Interview Questions for Measurement and Inspection Techniques

Accuracy and precision are crucial concepts in measurement, often confused but distinct. Accuracy refers to how close a measurement is to the true value. Think of it like hitting the bullseye on a dartboard – a highly accurate measurement is very close to the center. Precision, on the other hand, refers to how close repeated measurements are to each other. This is like consistently hitting the same spot on the dartboard, even if that spot isn’t the bullseye. You can have high precision but low accuracy (consistently missing the bullseye by the same amount), high accuracy but low precision (hitting near the bullseye but inconsistently), or ideally, both high accuracy and high precision.

Example: Imagine measuring the length of a table. If the true length is 1 meter, a measurement of 1.01 meters is more accurate than a measurement of 0.95 meters. However, if you measure the table multiple times and get readings of 0.98m, 0.99m, and 1.00m, your precision is higher than if you got readings of 0.95m, 1.05m, and 0.90m, even though the latter might have a single measurement closer to the true value.

Measurement errors are unavoidable, but understanding their types helps minimize their impact. Errors can be broadly classified as systematic, random, and gross errors.

• Systematic errors: These are consistent and repeatable errors that result from a flaw in the measurement system. For instance, a miscalibrated instrument will consistently produce biased results. Minimizing systematic errors involves proper calibration and instrument maintenance.
• Random errors: These are unpredictable variations in measurements due to uncontrollable factors like environmental conditions or observer variability. Reducing random errors involves taking multiple measurements and using statistical techniques like averaging to minimize the impact of these fluctuations.
• Gross errors: These are blunders or mistakes made by the operator, such as misreading a scale or incorrectly recording data. Careful attention to detail and procedural adherence minimize gross errors.

Minimizing Errors: Effective error minimization relies on a multi-pronged approach: Regular calibration and maintenance of instruments, proper environmental control during measurements, using appropriate measurement techniques, and training personnel in best practices. Statistical analysis of repeated measurements helps identify and potentially compensate for some errors.

Calibration is essential for maintaining the accuracy of measurement instruments. Common methods include:

• Traceable Standards: Comparing the instrument to a known standard, traceable to a national or international standard (e.g., NIST in the US). This is often done by sending the instrument to a certified calibration laboratory.
• Direct Comparison: Comparing the instrument’s reading to a known standard using a calibrated reference instrument.
• In-situ Calibration: Calibrating the instrument while it’s in place and operating, often using a known standard or signal within the system. This is common in process control systems.
• Using Calibration Software: Certain instruments, especially digital ones, can be calibrated using specialized software, often involving entering known values and adjusting internal parameters.

The chosen method depends on the type of instrument and its application. Proper calibration documentation, including date, results, and technician information, is crucial for traceability and regulatory compliance.

Selecting the appropriate measurement instrument requires careful consideration of several factors:

• Accuracy and Precision Requirements: Determine the level of accuracy and precision needed for the task. A high-precision application might require a more sophisticated instrument.
• Measurement Range: Choose an instrument with a range that covers the expected values.
• Resolution: The smallest increment the instrument can measure. Higher resolution means greater detail, but is not always necessary.
• Environmental Conditions: Consider environmental factors like temperature and humidity that can influence accuracy.
• Cost and Availability: Balance the need for precision and accuracy with budgetary constraints and instrument availability.
• Ease of Use and Maintenance: Select an instrument that is easy to use and maintain, factoring in operator training.

Example: Measuring the diameter of a small wire requires a different instrument (micrometer) than measuring the dimensions of a large steel plate (vernier caliper or CMM).

Measurement uncertainty quantifies the doubt associated with a measurement result. It represents the range of values within which the true value likely lies, considering all sources of error. It’s not about the error itself, but rather the degree of confidence in the measured value. A small uncertainty indicates high confidence, and a large uncertainty suggests considerable doubt.

Significance: Understanding uncertainty is crucial for making informed decisions. In engineering, for example, knowing the uncertainty associated with component dimensions ensures that designs account for potential variations and meet safety requirements. Reporting measurement uncertainty is a standard practice in many fields and is essential for ensuring data quality and reliability. It’s often expressed as a ± value alongside the measurement result (e.g., 10.00 ± 0.05 mm).

