14. Tribological Design, Reliability and Asset Decisions

Introduction and Context

Tribological performance is rarely determined by friction or wear alone. In an industrial asset, a bearing, seal, gear mesh, hydraulic actuator, guideway, or cutting interface must perform its intended function for a required duration, under variable loads and environments, while remaining safe, maintainable, and economically justified. A technically excellent surface treatment may be rejected because it is difficult to source, impossible to inspect, or too expensive over the equipment life. Conversely, a low-cost component may produce unacceptable downtime, contamination, energy loss, or safety exposure.

Tribological design therefore extends beyond selecting a lubricant or a harder material. It is a decision process that connects interface physics with reliability engineering, risk management, maintenance policy, lifecycle cost, and supply-chain capability. The central question is not simply, “Which material has the lowest wear rate?” It is, “Which design and management strategy provides the required function at acceptable risk and total cost over the asset life?”

This chapter develops that strategic perspective. It introduces design-of-experiments (DoE), failure-mode and effects analysis (FMEA), reliability and life prediction, risk-based maintenance, lifecycle costing, availability analysis, and multi-criteria decision-making. These methods are particularly valuable at postgraduate level because they require integration of experimental evidence, modelling, engineering judgement, and organisational priorities.

Learning Objectives

After studying this chapter, the reader should be able to:

1. Formulate tribological design problems in terms of function, failure modes, constraints, and decision objectives.

2. Plan efficient tribological experiments using screening, factorial, response-surface, and robust-design methods.

3. Apply FMEA, fault-tree reasoning, reliability functions, and life-distribution models to tribological assets.

4. Distinguish intrinsic reliability from maintainability, availability, and operational resilience.

5. Predict and interpret tribological life using physics-based, statistical, and hybrid approaches.

6. Compare alternatives using lifecycle cost, risk, sustainability, and multi-criteria decision analysis.

7. Translate tribological evidence into inspection, lubrication, replacement, inventory, and supply-chain strategies.

8. Recognise limitations, ethical responsibilities, data risks, and emerging digital opportunities.

From Interface Performance to Asset Value

A tribological interface performs a function within a larger system. A rolling bearing supports load and enables motion; a seal prevents leakage and excludes contaminants; a gear contact transmits torque; a coating preserves dimensional accuracy; and a lubricant separates surfaces while carrying away heat. Failure is therefore defined functionally. A bearing may be considered failed not only when it fractures, but also when vibration, temperature, noise, or clearance exceeds an operational limit.

A useful design statement identifies five elements: the required function, operating envelope, failure criterion, evidence needed for confidence, and consequence of failure. The operating envelope includes load, speed, temperature, alignment, contamination, humidity, chemical exposure, duty cycle, transients, and maintenance conditions. The failure criterion may be wear depth, leakage rate, friction coefficient, surface distress, vibration amplitude, temperature rise, or loss of positional accuracy.

Tribological decisions often involve competing objectives. Lower friction can conflict with load capacity; higher hardness can increase brittleness; a chemically active additive can improve protection but damage seals or pollute a process; and a premium coating can reduce wear while increasing procurement dependency. Design is consequently a constrained optimisation problem rather than a single-property selection exercise.

Important terminology includes:

• Tribosystem: The interacting pair or group of surfaces, lubricant, environment, loads, and surrounding machine structure.
• Failure mode: The physical or functional manner in which an item fails.
• Failure mechanism: The underlying process, such as abrasive wear, fatigue, adhesion, corrosion, or lubricant starvation.
• Mission profile: The time-dependent sequence of loads, speeds, temperatures, environments, and operating states.
• Reliability: The probability that an item performs its required function for a stated time under stated conditions.
• Maintainability: The probability that a failed or degraded item can be restored within a specified time and procedure.
• Availability: The proportion of required time during which the asset is capable of performing its function.
• Criticality: The significance of a failure based on consequences such as safety, production, environmental impact, quality, and cost.
• Degradation indicator: A measurable variable that changes predictably as damage develops.

Designing the Evidence: Design-of-Experiments for Tribology

Tribological tests frequently suffer from excessive variability. Surface roughness, specimen preparation, lubricant contamination, temperature, alignment, and measurement method can all influence results. Changing one factor at a time is attractive because it appears simple, but it is inefficient and can conceal interactions. For example, lubricant viscosity may have little effect at low load but a substantial effect at high temperature and speed. An experiment that changes viscosity alone cannot reveal this interaction.

