Open access peer-reviewed chapter

The Reliability Design and Its Direct Effect on the Energy Efficiency

By Seong-woo Woo, Jungwan Park, Jongyun Yoon and HongGyu Jeon

Submitted: January 19th 2012Reviewed: April 27th 2012Published: October 17th 2012

DOI: 10.5772/48790

Downloaded: 1559

1. Introduction

Reliability refersto the ability of system or component to performa required function under stated environmental andoperational conditions for a specified period of time. Traditionally,the reliabilityover the product life can be illustrated by a bathtub curve that has three regions: a decreasingrate of failure, a constant rate of failure, and an increasing rate of failure, as shown inFigure1(a). As the reliability of a product (or part) improves, failure of the part becomes less frequent in the field. The bathtubcurve may change into a straight line with the slope angle β. In a straight line there are two variables to be measured: product life LB (or mean time between failures) and failure rate λ, as shown in Eq. (1):

R(LB)=eλLB1λLBE1

Figure 1.

System life and failure rate

We can thus establish the reliability growth plan of parts with a constant failure rate.

A company generally designs its new products to (1) minimize initial failures, (2) reduce random failures during the expected product working period, and (3) lengthen product life. Such aims are met through the use of robust design techniques, including statistical design of experiment (SDE) and the Taguchi methods [1]. The Taguchi methods describe the robustness of a system for evaluation and design improvement, which is also known as quality engineering [2-3] or robust engineering [4]. Robust design processes include concept design, parameter design, and tolerance design [5].Taguchi’s robust design methods place a design in anoptimum position where random “noise” does not cause failure, which then and helps in determining the proper design parameters [6].

However, for a simple mechanical structure, the Taguchi methods’ robust design processes need to consider a large number of design parameters.They also have difficulty in predicting the product life, LB (or MTBF).

In this study we present a new method for the reliability design of mechanical systems. This new method takes into account the fact that products with missing or improper design parameters can result in recalls and loss of brand name value.Based on the analysis of a failed refrigerator drawer and handle systems, we demonstrated our new reliability design method. The new method uses ALT; the new concept of product life,LB; and sample size, as a novel means of determining proper design parameters [7-14].

1.1. Targeting the refrigerator BX life and failure rate λ

The multi-unit refrigerator used as a case study for this method consists of a compressor, a drawer, a door, a cabinet, and other units. For the drawer, the B1life of the new design is targetedto be over10 years with a yearly failure rate of 0.1%. The entire refrigerator’s BX life can be obtained by summing up the failure rates of each refrigerator unit. The refrigerator’s B12 life with the new design is targetedto be over10 years with a yearlyfailure rate of 1.2 % (Table 1) [19].

NoUnitsMarket DataDesignConversionExpectedTargetBx LifeBased Bx
Failure RateBx LifeFailure RateBx Life
1Compressor0.345.3Newx51.700.1010B1.0
2Door0.355.1Givenx10.350.1510B1.5
3Cabinet0.254.8Modifiedx20.500.1010B1.0
4Drawer0.206.0Newx20.400.1010B1.0
5Heat exchanger0.158.0Givenx10.150.1010B1.0
6etc0.5012.0Givenx10.500.5010B6.0
SumR-Set1.797.4--3.601.1010B12.0

Table 1.

Total parametric ALT plan of refrigerator

1.2. Analysis. of the problemsidentified in field samples (loads analysis)

In the field, certain components in these refrigerators had been failing or making noise, causing consumers to replace their refrigerators. Data from the failed products in the field showed how common used the refrigerators under common usage conditions. Refrigerator reliability problems in the field occurwhen the parts cannot endure repetitive stressesdue to internal or external forces over a specified period oftime. The energy flow in a refrigerator (or other mechanical) systemcan generally be expressed as efforts and flows (Table 2)[15]. Thus, the stresses come from the efforts.

Refrigerator Units
(or Parts)
Effort, e(t)Flow, f(t)
Mechanical translation
(draws, dispenser lever)
Force component, F(t)Velocity component, V(t)
Mechanical rotation
(door, cooling fan)
Torque component, τ(t)Angular velocity component, V(t)
CompressorPressure difference, ΔP(t)Volume flow rate, Q(t)
Electric
(PCB, condenser)
Voltage, V(t)Current, i(t)

Table 2.

