CQE Questions and Answers

Under a defect classification system, a nonconformance that could cause a product's usability to be substantially reduced would be classified as major. Major nonconformances are significant issues that affect the product's functionality, performance, or customer satisfaction but do not pose a safety risk or cause complete failure. Reference: ISO 9001:2015, which includes guidelines for classifying nonconformances based on their impact on product quality and usability.

In geometric dimensioning and tolerancing (GD&T), the symbol shown in the image is used to indicate a datum target point. This symbol is employed to designate specific locations on a part that are used as reference points for measurements. Datum targets are essential for ensuring accurate and repeatable measurements, particularly in complex assemblies where precision is crucial.

Datum Target Point: A specific point on a part that serves as a reference for measurement or manufacturing. Datum target points are critical for establishing a reference frame for the part's geometry.

References:

ASME Y14.5-2009 - Geometric Dimensioning and Tolerancing Standard.

Foster, L. W. (2010). Geometric Dimensioning and Tolerancing for Mechanical Design 2/E. McGraw-Hill Education.

Quality planning focuses on identifying and meeting the needs of the customers. It involves defining quality standards and determining how to achieve them to ensure customer satisfaction.

A scatter diagram is used to assess the relationship between two variables. In this case, it can be used to analyze the relationship between storage temperature and the overall length of the metalcomponent. A scatter diagram helps in visualizing any correlation between the two variables. Reference: "The Quality Toolbox" by Nancy R. Tague, which provides descriptions and uses for various quality tools including scatter diagrams.

Failing to cite the significant contributions of other individuals in a final manuscript for publication violates the category of "Relations with Peers" in the ASQ Code of Ethics. This category emphasizes the importance of acknowledging and respecting the contributions of colleagues and collaborators in professional work. Proper citation practices are essential for maintaining professional integrity and fostering a collaborative environment within the professional community.References:

ASQ Code of Ethics.

Professional Ethics in Engineering by Mike W. Martin and Roland Schinzinger.

Design Failure Mode and Effects Analysis (DFMEA) is a systematic approach to identifying and addressing potential failure modes in a product design, aiming to reduce risks associated with those failures.

Severity Classification Matrix:

Used in DFMEA to prioritize risks based on their severity, occurrence, and detection, but not the most desirable outcome.

Reduction in Design Risk:

The primary goal of DFMEA is to identify potential failure modes and mitigate their impact, thereby reducing the overall risk in the design.

This involves taking corrective actions to eliminate or reduce the likelihood of failures and their effects.

Comprehensive List of Design Functions:

Part of the DFMEA process but not the ultimate goal.

Estimate of Failure Frequency:

Important for assessing risk but secondary to actually reducing the risk.

References: DFMEA methodologies and best practices in design risk management.

Robust design focuses on improving product and process designs to be less sensitive to variations, such as ambient temperature changes.

Adjusting machine settings to maintain consistent seal strength despite temperature fluctuations is an example of making a process more robust.

Robust design techniques aim to ensure that products and processes perform reliably under varying conditions.

References: "Taguchi's Quality Engineering Handbook" explains the principles of robust design and its application in manufacturing.

Document control is a critical element of a quality system that ensures that all work instructions, procedures, and specifications are up-to-date, consistent, and accessible to relevant personnel.Proper document control helps prevent errors and defects caused by outdated or conflicting work instructions.

References:

ISO 9001:2015. (2015). Quality Management Systems – Requirements. ISO.

ASQ. (n.d.). Document Control. Retrieved from https://asq.org/quality-resources/document-control

Hoshin planning, also known as Hoshin Kanri or policy deployment, is a strategic planning process that aligns an organization's functions and activities with its strategic objectives. It involves setting idealistic goals and linking them to actionable work strategies through a systematic approach. This method ensures that all levels of an organization are working harmoniously towards common objectives.

The constant failure rate per hour is calculated as the inverse of the mean time between failures (MTBF). Given an MTBF of 200 hours: Failure rate (λ) = 1 / MTBF = 1 / 200 = 0.005

A product that exhibits a constant failure rate typically follows an exponential distribution for the failure rate. The exponential distribution is often used to model the time between failures in systems with a constant hazard rate, meaning the failure rate does not change over time.References:

Reliability Engineering by Elsayed A. Elsayed.

ASQ Quality Press: The Certified Reliability Engineer Handbook.

A check sheet is a structured, prepared form for collecting and analyzing data.

It is one of the seven basic quality tools used to record frequency of events or defects, making it ideal for showing frequency distribution.