DoE begins by defining the response and its measurement system. Responses may include wear volume, specific wear rate, friction coefficient, scuffing load, fatigue life, leakage, temperature, or energy consumption. The response must be operationally defined, including sampling interval, filtering, repeatability, and acceptance limits. Before testing materials, the measurement system should be assessed for repeatability and reproducibility; otherwise statistical analysis may attribute instrument variation to the tribological factors.

A practical experimental sequence is as follows:

1. Problem definition: State the design decision and the failure or performance response.

2. Factor identification: Separate controllable factors from noise factors. Typical controllable factors are material, coating, roughness, lubricant, load, speed, and temperature.

3. Screening: Use fractional factorial or other economical designs to identify influential factors.

4. Interaction study: Examine combinations that may produce synergistic or antagonistic effects.

5. Optimisation: Use response-surface methods when a quantitative operating optimum is required.

6. Confirmation: Test the predicted optimum and deliberately include realistic variation.

7. Transfer: Relate laboratory responses to field conditions through similarity analysis, scaling, and validation.

In a two-level factorial design, the main effect of a factor is the difference between the average response at its high and low levels. Interaction effects indicate that the effect of one factor depends on another. Analysis of variance can separate factor effects from experimental error, but statistical significance should not be confused with engineering significance. A small but statistically reliable reduction in friction may be irrelevant if it does not change energy consumption, temperature, or life.

Robust design treats uncontrollable variation as part of the problem rather than an inconvenience. Noise factors may include dust concentration, lubricant age, operator practice, and transient loading. A design that performs well only under nominal laboratory conditions is not robust. Signal-to-noise measures, replication, randomisation, blocking, and centre points help distinguish real effects from drift, order effects, and curvature.

Tribological DoE has specific hazards. A short-duration test may rank materials incorrectly when the dominant field mechanism is fatigue or contamination. A high-load test may create plastic deformation absent in service. Pin-on-disc results do not automatically predict bearing or gear life because contact geometry, lubrication regime, heat transfer, and stress fields differ. Laboratory testing is most useful when its failure mechanism and severity are demonstrably relevant to the mission profile.

FMEA and Risk-Based Tribological Design

Failure-mode and effects analysis provides a disciplined bridge between interface knowledge and asset decisions. For each subsystem, the team identifies the function, failure mode, local effect, system effect, causes, existing controls, and recommended actions. A seal, for example, may fail through lip wear, extrusion, chemical swelling, hard-particle abrasion, or thermal degradation. These modes have different indicators, causes, and remedies; treating all seal failures as “seal wear” weakens the analysis.

Traditional FMEA scores severity, occurrence, and detectability. Their product is often called a risk priority number, but this number should not be used mechanically. A low-frequency failure with catastrophic safety consequences may deserve priority over a frequent but easily detected nuisance failure. Modern practice therefore uses risk matrices, criticality analysis, action thresholds, and explicit consideration of uncertainty.

A strong FMEA asks:

• What function is lost, and what is the safe state?
• Which tribological mechanism is physically credible?
• What operating or maintenance conditions accelerate it?
• What evidence detects degradation before functional failure?
• Can the failure be prevented, slowed, contained, or tolerated?
• What is the consequence of a hidden failure?

Fault-tree analysis complements FMEA by starting with an undesired top event and tracing combinations of causes through logical gates. For example, catastrophic gearbox seizure may require inadequate lubricant delivery combined with excessive load, or contamination combined with ineffective filtration. Event-tree analysis then examines possible consequences after an initiating event, such as a seal leak. Together these tools support barrier design and safety cases.

Risk-based design does not seek to eliminate every failure. It allocates resources according to consequence, likelihood, detectability, and controllability. A noncritical conveyor roller may be run to failure with spare replacement, whereas a turbine bearing may require continuous monitoring, redundant protection, controlled lubricant chemistry, and formal change management.

Reliability, Degradation and Life Prediction

For a non-repairable item, reliability at time t is expressed as R(t) = P(T > t), where T is the random failure time. The failure distribution has a probability density f(t), cumulative distribution F(t), and hazard function h(t) = f(t)/R(t). The hazard function represents the instantaneous failure tendency conditional on survival to time t.

The familiar bathtub curve contains three broad regions: early failures caused by manufacturing or installation defects, a relatively stable random-failure period, and a wear-out period in which degradation increases failure probability. Tribological assets do not always exhibit the complete curve. Some rolling-contact components show fatigue-dominated life distributions; seals may exhibit a mixture of installation errors, chemical incompatibility, and progressive wear; and lubricated systems may fail suddenly after a long period of apparently normal operation.