Nondestructive Testing (NDT) methods are used to evaluate materials and components without causing damage. Common methods include:

• Visual Inspection: A simple but important method using visual aids like magnifying glasses or borescopes.
• Liquid Penetrant Testing (LPT): Detects surface-breaking defects by applying a dye that penetrates cracks and is then revealed with a developer.
• Magnetic Particle Testing (MPT): Uses magnetic fields to detect surface and near-surface flaws in ferromagnetic materials.
• Ultrasonic Testing (UT): Uses high-frequency sound waves to detect internal flaws.
• Radiographic Testing (RT): Uses X-rays or gamma rays to create images of internal structures, revealing defects.
• Eddy Current Testing (ECT): Uses electromagnetic induction to detect surface and subsurface flaws in conductive materials.

The choice of NDT method depends on the material, component geometry, and type of defect being sought.

My experience with dimensional inspection techniques spans various methods and applications. I’ve extensively used coordinate measuring machines (CMMs) for precise measurements of complex parts, utilizing various probing techniques and software packages for data analysis and report generation. I’m proficient in using vernier calipers, micrometers, and optical comparators for routine dimensional checks. I have practical experience in performing geometric dimensioning and tolerancing (GD&T) analysis to ensure parts meet specified tolerances. In a previous role, I was responsible for developing and implementing inspection plans for automotive components, using a combination of manual and automated measurement systems to ensure product quality and conformance to specifications. Furthermore, I’ve been involved in the analysis of measurement data, identification of root causes for dimensional variations, and implementation of corrective actions to improve manufacturing processes. My experience also includes training personnel on proper measurement techniques and the interpretation of inspection results.

When discrepancies arise during inspection, my approach is systematic and thorough. First, I verify the accuracy of the measurement equipment – is it calibrated? Are there any environmental factors affecting readings (temperature, humidity)? I then carefully re-inspect the item, potentially using different measurement techniques or tools to confirm the discrepancy. If the discrepancy is confirmed, I document everything meticulously: the original measurement, the re-measurement, the equipment used, environmental conditions, and any relevant photos or videos. This detailed record helps in root cause analysis. Next, depending on the severity and nature of the discrepancy, I might initiate corrective actions, ranging from minor adjustments to the process to complete rejection of the item. For example, if a small dimension is slightly off, we might adjust the machine settings; if a critical safety parameter is not met, the product is rejected. Finally, I report the findings to the appropriate team, offering recommendations to prevent similar discrepancies in the future. This could involve revising process parameters, improving operator training, or upgrading equipment.

Statistical Process Control (SPC) is a powerful method for monitoring and controlling manufacturing processes. It uses statistical techniques to identify variations and trends in a process, allowing us to distinguish between common cause (random) and special cause (assignable) variation. Common cause variation is inherent to the process itself, while special cause variation results from external factors like machine malfunction or operator error. The goal of SPC is to minimize special cause variation and maintain the process within acceptable control limits. It’s like having a dashboard for your manufacturing process; it gives you a real-time view of how things are going. This proactive approach prevents defects, improves efficiency, and reduces costs. I’ve applied SPC successfully in various projects, reducing scrap rates by 15% in one instance by pinpointing and addressing a specific machine setting that was leading to inconsistent outputs.

Control charts are the heart of SPC. They’re graphical tools that display data over time, allowing us to visually monitor process variation. Common types include X-bar and R charts (for average and range of measurements), p-charts (for proportions of defects), and c-charts (for the number of defects). Each chart has a central line representing the average, and upper and lower control limits. Data points falling within these limits suggest the process is stable and under control. Points outside these limits signal potential special cause variation, requiring investigation and corrective action. For instance, in a manufacturing line producing screws, an X-bar and R chart would track the average screw length and the range of lengths in each sample. If a point falls outside the control limits, it indicates a problem needing immediate attention. Perhaps the machine needs recalibration or maintenance.

My experience encompasses a broad range of gauging and measuring tools. This includes micrometers for precise linear measurements, calipers for internal and external measurements, dial indicators for detecting small displacements, and optical comparators for accurate inspection of complex parts. I’m also proficient with more advanced tools like CMMs (Coordinate Measuring Machines) for high-precision three-dimensional measurements and laser scanners for rapid surface scanning. In one project, we used a CMM to meticulously inspect the critical dimensions of aerospace components, ensuring they met the stringent tolerances specified. In another, we utilized laser scanning for reverse engineering a complex part, creating a 3D model for further analysis and manufacturing. The selection of the right tool depends entirely on the specific application and the required level of accuracy.