Affinity diagrams and interrelationship digraphs are used for organizing ideas and identifying relationships, while a PERT chart is used for project scheduling.

References: The ASQ and various quality management textbooks, such as "The Quality Toolbox" by Nancy R. Tague, outline the use of check sheets for frequency distribution analysis.

The correct order for analyzing experiments is:

Performing hypothesis tests on factors

Checking assumptions

Estimating a mathematical model

Using the model for prediction

Firstly, hypothesis tests are performed to identify significant factors. Secondly, assumptions are checked to ensure the validity of the tests. Thirdly, a mathematical model is estimated based on the significant factors. Lastly, the model is used for prediction. Reference: "Design and Analysis of Experiments" by Douglas C. Montgomery, which outlines these steps in detail.

The Average Outgoing Quality Limit (AOQL) is a key concept in quality control and inspection plans. It is defined as the maximum average outgoing quality over all possible levels of incoming quality for a given acceptance sampling plan. The AOQL is particularly used in situations where 100% inspection or screening of rejected lots is performed, meaning nonconforming units are identified and replaced. This ensures that the outgoing quality is controlled even if the incoming quality fluctuates.

References:

"Introduction to Statistical Quality Control" by Douglas C. Montgomery.

ASQ (American Society for Quality) Glossary: AOQL

When collecting and summarizing data, the methods used for data collection can significantly affect the reliability and validity of the data.

Descriptive Statistics:

Concerned with summarizing data but not the method of collection.

Data Accuracy:

Ensured through proper collection methods but is an outcome rather than an influencing element.

Measurement Scales:

Refers to the types of data (nominal, ordinal, interval, ratio) but not directly affected by redundancy, versatility, and flexibility.

Data Collection Methods:

The techniques and processes used to gather data.

Ensuring redundancy, versatility, and flexibility in data collection methods can help in obtaining comprehensive and accurate data.

References: Research methodology and quality management texts.

To detect subsurface flaws, a method that can penetrate the surface of the material and reveal flaws below the surface is required. Visual inspection and liquid penetrant tests are typically used for surface defects, while the Rockwell test measures hardness. Magnetic particle testing, however, is

Magnetic particle testing (MT) is a non-destructive testing method used to detect surface and near-surface flaws in ferromagnetic materials. It involves magnetizing the material and applying ferrous particles that gather around discontinuities, making subsurface flaws detectable.

In an unreplicated 232^323 factorial experiment, there are 8 runs (since 23=82^3 = 823=8). The total degrees of freedom are calculated as the number of runs minus 1, which is 8−1=78 - 1 = 78−1=7. However, considering there are three factors, each with one degree of freedom, and their interactions (1 for each main effect, 3 two-way interactions, and 1 three-way interaction), the correct total degrees of freedom are 7. References:

Design and Analysis of Experiments by Douglas C. Montgomery.

ASQ Quality Press: The Certified Quality Engineer Handbook.

Option B, which is the oval or rounded rectangle shape, indicates a termination point on a flowchart. This symbol is typically used to represent the start or end of a process in flowchart diagrams.References:

ASQ Quality Press: The Certified Quality Engineer Handbook.

Flowcharting Techniques: A Guide to Better Communication of Information by Marvin P. Larkin.

An effective design review ensures that the design meets certain established criteria and standards. This helps in objectively evaluating the design to find any discrepancies or improvements needed. The other options are not necessarily true for every effective design review:

A: Design reviews can occur at various stages, not just when the design is complete.

B: While brainstorming might be part of the process, an effective review should have structure and defined criteria.

D: The review can involve multiple stakeholders, not just the development team.

The producer's risk, also known as Type I error or alpha risk, is the risk of rejecting a lot that meets the acceptance criteria. It can be determined from the acceptance quality limit (AQL), which is the maximum percentage of defectives that is considered acceptable during random sampling of an inspection lot. The AQL is used in conjunction with sampling plans to assess the quality of a lot and make decisions about its acceptance or rejection.

In a full-factorial design with three factors, each at two levels, the total number of runs required is calculated by 2n2^n2n, where nnn is the number of factors. For three factors, 23=82^3 = 823=8. Thus, 8 runs are required to cover all possible combinations of factor levels.

In written communication, using the active voice is most effective because it is clear and direct. Active voice makes it easier for the reader to understand who is doing what, leading to less ambiguity. For example, "The manager approved the request" is clearer than "The request was approved by the manager." Reference: "The Elements of Style" by Strunk and White, which advises using active voice for clarity and precision in writing.