Effort and flow in the multi-port system

For a mechanical system, the time-to-failure approach employs a generalized lifemodel (LS model) [16], such as:

Tf=A(S)nexpEakT=A(e)nexpEakTE2

Repetitive stress can be expressed as the duty effectthat carries the on/off cycles and shortens part life [17]. Under accelerated stress conditions, the accelerationfactor (AF) can be described as:

AF=(SnS0)n[Eak(1T01T1)]=(e1e0)n[Eak(1T01T1)]E3

And n can be determined by multiple testings with different stress levels.

1.3. Parametric. ALT with BX life and sample size

Traditionally, the characteristic life is defined as:

ηβtiβrnhβrE4

As the reliability of a product (or part) improves,failures of the product become less frequent in laboratory tests. Thus, it becomes more difficult to evaluate the characteristic life using Equation (4).The distribution of failed samples should follow the Poisson distribution for small samples [18]. For a 60% confidence level, the characteristic life can be redefined as

ηβ1r+1nhβE5

In order to introducethe BX life in the Weibull distribution, the characteristic life can be modified as

LBβxηβ=xr+1nhβE6

where LB = BX life and x = 0.01X, on the condition thatx≤ 0.2.

BXis the time by which X% of the drawer and handle system installed in a particular population of refrigerators will have failed.In order to assess theBX life with about a 60% confidence level, the number of test samples is derived in Eq. (7). That is,

n1x(r+1)(1h*)βE7

with the condition that the durability target is defined as follows,

h*=(AFh)/LB1E8

Based on the customer usage conditions, the normal range ofoperating conditions and cycles of the product (or parts) are determined. Under the worst case, the objective number ofcycles and the number of required test cycles can be obtainedfrom Eq. (7). ALT equipment can then be conducted on thebasis of load analysis. Using ALT we can find the missing or improper parameters in the design phase.

1.4. Refrigerator unit LBx life and failure rate, λ, with the improved designs

The parameter design criterion of the newly designed samplescan be more than the target life of BX= 10 years. From the field data and from a sample under ALT with a corrective action plans, we can obtain the missing or improper parameters of parts and their levels in the design phase.

With the improved design parameters, we can derive theexpected LBx life of the final design samples using Equation (6).

LBβxn(hAF)βr+1E9

Let x = λ LB in Equation (9). The failure rate of the final design samples is derived in Equation (10)

λ1LB(r+1)LBβn(hAF)βE10

2. Case study: Reliability design of arefrigerator drawer and handle system

Figure2 shows a refrigerator with the newly designed drawer and handle system and its parts. In the field, the refrigerator drawer and handle system had been failing, causing consumers to replace their refrigerators (Figure 3). The specific causes of failures of the refrigerator drawers during operation wererepetitive stress and/or the consumer improper usage. Field data indicated that the damaged products had structural design flaws, including sharp corner angles and weak ribs that resulted in stress risers in high stress areas.

A consumer stores food in a refrigerator to have convenient access to fresh food. Putting food in the refrigerator drawer involves opening the drawer to store or takeout food, closing the drawer by force. Depending on the consumer usage conditions, the drawer and handle parts receive repetitive mechanical loads when the consumer opens and closes the drawer.

Figure 4 shows the functional design concept of the drawer and handle system. The stress due to the weight load of the food is concentrated on the handle and support slide rail of the drawer. Thus, the drawer must be designed to endure these repetitive stresses.

The force balance around the drawer and handle system cans be expressed as:

Fdraw=μWloadE11

Figure 2.

Refrigerator and drawer assembly. (a) French refrigerator (b) Mechanical parts of the drawer: handle (1), drawer (2), slide rail (3), and pocket box (4)

Figure 3.

A damaged product after use

Figure 4.

Functional design concept of the drawer and handle system

Because the stress of the drawer and handle system depends on the food weight, the life-stress model (LS model) can be modified as follows:

Tf=A(S)n=A(Fdraw)n=A(μWload)nE12

where A is constant. Thus, the acceleration factor (AF) can be derived as

AF=(S1S0)n=(F1F0)2=(μW1μW0)2=(W1W0)2E13

3. Laboratory experiments

The normal ranges of the operating conditions for the drawer system and handle were 0 to 50℃ ambient temperature, 0 to 85% relative humidity and 0.2 to 0.24G vibration. The normal number of operating cycles for one day was approximately 5; the worst case was 9. Under the worst case, the objective drawer open/close cycles for ten years would be 32,850 cycles (Table 3).

Item
Number of operations (times)
1 day10 years
NormalWorstNormalWorst
Drawer5918,25032,850

Table 3.

Operating number of a drawer

For the worst case, the food weight force on the handle of the drawer was 0.34 kN. The applied food weight force for the ALT was 0.68 kN. With a quotient, n, of 2, the total AFwas approximately 4.0 using equation (13).

