison to the ideal value. It is explained as:

  • 5 point - Excellent;
  • 4 point - Very Good;
  • 3 point - Good;
  • 2 point - Satisfying;
  • 1 point - Acceptable;
  • 0 point - Insufficient.

After collection of data, the Kesselring method is used to calculate the technical value of complex systems as:

x=i=1npipmax=p¯pmax,
where x is the technical value of product; pi is the point value of parameters; is the arithmetic mean; pmax is the point value of ideal solution; n is the number of technical parameters.

Each parameter has different units. Kesselring formed a sequence of scale with measurements with a common denominator. The disadvantage of this method is that it does not take into account the different weights of parameters. It was solved by the Kesselring weighing method. vi stands for weighing factor of parameter were coded on the factor 0-10. The technical values of products were calculated with the weight factor of parameter as the follow:

x'=pi×vipmax×vi

Here, x’ can be up to 1 for complex system value. The Kesselring method is also used for the relative and absolute ranking of products. The system value is measured as:

  • 1 ≥ x’ ≥ 0.8 = system is very good;
  • 0.8 > x’ ≥ 0.6 = system is good;
  • 0.6 > x’ ≥ 0.5 = system is appropriate;
  • x’ < 0.5 = system is unsatisfactory.

The Kesselring method was originally used to measure machine tools; however, it can also be used for a complex system. In order to be effective, this method was designed to operate on evaluation factors that can be measured on the scale of ratio and intervals.

For the matching of procedures, the steps are as follows:

  1. 1.Choose an alternative;
  2. 2.Select evaluation factors;
  3. 3.Define the target function. (e.g. minimum for better smaller values, maximum for higher value function);
  4. 4.Specify the value of rating factor based on scale;
  5. 5.Specify the weight of rating factor. (for example: pair-based comparison or preference based comparison) [8], [9], [10].

5 Application of the methods

The three manufacturer’s measuring systems have been compared with measurement methodology of complex systems. The main goal is to quantify the efficiency of each measuring system based on the determined parameters shown in Table II.

Table II.

Defined minimum and maximum target functions

No.Name of the criteriaTarget function
E1Price of the measurement systemMin.
E2Applicability for industrial processesMax
E3Simplicity of programmingMax.
E4User friendlinessMax
E5Data collection for reportsMin
E6Easy evaluation of dataMin
E7SizeMin
E8Sensor compatibilityMax.
E9DocumentednessMax.
E10SupportMax
E11Delivery timeMax.
E12Professional pre-qualificationMin
E13IT requirementsMin
E14Compatibility with softwaresMax.
E15ModularityMax.
E16RobustnessMax
E17Price of the softwaresMin.

The methods applied as the follows as it can be seen in Fig 5:

  • Selection of alternatives;
  • Definition of criteria;
  • Preferential matrix for determining the priority of criteria;
  • Specification of target functions for criteria;
  • Scoring of values-criterion for all alternatives;
  • Kesselring method for examining system efficiency.

Fig. 5.
Fig. 5.

Steps of the methods used

Citation: Pollack Periodica Pollack Periodica 15, 2; 10.1556/606.2020.15.2.6

The order priority of the preference matrix was determined on the basis of the chosen criteria (relationship of criteria).

  1. 1.The comparison is based on the 17 criteria (aspects) as it can be seen in Table III. These criteria are the most important for selecting a measurement system. The effectiveness of measurements system is determined the value of the criteria.
  2. 2.Best value criteria have been considered;
  3. 3.The low level of inconsistency of a pair wise comparison is a necessary condition to generate the acceptable result. The Consistency Ratio (CR) is based on the fact that the dominant eigenvalue of a consistent pair wise comparison matrix is N [11]. Basically consistency ration is a positive linear transformation of the Perron eigenvalue λmax as follows: CR = CI/CR, where CI stand for consistency index, CI = (λmax - n)/(n - 1). RI stands for random index. Consistency ration is zero if and only if the pair wise comparison is consistent otherwise CR is a positive value. The threshold values of 0.1 (10%) has been accepted in the practice [12]. The following table contains the value of consistency analysis.

Table III.