In value stream analysis, inspection is typically classified as non-value added. This is because inspection does not directly transform the product or add value from the customer's perspective. Instead, it is an activity that checks for defects or issues, which ideally should be prevented by improving the processes.

References:

Rother, M., & Shook, J. (1999). Learning to See: Value Stream Mapping to Add Value and Eliminate MUDA. Lean Enterprise Institute.

Liker, J.K. (2004). The Toyota Way: 14 Management Principles from the World's Greatest Manufacturer. McGraw-Hill.

Design Failure Mode and Effects Analysis (DFMEA) is a systematic method for evaluating a design to identify potential failure modes, their causes, and effects, and to prioritize the actions to mitigate these failures.

Identify Personnel Responsible for Implementation:

While important for action plans, it is not the primary focus of DFMEA.

Predict Failure Frequencies:

DFMEA does consider failure frequencies, but the primary goal is to address and mitigate failures.

Minimize Measurement Variation:

This is more relevant to process control and not the primary focus of DFMEA.

Mitigate Failure Modes:

The main objective of DFMEA is to identify potential failure modes and implement actions to reduce or eliminate the risks associated with these failures.

This involves analyzing the design to foresee where it might fail and taking preventive measures to improve reliability and safety.

References: Quality engineering and risk management texts, specifically those discussing the principles and objectives of DFMEA.

The Plan-Do-Check-Act (PDCA) cycle specifies the use of a feedback cycle. It is a continuous loop of planning, doing, checking, and acting, aimed at continuous improvement and problem-solving in quality management.

Given that components 1, 2, and 3 each have reliabilities of 0.95, and assuming the system configuration as depicted in the image (series-parallel configuration):

Reliability of the parallel components (2 and 3): Rparallel=1−[(1−R2)×(1−R3)]R_{\text{parallel}} = 1 - [(1 - R_2) \times (1 - R_3)]Rparallel​=1−[(1−R2​)×(1−R3​)] Rparallel=1−[(1−0.95)×(1−0.95)]R_{\text{parallel}} = 1 - [(1 - 0.95) \times (1 - 0.95)]Rparallel​=1−[(1−0.95)×(1−0.95)] Rparallel=1−[0.05×0.05]R_{\text{parallel}} = 1 - [0.05 \times 0.05]Rparallel​=1−[0.05×0.05] Rparallel=1−0.0025R_{\text{parallel}} = 1 - 0.0025Rparallel​=1−0.0025 Rparallel=0.9975R_{\text{parallel}} = 0.9975Rparallel​=0.9975

Reliability of the entire system (series of component 1 and the parallel system): Rsystem=R1×RparallelR_{\text{system}} = R_1 \times R_{\text{parallel}}Rsystem​=R1​×Rparallel​ Rsystem=0.95×0.9975R_{\text{system}} = 0.95 \times 0.9975Rsystem​=0.95×0.9975 Rsystem=0.947625R_{\text{system}} = 0.947625Rsystem​=0.947625

Rounded to three decimal places, the system reliability is 0.948.

Recording and distributing meeting minutes primarily serve to capture any decisions made during the meeting. This ensures that all participants have a clear understanding of the outcomes and action items agreed upon.

Error-proofing, or "poka-yoke," is a technique used to prevent errors by designing processes, equipment, or fixtures to avoid mistakes.

Fixture design involves creating jigs, fixtures, or tooling that ensure parts are correctly positioned and assembled, thereby preventing errors.

Housekeeping, equipment calibration, and double sampling do not inherently prevent errors by design.

References: The concept of error-proofing is detailed in quality engineering literature, such as "Poka-Yoke: Improving Product Quality by Preventing Defects" by Shigeo Shingo.

The Risk Priority Number (RPN) in FMEA is determined by the product of the frequency of occurrence, severity, and detection ratings. This multiplication helps in quantifying the risk associated with each potential failure mode, allowing for prioritization of corrective actions based on the highest risk areas.

The primary purpose of the Material Review Board (MRB) is to review and determine the appropriate disposition of nonconforming products. The MRB evaluates the nonconformities, decides whether the products can be reworked, used as-is, or must be scrapped, and ensures that corrective actions are implemented to prevent recurrence.

References:

"The Certified Quality Engineer Handbook" by Connie M. Borror.