Traceability of measurements is crucial for ensuring the reliability and validity of inspection results. It means establishing an unbroken chain of custody, showing how a measurement can be linked back to a known standard. This involves using calibrated equipment, regularly verifying its accuracy against traceable standards (often NIST-traceable in the US), and meticulously documenting all measurement procedures. Calibration certificates and records are essential components. For instance, when using a micrometer, we ensure it’s been recently calibrated against a certified standard micrometer, which in turn can be traced back to a national standard. This assures that our measurements are accurate and reliable, offering confidence in our inspection results. Without traceability, measurements are essentially meaningless.

Data analysis and reporting form a vital part of my quality control responsibilities. I use statistical software (like Minitab or JMP) to analyze measurement data, identifying trends, patterns, and outliers. This involves creating histograms, scatter plots, and other visual representations of data to highlight key findings. I’m also experienced in generating comprehensive reports that summarize inspection results, including statistical summaries, charts, and conclusions. These reports are presented to stakeholders, highlighting areas of strength and areas needing improvement. For example, I recently analyzed defect data from a production line, identifying a specific shift where defect rates were significantly higher. This led to an investigation that revealed a maintenance issue, which was addressed, resulting in a significant reduction in defects.

Interpreting measurement data involves identifying trends and patterns through various analytical techniques. I often use statistical process control charts to detect shifts in process averages or increases in variability. Histograms show the distribution of data, highlighting potential issues such as excessive skewness or bimodality. Scatter plots reveal relationships between different variables. For instance, analyzing a scatter plot of part thickness versus machine speed might reveal a correlation, indicating that adjustments to the machine speed are needed to maintain consistent thickness. I also employ run charts to identify trends over time. Combining these visual tools with statistical analysis allows for a comprehensive understanding of measurement data, enabling proactive corrective actions and process improvements.

Root cause analysis (RCA) in quality control is a systematic approach to identifying the underlying causes of defects or non-conformances, rather than just addressing the symptoms. It’s crucial for preventing future occurrences and improving overall product quality. My experience involves using various RCA methodologies, including the 5 Whys, Fishbone diagrams (Ishikawa diagrams), and Fault Tree Analysis (FTA).

For example, in a previous role, we experienced a recurring issue with surface imperfections on a manufactured part. Instead of simply rejecting the faulty parts, we employed the 5 Whys technique:

• Why are there surface imperfections? – Insufficient cleaning of the material before processing.
• Why was the material insufficiently cleaned? – The cleaning machine was malfunctioning.
• Why was the cleaning machine malfunctioning? – A sensor was faulty, leading to inaccurate cleaning cycles.
• Why was the sensor faulty? – Lack of scheduled preventative maintenance.
• Why was preventative maintenance neglected? – Insufficient training for maintenance personnel.

Identifying the root cause (inadequate training) allowed us to implement targeted solutions: retraining personnel and establishing a robust preventative maintenance schedule. This significantly reduced the incidence of surface imperfections.

Documenting and managing inspection records is critical for maintaining traceability and demonstrating compliance. We utilize a combination of digital and physical methods. Inspection data is usually recorded directly into a computerised system using dedicated software, often integrated with our manufacturing execution system (MES).

This software provides tools for data entry, automated reports generation, and secure storage. Physical records, like hard copies of inspection reports or calibration certificates, are also kept in a well-organized filing system that complies with industry regulations and our internal quality management system (QMS). A key aspect is version control – ensuring that only the latest approved version of any inspection document is in use. For instance, we use a document control system with revision tracking, allowing easy identification and retrieval of relevant documents.

Data security and integrity are paramount. Access control measures are implemented to ensure only authorized personnel can access and modify inspection records. Regular backups are performed to safeguard against data loss.

Inspection plans define the methods and criteria used to verify that products or processes meet predetermined quality standards. Different types exist, catering to varying needs and risk levels:

• 100% Inspection: Every single item is inspected. This is highly effective but expensive and time-consuming, usually reserved for critical components or low-volume production.
• Sampling Inspection: A subset of the production batch is inspected, and inferences are made about the entire lot’s quality. This is more cost-effective but carries a risk of missing defects if the sample isn’t representative.
• Acceptance Sampling: Used to decide whether to accept or reject an entire lot based on the sample inspection results. Different plans exist (e.g., single, double, multiple sampling) depending on the acceptable risk level.
• Variable Inspection: Measures a continuous quality characteristic (e.g., dimension, weight). Statistical methods are then used to assess the process capability.
• Attribute Inspection: Checks whether a discrete quality characteristic is present or absent (e.g., surface defects, scratches).