The Weibull distribution is widely used because its shape parameter can represent different hazard behaviours. A shape parameter below one is associated with decreasing hazard, approximately one with constant hazard, and above one with increasing wear-out hazard. Weibull analysis can estimate characteristic life and compare designs, but extrapolation beyond the tested range is dangerous when the failure mechanism changes.

Life prediction may be:

• Empirical: Based on field or accelerated-test data and statistical distributions.
• Physics of failure: Based on stress, temperature, lubrication, contact mechanics, crack growth, wear laws, or chemical kinetics.
• Reliability growth: Based on progressive design or process improvement after observed failures.
• Hybrid: Combining a physical degradation model with statistical variation and uncertain operating conditions.

A wear model such as Archard’s relation can provide a first-order estimate linking wear volume to load, sliding distance, hardness, and a wear coefficient. It is useful for sensitivity studies but not a universal life law. The wear coefficient may change with temperature, lubrication regime, counterface roughness, debris, transfer-film formation, and running-in. Similarly, fatigue life models require appropriate contact stress, material, lubrication, contamination, and reliability assumptions.

Condition-based life prediction uses degradation indicators. If a measured variable y increases with time, a threshold-based model may estimate the remaining useful life when y reaches a critical limit. More advanced methods use stochastic processes, Bayesian updating, particle filters, or machine-learning regression. These methods require training data that represent the actual asset population and operating conditions. A highly accurate model trained on clean laboratory data can be less valuable than a simpler model that remains reliable in the field.

Uncertainty must be reported explicitly. Sources include parameter uncertainty, measurement error, model-form error, changing duty cycle, and unknown maintenance quality. Decisions should use confidence bounds or conservative design margins rather than a single predicted life.

Maintenance, Availability and Lifecycle Cost

Reliability alone does not determine operational performance. Availability depends on failure frequency, repair duration, logistics, inspection intervals, planned downtime, and restoration quality. A simplified steady-state expression for inherent availability is A = MTBF/(MTBF + MTTR), where MTBF is mean time between failures and MTTR is mean time to repair. In real plants, achieved availability also includes waiting for diagnosis, permits, parts, specialists, and production access.

Tribological evidence can support several maintenance strategies:

• Run-to-failure: Appropriate when consequences are low and replacement is simple.
• Time-based replacement: Appropriate when age-related failure is demonstrated and inspection is impractical.
• Condition-based maintenance: Appropriate when degradation can be detected early and action time exceeds procurement and repair time.
• Risk-based inspection: Appropriate when inspection resources must be directed toward high-consequence mechanisms.
• Design-out maintenance: Appropriate when repeated failures arise from an inherently unsuitable interface.

A maintenance interval should not be selected solely from average life. It should consider the probability of failure before intervention, the cost and risk of intervention, inspection effectiveness, and the consequences of both false alarms and missed degradation. Excessively frequent lubrication can introduce contamination, overgreasing, or unnecessary exposure; insufficient lubrication can accelerate damage. Maintenance instructions must therefore specify quantity, method, cleanliness, lubricant identity, compatibility, and verification.

Lifecycle cost includes acquisition, installation, energy, consumables, inspections, planned and unplanned maintenance, downtime, disposal, environmental compliance, and residual value. A useful comparison is the net present cost:

LCC = initial cost + present value of operating and maintenance costs + present value of downtime and risk consequences − residual value.

Discounting makes future costs comparable, but economic calculations should not hide safety or environmental obligations. A low-cost design with a small probability of severe release, fire, or injury cannot be justified merely by an attractive average cost.

Multi-Criteria Decisions and Supply-Chain Resilience

Tribological alternatives often cannot be ranked by one metric. A decision matrix may include reliability, friction, wear, temperature capability, compatibility, maintainability, cost, carbon impact, qualification maturity, supplier dependence, and consequence of failure. Methods such as weighted scoring, the analytic hierarchy process, outranking, or Pareto analysis can structure the decision.

Weights should be agreed by relevant stakeholders and tested through sensitivity analysis. If a small change in the weight assigned to supply risk reverses the preferred option, the decision is sensitive and should not be presented as objectively determined. Hard constraints—such as legal compliance, safety limits, or required temperature capability—should be screened before weighted scoring.