The parameter design criterion of the newly designed drawer can be more than the target life of B1 = 10 years. Assuming the shape parameter β was 2.0 and xwas 0.01, the test cycles and test sample numbers calculated in Equation (7) were 67,000 cycles and 3units, respectively. The ALT was designed to ensure a B1 life of 10 years with about a 60% level of confidence that it would fail less than once during 67,000 cycles.

Figure 5.

ALT equipment and duty cycles.

Figure 5 shows ALT equipment and duty cycles for the repetitive food weight force, Fdraw. For the ALT experiments, the control panel on top of the testing equipment started and stopped the drawer during the mission cycles. The food load, F, was controlled by the accelerated weight load in the drawer storage. When a button on the control panel was pushed, mechanical arms and hands pushed and pulled the drawer.

4. Parametric ALTs with corrective action plans

Figures 6(a) and 6(b) show the failed product from the field and the 1st accelerated life testing, respectively. The failure sites in the field and the first ALT occurred at the drawer handle as a result of high concentrated stress. Figure 7 shows a graphical analysis of the ALT results and field data on a Weibull plot. For the shape parameter, the estimated value on the chart was 2.0. For the final design, the shape parameter was determined to be 3.1. These methodologies were valid for pinpointing the weak design responsible for failures in the field and 1st ALT.

Figure 6.

Failed products in field and first ALT; (a) Failed product in field and (b) Failed sample in first accelerated life testing

Figure 7.

Field data and results of 1stALT on Weibull chart.

Figure 8.

Failed slide rails in second ALT

The fracture of the drawer in the first and second ALTs occurred in the handle and slide rails (Figure 6(b) and Figure 8). The missing or improper parameters of the handle and slide rails in the design phase arelisted in Table 4. These design flaws can result in a fracture when the repetitive food load is applied.

CTQParametersUnit
FractureKNPN1Consumer food loadingkN
KCPC1Reinforced handle width-
C2Handle hooker width-
C3Fastening screw number-
C4Slide rail chambermm
C5Slide rail boss thicknessmm
C6New added rib-

Table 4.

Vital parameters based on ALTs

To prevent the fracture problem and release the repetitive stresses, the handle and slide rails were redesigned. The corrective action plan for the design parameters included: (1) increasing the width of the reinforced handle, C1, from 90mm to 122mm; (2) increasing the handle hooker size, C2, from 8mm to 19mm; (3) increasing the rail fastening screw number, C3, from 1 to 2; (4) adding an inner chamber and plastic material, C4, from HIPS to ABS; (5) thickening the boss, C5, from 2.0mm to 3.0mm; (6) adding a new support rib, C6 (Table 5).

The parameter design criterion of the newly designed samples was more than the target life, B1, of ten years. The confirmed value,β,on the Weibull chart was 3.1. For the second ALT, the recalculated test cycles and sample size in Equation (7) were 32,000 and 3units, respectively. In the third ALT, no problems were found with the drawerafter 32,000 cycles and 65,000 cycles. We therefore concluded that the modified design parameters were effective.

Table 6 provides a summary of the ALT results. Figure 9 shows the results of the 1stALT and 3rd ALT plotted in a Weibull chart. With the improved design parameters, the B1 life of the samples in the third ALT was lengthened tomore than 10.0 years.

HandleRight/left slide rail






C1: Width L90mm → L122mm (1st ALT)
C2: Width L8mm → L19mm (1st ALT)
C3: Rail fastening screw number 1→2 (2nd ALT)
C4: Chamfer: Corner chamfer
Plastic material HIPS → ABS (2nd ALT)
C5: Boss thickness 2.0 → 3.0 mm (2nd ALT)
C6: New support rib (2nd ALT)

Table 5.

Redesigned handle and right/left slide rail

Figure 9.

Results of 1stALT and 3rd ALT plotted in Weibull chart

First ALTSecond ALTThird ALT
Initial designFirst design iterationFinal design
In 32,000 cycles, Fracturing is less than 17,500 cycles: 2/3 Fail
12,000 cycles: 1/3 OK

λ = 26.6%/year
B1 = 3.4 year
16,000 cycles: 2/3 Fail

λ= 2.46%/year
B1 = 7.3 year
32,000 cycles: 3/3 OK
65,000 cycles: 3/3 OK

λ = 0.1%/year
B1 = 10 year
Drawer structure
Material & Spec.Width1: L90 →L122
Width2: L8→L19.0
Rib1: new support rib
boss: 2.0 → 3.0 mm
Chamfer1: Corner
Material: HIPS →ABS

Table 6.