The choice of inspection plan depends on factors such as the cost of inspection, the criticality of the product, the process capability, and the acceptable risk level. Often, a combination of these plans is utilized for optimal effectiveness.

Sampling techniques are crucial in quality control when 100% inspection is impractical. The goal is to obtain a sample that accurately represents the population, allowing for reliable inferences about the entire batch’s quality. Several sampling methods exist:

• Simple Random Sampling: Every item in the population has an equal chance of being selected. This is straightforward but may not capture variations if the population is heterogeneous.
• Stratified Sampling: The population is divided into subgroups (strata) based on relevant characteristics, and a random sample is taken from each stratum. This is useful for ensuring representation of different variations within the population.
• Systematic Sampling: Items are selected at regular intervals (e.g., every tenth item). This is easy to implement but can be problematic if there’s a pattern in the production process.
• Cluster Sampling: The population is divided into clusters (e.g., batches), and some clusters are randomly selected for inspection. This is efficient but may lead to less precise estimations.

The appropriate sampling technique is chosen based on the characteristics of the population and the desired level of precision and accuracy. Statistical techniques are then used to analyze the sample data and make inferences about the quality of the entire lot, including calculating confidence intervals and acceptance criteria. For example, if a 95% confidence interval indicates a defect rate above an acceptable threshold, the entire batch might be rejected.

My experience encompasses various types of Coordinate Measuring Machines (CMMs), including:

• Bridge-type CMMs: These are large, robust machines ideal for measuring large parts. They utilize a bridge structure to support the probe head, allowing for movement along three axes.
• Gantry-type CMMs: Similar to bridge-type but with a larger working volume. They are often used for inspecting automotive parts or large aerospace components.
• Horizontal-arm CMMs: These machines have a horizontal arm that moves over the workpiece. They are suitable for measuring parts that are difficult to place on a traditional CMM table.
• Articulated-arm CMMs: Highly portable, these CMMs are excellent for measuring parts in situ, reducing the need for part handling.

I’m proficient in operating different CMM software packages and employing various measurement techniques, including touch probing, laser scanning, and optical scanning, depending on the part geometry and required precision. I understand the importance of proper probe calibration, environmental control, and the use of appropriate statistical process control (SPC) methods to ensure accurate and reliable measurements.

Troubleshooting and maintaining measurement equipment is vital for ensuring the accuracy and reliability of inspection results. My approach involves a combination of preventive and corrective maintenance.

Preventive Maintenance: This includes regular calibration checks (following manufacturer recommendations), cleaning of optical components, and lubrication of mechanical parts. We utilize a computerized maintenance management system (CMMS) to track maintenance schedules and ensure all equipment is serviced as needed. For example, we schedule regular checks on CMM probes for wear and tear, which directly impacts measurement accuracy.

Corrective Maintenance: If a problem occurs, I follow a structured troubleshooting process. I begin by reviewing the error messages, checking connections, and visually inspecting the equipment. If the problem persists, I consult the equipment’s documentation and may contact the manufacturer’s support team for assistance. Proper record-keeping is crucial for tracking repairs, replacement parts, and maintenance history. This helps to improve equipment uptime and identify recurring issues that may require process improvements.

Image analysis plays a significant role in modern inspection, offering a non-contact and highly automated approach to quality control. My experience involves using image analysis software to inspect various features such as dimensions, surface finish, and defects on a wide range of components.

For example, I’ve utilized image analysis to automate the inspection of printed circuit boards (PCBs). The software automatically identifies components, verifies their placement and orientation, and detects solder defects. This significantly improves inspection speed and consistency compared to manual visual inspection.

The process usually involves acquiring high-resolution images using appropriate lighting techniques, employing image processing algorithms (like edge detection, thresholding, and pattern recognition) to extract relevant features, and finally comparing the measured data to predefined specifications. Statistical methods are employed to assess the process capability and identify any out-of-specification conditions. Experience with various image analysis software packages, along with an understanding of image processing principles, is essential for successful implementation.