Supply-chain analysis is part of tribological design because interface performance depends on consistent materials, lubricants, seals, coatings, and maintenance consumables. Risks include single-source components, counterfeit lubricants, obsolete formulations, long lead times, changing supplier processes, and dependence on restricted minerals or specialist coating facilities. Mitigations include approved-equivalent specifications, qualification of alternate suppliers, incoming inspection, strategic spares, local repair capability, and configuration control.

The engineering specification should describe performance and compatibility requirements rather than relying unnecessarily on a proprietary name. However, equivalence must be demonstrated; two lubricants with the same viscosity grade may differ in additive chemistry, seal compatibility, oxidation stability, or filterability. Substitution without technical approval is a common cause of tribological failure.

Digital Monitoring and Emerging Practice

Sensors for vibration, temperature, acoustic emission, lubricant pressure, particle concentration, dielectric properties, and friction-related torque can convert a hidden tribological process into an observable condition. Digital twins can combine asset geometry, mission profile, sensor data, and degradation models to estimate risk and remaining life. Artificial intelligence may identify patterns in high-dimensional signals, but it does not remove the need for sound failure physics.

A useful digital system has a clear decision purpose: trigger lubrication, schedule inspection, derate operation, order a spare, or stop the machine. Data without an action pathway creates alarm fatigue. Models should be validated against confirmed failure mechanisms, monitored for drift, and protected against poor sensor installation, missing data, cyber intrusion, and biased training samples.

Emerging practice is moving toward physics-informed machine learning, probabilistic digital twins, automated lubricant condition assessment, additive manufacturing of wear-resistant surfaces, self-lubricating materials, and surface engineering designed for reduced environmental impact. These developments remain subject to qualification, repairability, regulatory approval, and lifecycle evidence.

Illustrative Case Study: Selecting a Gearbox Bearing Strategy

Background

A fictional process plant operates a high-load gearbox driving a continuous production line. Bearing failures have caused long outages. Three alternatives are available: retain the current bearing and improve lubrication control; install a premium bearing with enhanced surface treatment; or redesign the housing and filtration system while using a standard bearing.

Situation and Problem

Historical records show failures associated with lubricant contamination, misalignment, and inadequate relubrication. The plant reports an average repair duration of 30 hours, but parts and specialist availability can extend this considerably. The production consequence of an unexpected stoppage is high, whereas planned maintenance can be performed during a monthly production window.

Analysis

The team first defines bearing failure functionally as unacceptable vibration or temperature that threatens torque transmission, not merely visible surface damage. FMEA identifies three dominant mechanisms: abrasive wear from particles, rolling-contact fatigue accelerated by misalignment, and lubricant starvation. A DoE using contamination level, alignment error, lubricant quantity, and bearing design shows strong interaction between contamination and lubricant condition. The premium bearing performs well in clean tests but loses much of its advantage under realistic contamination. The housing redesign improves filtration and alignment control and is less sensitive to lubricant quantity.

A lifecycle model includes purchase, installation, planned inspection, unplanned downtime, lubricant consumption, and spare inventory. Although the housing redesign has the highest initial cost, its predicted failure frequency and repair exposure are lowest. Sensitivity analysis shows that it remains preferable unless the production downtime cost is substantially lower than assumed or the redesign cannot be installed during the available shutdown.

Strategy and Solution

The selected strategy combines the housing and filtration redesign with a standard qualified bearing. A contamination-control plan specifies filter performance, cleanliness verification, breather maintenance, and lubricant sampling. Vibration and temperature monitoring are connected to alarm levels based on baseline data. A condition-based inspection is scheduled when the degradation trend indicates sufficient lead time for a planned shutdown. Two bearing sets are held as critical spares, and an alternate supplier is qualified.

Outcome

In this illustrative case, the decision does not arise from the lowest laboratory wear rate. It results from matching the dominant field mechanisms, improving detectability, reducing repair exposure, and controlling supply risk. The case demonstrates that a system-level intervention may create more value than a superior isolated component.

Learning Points

The most important evidence is mechanism-relevant evidence. FMEA should guide both testing and monitoring. Lifecycle cost must include downtime and logistics, and a technically attractive component is not automatically the best asset decision. Finally, maintenance strategy should be designed concurrently with the interface rather than added after commissioning.