Priority matrix determination

E1E2E3E4E5E6E7E8E9E10E11E12E13E14E15E16E17
E111/61/321/31/31/21/91/942651/71/531/9
E261864132222226385
E331/8111/51/553228321638
E41/21/6112252329762256
E531/451/21665349696297
E63151/21/6197659966699
E721/31/51/51/61/911/91/61/331/51/31/31/341/4
E891/21/31/21/51/791969751448
E991/21/21/31/31/661/9153381/61/659
E101/41/21/21/21/41/531/61/519971/61/335
E111/21/21/81/91/91/91/31/91/31/911/91/91/91/91/91/9
E121/61/21/31/71/61/951/71/31/99191/91/359
E131/51/21/21/61/91/631/51/81/791/911/91/51/62
E1471/611/21/61/631669991996
E1551/31/61/21/21/631/4639351/9191/9
E161/31/81/31/51/91/91/41/41/51/391/561/91/916
E1791/51/81/61/71/941/81/91/591/91/21/691/61

The calculated CR value is 0.073, that value can be accepted and the consistency is assumed in respect that there are 17 parameters in the calculation.

  1. 4.The manufacturers rating has been calculated based on the weighted scores (1-5) (subjective comparison). The results can be seen in Table IV;
  2. 5.Weighted scores of measuring systems (summary); all three measurement system were well done based on the criteria set up (Fig. 6). The rating scores for mean scores are significantly affected by the following weighted points: E5Data collection for reports; E6 - Data evaluation; E3 - Difficulty of programming; E8 - Sensor compatibility; E4 - User friendliness [11], [12], [13], [14], [15], [16].

Table IV.

Rating of measure systems

HBMValueNIValueBeckhoffValue
Min.E1moderate3expensive1moderate5
MaxE2moderate2moderate3moderate5
MaxE3moderate3easy54
MaxE4moderate3high4moderate3
Min.E5easy5easy5moderate4
Min.E6easy5easy5moderate3
Min.E7moderate2moderate3moderate2
MaxE8high4high5moderate3
MaxE9low2moderate3moderate5
.MaxE10moderate2moderate3high5
Min.E11moderate3moderate1high5
.Min.E12moderate3slow2fast4
Min.E13low5moderate1low3
MaxE14moderate3high1moderate5
MaxE15moderate3low3high5
MaxE16moderate3moderate5high2
Min.E17required3essential1Low5
Average3.183.004.00
Fig. 6.
Fig. 6.

Rating of measure systems

Citation: Pollack Periodica Pollack Periodica 15, 2; 10.1556/606.2020.15.2.6

6 Results

As it is shown in Fig. 6, all three measuring systems achieved similar scores. Based on weighted scores (x'), the manufacturers achieved the following scores, HBM: 0.677, NI: 0.702 and Beckhoff: 0.812.

This means that all manufacturer’s systems have received a good rating as described in paragraph 3.1 (0.8 > x’ ≥ 0.6 = system is good, x’>0.8= system excellent).

7 Conclusion

Based on subjective and objective factors, the Beckhoff's industrial measuring systems are ahead of the above-mentioned competitors. The rating obtained is further corroborated by the Beckhoff company fact that the price of the measuring instruments and the programming software is absolutely free.

The method can be used as a basis for a customer satisfaction measurement, which can be the basis for future product development.

Acknowledgements

This work was supported by EFOP-3.6.1-16-2016-00022 ’Debrecen Venture Catapult program’. The project was supported by the European Union, co-financed by the European Social Fund.

References

  • [1]

    Laird L. M., Brennan M. C. Software measurement and estimation, A practical approach, Wiley, 2006.

  • [2]

    Ábrahám I. Decision theory methods, (in Hungarian) Typotex Kiadó, Budapest, 2013.

  • [3]

    Kovács B., Tóth J. Homes of the future, Annals of University of Oradea, Fascicle of Management and Technological Engineering, Vol. 17, No. 27, 2018, pp. 141144.

    • Export Citation
  • [4]

    Triantaphyllou E., Shu B., Sanchez S. N., Ray T. Multi-criteria decision making: An operations research approach, in: Encyclopedia of Electrical and Electronics Engineering, J. G. Webster (Ed.), Wiley, Vol. 15, 1998, pp. 175186.

    • Search Google Scholar
    • Export Citation
  • [5]

    Triantaphyllou E. Multi-criteria decision making methods, A comparative study, Springer, 2000.

