Sciemetric Instruments ULC blog

How to use process monitoring to catch early signs of machine wear affecting product quality

Sciemetric Staff — Thu, 01 Feb 2024 05:00:00 GMT

In applications where precision is key, maintaining perfect calibration and a properly functioning machine is critical to ensure product quality and avoid costly amounts of scrap and production downtime. This is even more important in a high-speed manufacturing environment.

Struggling with precision in a high-speed manufacturing environment

Sciemetric had one customer, a leading supplier of rotary cutting dies, that was struggling with the dependability, efficiency, and operating life of its products in the field.

Rotary die cutting is a high-speed and high-precision manufacturing process that is used for applications as diverse as labels, automotive components, and multi-layered parts. The material (which could be a thin metal, plastic, paper, foam, fabric or laminate) is drawn and pinched between a rotating cylinder that contains sharp cutting blades (the die) and a smooth cylinder rotating in the opposite direction (the anvil). The press, which houses the die and the anvil, can be calibrated to cut to depths as fine as the thickness of a sheet of paper.

Maintaining high-speed manufacturing processes with the highest degree of precision was of the utmost of importance for this manufacturer. In some scenarios, it could take only seconds for hundreds of feet of material to be wasted due to poor cuts if the clearance between the anvil and the die was too great or misaligned. They needed a reliable, accurate process monitoring system to catch defects and early wear problems with machines in real time.

Solution: Achieving high-speed, high-precision defect detection with Sciemetric’s sigPOD process monitoring system

The rotary die maker first deployed Sciemetric’s sigPOD on a setup designed to cut and shape plastic cup lids.

Load cells were placed strategically to measure the vertical force between the bearing blocks that support the die and anvil in the frame of the press. As the die and anvil rotate and sheet material was pulled through, the sigPOD captured the force applied during cutting to monitor machine wear, as well as any changes in the die’s alignment or clearance with the anvil during production.

The sigPOD, equipped with Sciemetric’s unique process signature verification technology, performed the calculation to gauge sensitivity. In this case, the load cells had a range of 10,000-lb full scale. The cutting force precision of interest was less than 1 lb—easily measured using the sigPOD.

Figure 1 (below) shows an example of the process signature visualization of the cutting force for the simple star-shaped cutter. Three cuts in fresh material are shown along with three runs without material. This process signature captures and displays the three cutting events clearly as the spike, distinguishing it from the 8,000-lb constant static load of the rotary die press.

Along with clearly indicating when/if contact was made, this measurement would also allow the manufacturer to discern if the die and the anvil were operating with too little clearance. This would cause harder physical contact (force) between the die and anvil than necessary, which could shorten the life of the press components and increase machinery maintenance and replacement costs for the end user customer.

In Figure 2 (below), the 14-lb cut is easily discerned. The repeatability shown, which could also be described as the degree of variance between cuts, is less than 0.5 lb for the three cutting events, well within the manufacturer’s required repeatability range.

Result: Cutting scrap and downtime leads to higher production yield, improved profitability, and efficiency for manufacturer

The sigPOD enabled continuous monitoring and reporting of machine performance in production real-time for true quality assurance. The ability to perform predictive maintenance on equipment also provided the added benefit of improved machine lifespan and decreased replacement costs.

By equipping its machines with data-driven process monitoring using Sciemetric’s sigPOD, this manufacturer was able to ensure the dependability, efficiency, and operating life of its products in the field, securing them an impressive competitive edge in the market.

Interested how the sigPOD could improve your machine performance? Contact us!

How to cut production downtime using data you’re already collecting

Sciemetric Staff — Mon, 06 Nov 2023 05:00:00 GMT

When quality issues halt production, the ability to retrieve and analyze data quickly to trace root cause becomes invaluable. Having a good data management system in place can save manufacturers hours, days, or even weeks in costly downtime—not to mention improve product quality.

In one specific case, we helped an OEM (a manufacturer of agricultural machinery) achieve faster root cause analysis, higher yields, and improved product quality—all by making better use of the data they were already collecting on the production line.

The problem came down to poor data management

When confronted with a quality issue, it would routinely take this manufacturer as long as a week to retrieve all the related station data scattered across the plant to analyze and identify the issue. And while they were searching for the problem, they didn’t want to take the risk of continuing to ship potentially defective products, leading to uncertainty and lengthy (costly) production delays.

Ultimately, their problem came down to poor data management and accessibility across their plant floor. They were collecting data, but it was not accessible in a way that would help them solve their problems in the event of a quality issue.

We identified the below top problems with their data management system:

Data was not accessible in one place, with data trapped in silos across the plant floor
Data was not accessible in one common format, making it difficult to compare
Custom query tools were required for each data retrieval—a timely guessing game
No simple, standardized data reporting and visualization tools

How Sciemetric made better use of their data

Using QualityWorX, Sciemetric’s data management software, our team of engineers implemented the following changes:

Custom scripting to convert data from all processes to one common format
Custom algorithms for easy data retrieval/analysis
Clear, visualized reporting tools
Standardization across the enterprise

Sciemetric’s data management tools are flexible and agnostic, able to communicate with other third-party process and test station equipment and operating systems, which made it easy to connect and collect data from different processes across their production line.