Challenges, Professional Responsibilities and Common Mistakes

Several errors recur in tribological decision-making. Teams may optimise friction while ignoring wear debris, use laboratory ranking as a direct life prediction, fit a statistical distribution to too few failures, or assume that a sensor signal proves a particular mechanism. Other mistakes include confusing correlation with causation, neglecting installation quality, treating supplier substitutions as administrative changes, and using a risk-priority number without examining severity.

Ethically, engineers must communicate uncertainty and avoid overstating predicted life. Safety-critical decisions require traceable evidence, independent review, controlled configuration, and compliance with applicable standards and regulations. Maintenance personnel should receive clear instructions and realistic access provisions. Environmental responsibilities include lubricant leakage prevention, waste handling, chemical compatibility, energy use, and consideration of repair or reuse instead of premature replacement.

Governance should define who owns the model, who approves a material or lubricant change, how field failures are reported, and how lessons are fed back into design. Reliability data must be protected from incentives that encourage under-reporting. A credible asset-management culture treats failures as evidence for learning rather than merely as events to be concealed.

Best-Practice Workflow

A robust tribological asset decision can follow this sequence:

1. Define the required function, mission profile, boundaries, and failure criteria.

2. Map the tribosystem and identify credible mechanisms.

3. Conduct FMEA and criticality analysis with operations and maintenance personnel.

4. Select measurements and verify the measurement system.

5. Use DoE to identify influential factors and interactions.

6. Validate laboratory mechanisms against field evidence.

7. Build reliability, degradation, and uncertainty models.

8. Compare design, maintenance, inventory, and supplier alternatives using lifecycle cost and multiple criteria.

9. Establish monitoring, inspection, intervention, and escalation rules.

10. Verify performance after implementation and update the models with field data.

This workflow is iterative. New evidence about contamination, lubricant ageing, or repair quality may change the risk assessment and the preferred design.

Chapter Summary

Tribological design is a system-level activity linking interface physics with reliability, maintenance, risk, economics, and supply-chain decisions. Design-of-experiments reveals influential factors and interactions more efficiently than one-factor-at-a-time testing, but results must represent the actual failure mechanism. FMEA, fault-tree analysis, and criticality methods organise failure knowledge and prioritise controls. Reliability and life prediction require appropriate statistical distributions, physics-based understanding, and explicit uncertainty. Availability depends on both failure behaviour and restoration capability. Lifecycle cost should include energy, maintenance, downtime, risk, and disposal rather than purchase price alone. Multi-criteria and supply-chain analysis help select solutions that remain effective and supportable throughout the asset life. Digital monitoring and artificial intelligence can improve prediction when connected to validated mechanisms and clear decisions. The best tribological strategy is therefore the one that delivers required function with controlled risk, maintainable performance, and defensible lifecycle value.

Key Terms

Tribosystem: Interacting surfaces, lubricant, environment, loads, and machine structure.

Mission profile: Time-dependent description of operating loads, speeds, temperatures, and environments.

Failure mode: The manner in which an item loses a required function.

Failure mechanism: Physical process producing failure, such as abrasion, fatigue, or corrosion.

Design-of-experiments: Statistical planning method for studying factor effects efficiently.

Interaction effect: Change in one factor’s effect caused by the level of another factor.

Robust design: Design that maintains performance despite noise and operating variation.

FMEA: Structured method for identifying failure modes, effects, causes, and controls.

Fault-tree analysis: Deductive analysis tracing combinations of causes to a top event.

Reliability: Probability of successful function for a stated time and condition.

Hazard function: Conditional instantaneous tendency to fail at a specified time.

Weibull distribution: Flexible life distribution used to represent varying hazard behaviour.

Physics of failure: Life prediction based on physical degradation mechanisms.

Degradation indicator: Measurable variable that tracks developing damage.

Maintainability: Probability of restoring function within a specified time.

Availability: Proportion of required time that an asset is operationally capable.

Lifecycle cost: Total discounted cost of ownership, operation, maintenance, risk, and disposal.

Criticality: Significance of a failure based on its consequences and likelihood.

Condition-based maintenance: Maintenance triggered by evidence of developing condition.

Digital twin: Digital representation combining asset information, models, and operational data.

Review Questions

Short Answer Questions

1. Why is a low laboratory wear rate insufficient by itself for selecting a tribological design?

2. What is the purpose of identifying interaction effects in a design-of-experiments study?

3. Distinguish reliability from availability.

4. What information should a tribological FMEA record for each failure mode?

5. Why must uncertainty be included in life prediction?

Long Answer Questions

1. Explain a complete DoE workflow for optimising a lubricated sliding interface, including factor selection, screening, confirmation, and field validation.