Results of ALTs

6. Conclusions

We developed a new reliability design method based on a study of a defective refrigerator drawer and handle system that was failing under field use conditions. The failure modes and mechanisms for the drawer in the field and in the ALTs were identified. Important design parameterswere studied and improvements were evaluated using ALTs.

Based on the products returned from the field and the results of the firstALT, we found that the handles were fracturing because of design flaws. The handle design was corrected by increasing the handle width. During the second ALT, the slide rails fractured because they did not have enough strength to endure the repetitive food storage loads. The slide rails were corrected by providing additional reinforced ribs, reinforced boss, and an inner chamber. As a result these modified design parameters, there were no problems in the third ALT. We therefore concluded that the values for the design parameterswere effective to meet the life cycle requirements. The yearly failure rate and B1 life of the redesigned drawer and handle system, based on the results of ALT, were under 0.1% and more than 10 years, respectively. The study of the missing or improper design parameters in the design phase, through the inspection of failed products in the field, load analysis, and ALTs was very effective in redesigning more reliableparts with significantly longer life.

The casestudy focused on a mechanical structure consisting ofseveral parts subjected to repetitive stresses under consumer usage conditions. The same principles developed for the new reliability design methodology could be applied to other mechanical systems, including construction equipment, automobile gear trains and engines,forklifts, washing machines, vacuum cleaners, and motor fansystems. We recommend that the missing or improper controllable design parameters on these systemsalso be studied for reliability design. These parameter studies would also include failure analysis, load analysis, and a tailored series of accelerated life tests. Thesemethodologies could then predict part life quantitatively through accelerated factors and exact sample size.

7. Nomenclature

  1. AF - acceleration factor

  2. BX - durability index

  3. C1 - width of reinforced handle, mm

  4. C2 - width of handle hooker, mm

  5. C3 - back rib of slide rail

  6. C4 - screw boss height of slide rail, mm

  7. C5 - inner chamber of slide rail

  8. C6 - material of slide rail

  9. C7 - screw number of slide number

  10. e - effort

  11. e0 - effort under normal stress conditions

  12. e1 - effort under accelerated stress conditions

  13. Ea - activation energy

  14. f - flow

  15. F(t) - unreliability

  16. Fdraw - open/close force of the freezer drawer system, kN

  17. F1 - weight force under accelerated stress conditions, kN

  18. F0 - weight force under normal conditions, kN

  19. h - testing time (or cycles)

  20. h* - non-dimensional testing cycles, h*=h/LB1

  21. i - current, A

  22. k - Boltzmann’s constant, 8.62 x 10-5eV/deg

  23. KCP - Key Control Parameter

  24. KNP - Key Noise Parameter

  25. LB - the target BX life and x = 0.01X, on the condition thatx≤ 0.2

  26. n - the number of test samples

  27. N1 - consumer freezer door drawer open/close force, kN

  28. ΔP - pressure difference, MPa

  29. r - failed numbers

  30. R - reliability function

  31. S - stress

  32. S0 - mechanical stress under normal stress conditions

  33. S1 - mechanical stress under accelerated stress conditions

  34. ti - test time for each sample

  35. T - absolute temperature, K

  36. T1 - absolute temperature under accelerated stress conditions, K

  37. T0 - absolute temperature under normal stress conditions, K

  38. Tf - time to failure

  39. V - velocity, m/s

  40. V - voltage, volt

  41. W1 - food weight force under accelerated stress conditions, kN

  42. W0 - food weight force under normal stress conditions, kN

  43. Wload - total food weight force in the freezer door drawer, kN

  44. X - accumulated failure rate, %

  45. x - x = 0.01 X, on condition thatx≤ 0.2.

Greek symbols

  1. η - characteristic life

  2. λ - failure rate

  3. μ - friction coefficient

Superscripts

  1. β - shape parameter in a Weibull distribution

  2. n - stress dependence, n=[ln(Tf)ln(S)]T

Subscripts

  1. 0 - normal stress conditions

  2. 1 - accelerated stress conditions

© 2012 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Seong-woo Woo, Jungwan Park, Jongyun Yoon and HongGyu Jeon (October 17th 2012). The Reliability Design and Its Direct Effect on the Energy Efficiency, Energy Efficiency - The Innovative Ways for Smart Energy, the Future Towards Modern Utilities, Moustafa Eissa, IntechOpen, DOI: 10.5772/48790. Available from:

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