  • [6]

    Kindler J., Papp O. Comparison of complex systems, (in Hungarian), Műszaki Könyvkiadó, 1977.

  • [7]

    Monahan G. E. Management decision making, Spreadsheet modeling, analysis, and application, Cambridge University Press, 2000.

  • [8]

    Winston W. L. Operations research: Applications and algorithms (with CD-ROM and InfoTrac), Duxbury Press, Boston, 2004.

  • [9]

    Harrington J. E. Games, strategies and decision making, Worth Publishing, 2009.

  • [10]

    Adams J., Juleff L. Managerial economics for decision making, Palgrave, 2003.

  • [11]

    Saaty T. L. The analytic hierarchy process, McGraw Hill, New York, 1980.

  • [12]

    Murphy C. K. Limits on the analytic hierarchy process from its inconsistency index, European Journal of Operational Research, Vol. 65, No.1, 1993, pp. 138139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [13]

    Menyhárt J., Szabolcsi S. Support vector machine and fuzzy logic, Acta Polytechnica Hungarica, Vol. 13, No.5, 2016, pp. 205220.

  • [14]

    Pusztai L., Kocsi B., Budai I. Business process development with the application of simulation technique, International Journal of Engineering and Management Sciences, Vol. 2, No.3, 2017, pp. 109118.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [15]

    Achs Á. Vague information in logical databases, Pollack Periodica, Vol. 3, No. 1, 2008, pp. 2940.

  • [16]

    Pusztai L., Kocsi B., Budai I. Making engineering projects more thoughtful with the use of fuzzy value-based project planning, Pollack Periodica, Vol. 14, No. 1, 2019, pp. 2534.

    • Crossref
    • Search Google Scholar
    • Export Citation

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1]

    Laird L. M., Brennan M. C. Software measurement and estimation, A practical approach, Wiley, 2006.

  • [2]

    Ábrahám I. Decision theory methods, (in Hungarian) Typotex Kiadó, Budapest, 2013.

  • [3]

    Kovács B., Tóth J. Homes of the future, Annals of University of Oradea, Fascicle of Management and Technological Engineering, Vol. 17, No. 27, 2018, pp. 141144.

    • Export Citation
  • [4]

    Triantaphyllou E., Shu B., Sanchez S. N., Ray T. Multi-criteria decision making: An operations research approach, in: Encyclopedia of Electrical and Electronics Engineering, J. G. Webster (Ed.), Wiley, Vol. 15, 1998, pp. 175186.

    • Search Google Scholar
    • Export Citation
  • [5]

    Triantaphyllou E. Multi-criteria decision making methods, A comparative study, Springer, 2000.

  • [6]

    Kindler J., Papp O. Comparison of complex systems, (in Hungarian), Műszaki Könyvkiadó, 1977.

  • [7]

    Monahan G. E. Management decision making, Spreadsheet modeling, analysis, and application, Cambridge University Press, 2000.

  • [8]

    Winston W. L. Operations research: Applications and algorithms (with CD-ROM and InfoTrac), Duxbury Press, Boston, 2004.

  • [9]

    Harrington J. E. Games, strategies and decision making, Worth Publishing, 2009.

  • [10]

    Adams J., Juleff L. Managerial economics for decision making, Palgrave, 2003.

  • [11]

    Saaty T. L. The analytic hierarchy process, McGraw Hill, New York, 1980.

  • [12]

    Murphy C. K. Limits on the analytic hierarchy process from its inconsistency index, European Journal of Operational Research, Vol. 65, No.1, 1993, pp. 138139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [13]

    Menyhárt J., Szabolcsi S. Support vector machine and fuzzy logic, Acta Polytechnica Hungarica, Vol. 13, No.5, 2016, pp. 205220.

  • [14]

    Pusztai L., Kocsi B., Budai I. Business process development with the application of simulation technique, International Journal of Engineering and Management Sciences, Vol. 2, No.3, 2017, pp. 109118.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [15]

    Achs Á. Vague information in logical databases, Pollack Periodica, Vol. 3, No. 1, 2008, pp. 2940.

  • [16]

    Pusztai L., Kocsi B., Budai I. Making engineering projects more thoughtful with the use of fuzzy value-based project planning, Pollack Periodica, Vol. 14, No. 1, 2019, pp. 2534.

    • Crossref
    • Search Google Scholar
    • Export Citation

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