2. Discuss how FMEA and fault-tree analysis complement each other in tribological risk management.

3. Explain empirical, physics-of-failure, and hybrid approaches to tribological life prediction, including their limitations.

4. Develop a framework for selecting run-to-failure, time-based, condition-based, or risk-based maintenance for an industrial bearing.

5. Discuss how lifecycle cost and supply-chain resilience can change the preferred tribological design even when technical performance is similar.

Analytical / Application Questions

1. A seal has excellent wear resistance in clean oil but fails rapidly in a dusty environment. Design an investigation using FMEA, DoE, and condition indicators to identify and control the dominant mechanism.

2. Two bearing designs have similar predicted mean lives, but one requires a single overseas supplier and the other has several qualified suppliers. Construct a multi-criteria decision approach and identify the sensitivity checks required.

3. A monitored gearbox shows slowly increasing vibration, but the model has been trained only on laboratory data. Decide whether to schedule immediate replacement, continue operation, or collect more evidence, and justify the decision using uncertainty, consequence, lead time, and availability considerations.

22. Multiple Choice Questions with Answers

Question 1

Which statement best describes the central objective of tribological asset design?

A. Minimise friction under nominal laboratory conditions

B. Select the hardest available material

C. Deliver required function over the asset life at acceptable risk and total cost

D. Maximise the number of condition-monitoring sensors

Correct Answer: C. Deliver required function over the asset life at acceptable risk and total cost

Question 2

Why are interaction effects important in a tribological design-of-experiments study?

A. They show whether a factor’s effect depends on another factor

B. They eliminate the need for replication

C. They prove that laboratory results equal field life

D. They replace measurement-system analysis

Correct Answer: A. They show whether a factor’s effect depends on another factor

Question 3

A Weibull shape parameter greater than one generally indicates:

A. Decreasing failure tendency

B. Approximately constant failure tendency

C. Increasing failure tendency associated with wear-out

D. Absence of statistical variation

Correct Answer: C. Increasing failure tendency associated with wear-out

Question 4

Which maintenance strategy is most appropriate when degradation can be detected with sufficient warning before failure?

A. Condition-based maintenance

B. Uncontrolled run-to-failure

C. Immediate replacement after every operating cycle

D. Ignoring the degradation signal

Correct Answer: A. Condition-based maintenance

Question 5

What is a major limitation of applying Archard’s wear relation directly to every tribological system?

A. It cannot include load or sliding distance

B. The wear coefficient may change with mechanism and operating conditions

C. It applies only to electrical systems

D. It predicts reliability without any material data

Correct Answer: B. The wear coefficient may change with mechanism and operating conditions

Question 6

Why should a risk-priority number not be used mechanically in FMEA?

A. It cannot contain any numerical values

B. It may obscure catastrophic low-frequency failures and unequal consequences

C. It measures only lubricant viscosity

D. It automatically includes lifecycle cost

Correct Answer: B. It may obscure catastrophic low-frequency failures and unequal consequences

Question 7

Which item is most clearly part of lifecycle cost rather than initial acquisition cost?

A. Purchase price only

B. Colour of the equipment

C. Unplanned downtime and disposal obligations

D. Nominal shaft diameter only

Correct Answer: C. Unplanned downtime and disposal obligations

Question 8

In the gearbox case, why was a system redesign preferred over the premium bearing alone?

A. The premium bearing had no measurable laboratory performance

B. The redesign addressed contamination and alignment mechanisms dominant in the field

C. The redesign eliminated the need for maintenance

D. The standard bearing required no qualification

Correct Answer: B. The redesign addressed contamination and alignment mechanisms dominant in the field

Question 9

What is the principal purpose of sensitivity analysis in multi-criteria decision-making?

A. To conceal disagreement among stakeholders

B. To determine whether changes in assumptions alter the preferred option

C. To remove all engineering judgement

D. To guarantee that the cheapest option wins

Correct Answer: B. To determine whether changes in assumptions alter the preferred option

Question 10

Which practice most strengthens the credibility of a digital tribological life-prediction model?

A. Training only on ideal laboratory data

B. Using the model without defining an operational action

C. Validating predictions against field-confirmed mechanisms and monitoring model drift

D. Replacing all physical understanding with opaque algorithms

Correct Answer: C. Validating predictions against field-confirmed mechanisms and monitoring model drift