ASQ Glossary: Material Review Board (MRB)

An absolutely objective way to measure risk does not exist. Risk assessment inherently involves some level of subjectivity due to the uncertainty and variability in processes and outcomes. While various tools and metrics (e.g., lean metrics, six sigma metrics, quality function deployment) can aid in evaluating and managing risk, none provide an entirely objective measure. Reference: "Risk Management: Concepts and Guidance" by Carl L. Pritchard, which discusses the challenges of achieving absolute objectivity in risk assessment.

A run chart, also known as a run-sequence plot, is a basic tool for analyzing data over time.

Process Stability:

Stability is analyzed using the run chart, but it is not an essential element of the chart itself.

Defect Classifications:

While defects can be plotted, they are not essential to all run charts.

Special Causes:

Identifying special causes can be an outcome of analyzing a run chart, but it is not an essential element.

Time Sequence:

The essential element of a run chart is the time sequence. It plots data points in time order to show trends, shifts, or cycles.

This temporal plotting helps in understanding how the process changes over time.

References: Statistical process control literature and quality management resources discussing run charts.

A criticality analysis is used to classify or determine the safety and quality characteristics of a product or process. This analysis evaluates the severity, occurrence, and detection of potential failures, helping to prioritize issues that could significantly impact safety and quality.

References:

Stamatis, D.H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution. ASQ Quality Press.

IEC 60812:2018. (2018). Analysis techniques for system reliability – Procedure for failure mode and effects analysis (FMEA).

A full factorial experiment involves testing all possible combinations of levels for each factor.

For a three-level (3 levels) and two-variable (2 factors) experiment, the number of runs is calculated as: 32=93^2 = 932=9

Therefore, the minimum number of runs required is 9.

References: "Design and Analysis of Experiments" by Douglas C. Montgomery discusses factorial design and the calculation of runs for different levels and factors.

To convince management of the need for a quality improvement program, it is most effective to present the size of the problem in terms of potential savings and increased profit. Management is often driven by financial metrics, so quantifying the impact of quality issues and the benefits of improvements in monetary terms will be persuasive. Reference: "Juran's Quality Handbook" by Joseph M. Juran, which emphasizes presenting business cases in financial terms to gain management support for quality initiatives.

When establishing a system for the classification of defects, it is crucial to consider the relative seriousness of the defects. This involves evaluating the potential impact of each type of defect on the end-user and the product's performance. The classification typically divides defects into categories such as critical, major, and minor, based on their severity and potential consequences.

References:

"Quality Control Handbook" by J.M. Juran.

ISO 2859-1:1999, "Sampling procedures for inspection by attributes"

The expected number of rejects per sample can be calculated using the reject rate and the sample size. Given a reject rate of 6% (or 0.06) and a sample size of 5:

Expected number of rejects per sample = 0.06 * 5 = 0.3

References:

Montgomery, D.C. (2009). Introduction to Statistical Quality Control. Wiley.

ASQ. (n.d.). Basic Quality Tools. Retrieved from https://asq.org/quality-resources/basic-quality-tools

In risk management, after measures have been taken to prevent or control risks, some risks will still remain. These are known as residual risks.

Inherent Risk:

The risk present before any control measures are applied.

Residual Risk:

The risk that remains after preventive and control measures have been implemented.

It is the risk that the organization decides to accept and manage, rather than eliminating entirely.

Control Risk:

The risk that controls will fail to prevent or detect a risk.

Credit Risk:

The risk of a financial loss due to a borrower's failure to repay a loan.

Residual risk is the remaining risk after all control measures are applied.

References: Risk management frameworks and standards, such as ISO 31000.

Buy-in at all levels of management is critical for the success of quality initiatives. When all management levels are aligned and supportive of quality goals, it ensures that these initiatives are prioritized and integrated into daily operations. This comprehensive support fosters a culture of quality throughout the organization, making it easier to implement and sustain quality improvements.

References:

Juran, J.M., & Gryna, F.M. (1993). Quality Planning and Analysis: From Product Development through Use. McGraw-Hill.

Deming, W.E. (1986). Out of the Crisis. MIT Press.

To calculate the 95% confidence interval for the proportion of nonconforming items in a sample, we use the formula for the confidence interval for a proportion: p̂ ± Z * √(p̂(1-p̂)/n), where p̂ is the sample proportion (80/2000 = 0.04), Z is the Z-score for a 95% confidence level (1.96), and n is the sample size (2000). Plugging in the values, we get the confidence interval as [0.0308, 0.0499]. Reference: "Statistics for Engineers and Scientists" by William Navidi, which explains confidence intervals and their calculations.