Selecting the right sampling plan is crucial for efficient and effective quality control. It’s about finding the balance between ensuring sufficient data to make reliable conclusions about the overall quality and minimizing the cost and time involved in inspection. Key considerations include:

• Acceptable Quality Level (AQL): This defines the maximum percentage of defective units that is still considered acceptable in a batch. A lower AQL indicates a stricter quality requirement.
• Lot Size: The total number of units in the batch to be inspected. Larger lots generally require more complex sampling plans.
• Inspection Severity: This reflects the criticality of the product’s quality. Higher-risk products require more stringent sampling plans.
• Type of Inspection: Different inspection methods (e.g., attribute inspection – counting defects, or variable inspection – measuring continuous variables) lead to different sampling plans. Attribute inspection focuses on whether an item is defective or not, while variable inspection measures characteristics like length, weight, or temperature.
• Cost-Benefit Analysis: The cost of inspection (labor, equipment, etc.) needs to be balanced against the potential costs of accepting defective units. A very large sample size is great for accuracy but might not be cost-effective.
• Sampling Standards: Adherence to recognized standards like MIL-STD-105E or ANSI/ASQ Z1.4 provides a standardized approach and ensures consistency.

For example, imagine inspecting a batch of 1000 screws for defects. A low AQL (e.g., 0.65%) might be chosen for a critical application like aircraft fasteners, leading to a larger sample size than would be used for less critical screws (e.g., for furniture).

Developing a quality control plan for a new product is a systematic process that starts long before production begins. Here’s a step-by-step approach:

1. Define Product Specifications: Clearly outline all the requirements for the product, including dimensions, material properties, performance characteristics, and safety standards.
2. Identify Critical Characteristics: Determine the most critical features that directly impact the product’s functionality, safety, and customer satisfaction. These require the most rigorous inspection.
3. Select Inspection Methods: Choose appropriate measurement techniques (e.g., dimensional metrology, visual inspection, destructive testing) for each critical characteristic. The choice will depend on the characteristic’s nature and precision requirements.
4. Establish Acceptance Criteria: Define the acceptable limits for each characteristic, specifying tolerance ranges or acceptable defect rates. This is often based on specifications and industry standards.
5. Develop Sampling Plan: Select an appropriate sampling plan (as discussed in the previous question) to ensure efficient and representative inspection.
6. Create Inspection Procedures: Document detailed, step-by-step procedures for performing each inspection task, including the use of equipment, data recording, and handling of non-conforming items.
7. Implement Control Charts: Use control charts (e.g., X-bar and R charts) to monitor process variability and identify trends indicating potential problems.
8. Training and Calibration: Ensure that inspectors are properly trained and that measurement equipment is regularly calibrated to ensure accuracy and consistency.
9. Corrective Actions: Establish a clear process for investigating and correcting any identified non-conformances, including root cause analysis and preventive actions.

For instance, developing a quality control plan for a newly designed smartphone would involve defining precise tolerances for screen size and resolution, battery capacity, and signal strength. Detailed inspection procedures would be created, including functional tests and drop tests, along with clear acceptance criteria for each.

ISO 9001 is a globally recognized standard for quality management systems. My familiarity extends to its principles, requirements, and practical implementation. I understand its focus on customer satisfaction, continual improvement, and the importance of a robust quality management system. I’m experienced in interpreting and applying its clauses related to:

• Control of Nonconforming Outputs: Managing and rectifying any product or process deficiencies.
• Internal Audits: Conducting regular internal audits to assess the effectiveness of the QMS.
• Corrective Actions: Investigating and correcting deficiencies identified through internal audits or customer feedback.
• Management Review: Participating in management reviews to assess the effectiveness of the QMS and identify areas for improvement.
• Measurement, Analysis and Improvement: Using data to monitor and improve the effectiveness of processes.

I’ve worked in environments where ISO 9001 certification was a crucial requirement, and I’m proficient in documenting processes and procedures to meet its rigorous demands. I understand the importance of traceability and record keeping to maintain compliance.

My experience encompasses a wide range of materials, each presenting unique inspection challenges. For example:

• Metals: I’ve worked extensively with ferrous and non-ferrous metals, using techniques like dimensional metrology (CMMs, optical comparators), hardness testing, and metallurgical analysis to assess quality. Precision is paramount, and understanding material properties like thermal expansion is crucial.
• Plastics: Inspection of plastics often involves visual inspection, dimensional measurements, and mechanical testing to evaluate strength, flexibility, and resistance to chemicals. Factors such as shrinkage during molding must be accounted for.
• Ceramics: Ceramic components require careful inspection for surface defects, dimensional accuracy, and strength. Non-destructive testing (NDT) methods like ultrasonic testing are often used.
• Composites: Inspecting composite materials requires a multi-faceted approach, including visual inspection, ultrasonic testing, and X-ray inspection to detect delamination or voids. Understanding the fiber orientation and resin content is vital.
• Textiles: Inspection of textiles can involve assessments of fiber content, strength, colorfastness, and other quality properties using specialized equipment and techniques.

In each case, the specific inspection requirements are determined by the material’s properties, the product’s application, and the relevant industry standards. My expertise lies in adapting inspection methods to suit these unique needs.

Ensuring the integrity of the measurement process is paramount for accurate and reliable results. Several steps are essential:

• Calibration and Traceability: All measuring equipment must be regularly calibrated against traceable standards to ensure accuracy. Calibration certificates must be maintained to provide traceability to national or international standards.
• Proper Equipment Use: Inspectors must be properly trained in the correct use and limitations of the equipment. This includes understanding potential sources of error and how to minimize them.
• Environmental Control: Factors like temperature, humidity, and vibration can affect measurement accuracy. Controlling the environment where measurements are taken is critical.
• Standard Operating Procedures (SOPs): Detailed SOPs for each measurement procedure must be followed to ensure consistency and minimize variability between inspectors and over time.
• Data Management: Effective data management systems must be in place to ensure that data is accurately recorded, stored, and retrieved. This is essential for traceability and analysis.
• Regular Audits: Periodic audits of the measurement process itself are necessary to verify that it is functioning correctly and to identify areas for improvement. This includes equipment audits and process audits.

For example, in a precision machining environment, the temperature of the CMM room must be strictly controlled to prevent thermal expansion from affecting measurements. Regular calibration of the CMM’s probe and careful adherence to SOPs are also critical.

Conflicts regarding inspection results are best managed through a collaborative and objective approach. Here’s my strategy:

1. Review the Data: Begin by carefully reviewing all the data, including the measurement results, inspection procedures, and any supporting documentation.
2. Identify Discrepancies: Pinpoint the specific areas of disagreement. Are the differences due to measurement errors, interpretation of standards, or other factors?
3. Re-Inspection: If discrepancies are significant, a re-inspection may be necessary, potentially using a different inspector or different equipment.
4. Root Cause Analysis: Determine the root cause of any discrepancies. This may involve investigating the measurement process, inspector training, or equipment calibration.
5. Communication and Collaboration: Open communication is key. Involve all relevant parties – inspectors, engineers, and management – in a discussion to understand different perspectives and reach a consensus.
6. Documentation: Thoroughly document all findings, decisions, and corrective actions taken to resolve the conflict. This documentation should be kept for future reference.
7. Escalation Process: If a resolution cannot be reached at lower levels, escalate the issue to a higher authority for review and decision-making. Pre-defined escalation paths are helpful.

A systematic approach focused on objectivity, clear communication, and documented procedures usually resolves conflicts effectively. The goal is to ensure that the final decision reflects the most accurate and reliable assessment of the product’s quality.

I once faced a challenge involving the measurement of micro-features on a precision-machined component. The features were so small that traditional optical methods were insufficient. The required accuracy was ± 1µm, a significant challenge. We tried using a high-resolution optical microscope initially but struggled to obtain reliable and repeatable measurements due to the challenges of focusing and image analysis with such minute features.

To solve this, I implemented a multi-pronged approach:

1. Investigated alternative measurement techniques: I researched and found that Atomic Force Microscopy (AFM) could provide the necessary resolution and accuracy.
2. External Expertise: We consulted with a metrology expert specializing in AFM to ensure proper technique and data interpretation.
3. Rigorous Calibration and Validation: We implemented a rigorous calibration procedure for the AFM equipment and validated the measurement system using certified standards.
4. Statistical Analysis: We performed statistical analysis on the data to assess the repeatability and reproducibility of the measurements and reduce random errors.

Using AFM, we successfully achieved the required measurement accuracy. This experience highlighted the importance of utilizing appropriate measurement techniques, seeking external expertise when needed, and carefully validating measurement methods before drawing conclusions.