Our engineers took all the manufacturer’s disparate forms of process and test data from the line and converted it into formats that could be uploaded into the QualityWorX database. Data was no longer trapped in silos. With one common database and a common data format, the manufacturer could now pull a full birth history for a part or unit by serial number.

Equipped with a new suite of algorithms to quickly search, retrieve and correlate data from this single centralized repository, the manufacturer’s quality teams were now able to achieve fast and accurate root cause analysis.

The results of these data management updates

After implementing the above changes in their production process, this manufacturer cut substantial downtime from their root cause analysis process. Production and quality issues that once took this manufacturer days or weeks to identify and address could now be resolved in minutes!

Sciemetric’s manufacturing analytics tools have allowed the manufacturer to quickly drill into its data and analyze the impact of design changes, improve quality checks and report on metrics. All this has been achieved from the data the manufacturer already collected—it just needed the right data management and analysis tools to unlock its potential.

This new data management strategy performed so well in this plant that the manufacturer then adopted QualityWorX data management as a standardized quality platform across four plants in North America and Europe. Want to learn more? Download the application note for the full story

Interested in how your production data could be helping your throughput and product quality? Contact the data experts at Sciemetric!

5 ways to make a manufacturing engineer’s life easier

Sciemetric Staff — Thu, 18 May 2023 04:00:00 GMT

Practical ways to use your data to improve quality and response time to daily issues

Manufacturing engineers are relied on to support product quality and throughput on the production line. You are faced with keeping equipment up and running, solving process problems, and lean manufacturing has continued to increase this workload.

Efficiencies are required to keep up with this new demand and learning to make the most of your manufacturing data is critical to solving production issues quickly and efficiently in this fast-paced environment. Here are 5 practical ways to make your life easier, using your data to improve quality and response time to daily issues.

1) Store the Right Data, in the Right Format, in the Right Place

Quickly respond to quality issues with centralized data collection and analysis

There is a lot of data available on a connected production line and many data types. But collecting data just for the sake of collecting is inefficient. Here’s how to strategically approach your data collection:

Collect all kinds of data applicable to your part quality, including waveforms, features, spec limits, part-specific parameters, pass/fail results, images, defect and repair results, and more.
Differentiate between parts so you can reference the information when issues arise. This means storing part data by a unique serial number for each part and having an organized filing structure in your software for quick analysis.
Centralize data collection on your line. Digitize key process variables and store the data through gateways to a central location, allowing for quick data retrieval response to issues. Everything that happens to a part can be stored and is traceable. Data can then be accessed from any location for plant, line, and station comparisons. This also makes for quick compliance reporting.

2) Use Data Reporting to Improve Quality

Identify production and quality issues and solve day-to-day problems

Often Engineers run data reports at the start of their day to get insight into the latest manufacturing issues. With digitized data stored to a central location, you can run useful reports that give you a good view of your whole production line.

Key reports to help you solve problems include the Part History report and Trend report:

Part history report (example) A Part History report shows the life of an individual part as it went through the manufacturing process. It highlights quality checks performed throughout the line and indicates compliance. Ideally, it provides consolidated data, with waveforms, scalars, and images all accessible and comparable in the same place.
Trend report (example) A Trend report is a group of parts based on criteria such as date range or specific models. Trend reports allow comparison of parts and stations to help expose quality and process changes. Here we see all parts in our dataset overlaid in waveform and feature trends quickly identify process anomalies. SPC plots and statistics also provide visibility into quality and process changes.

Watch the webinar to see these reports in action using Sciemetric Studio

3) Optimize Production Using Data to Improve Quantity, Throughput

Identify bottlenecks, reduce cycle time, improve productivity, and speed up line launch

Optimizing production is key to improving the quantity of parts built per hour. This begins with First Time Yield (FTY) or First Time Through (FTT) tracking. This kind of data reporting gives visibility into where the problems are and how many parts are affected, which helps with addressing the biggest issues first. Here’s how you do it:

First Time yield (FTY) tracking provides a key measurement of line and process health indicating bottlenecks. Reducing bottlenecks improves line efficiency. The FTY or FTT report highlights the stations that contribute to the manufacturing bottlenecks. You can drill down to specific parts that failed each station and get better visibility into rework and re-test to improve production yield. These improvements can add up to significant cost savings per year.
Comparing stations can highlight process issues such as bad seals or test pressure variation, which can be fixed to ensure optimized cycle times.
Setting tighter test limits can also improve cycle time and just result in overall better defect detection. Ideally, you can review and analyze your production data offline to see how new test limits would affect your reject rate and Gage R results, without affecting live production.
Accelerate line or station launch using data-driven insight. Running a data simulation for quick re-analysis without running more production cycles, or using existing test data, can help you model new feature analysis, set test limits, and confirm station performance and accelerate development of a successful test strategy. This can reduce time and rework and allow Manufacturing Engineers to focus on improving the manufacturing process.

Watch this webinar to see how Sciemetric Studio Studio reporting allows for process optimization without affecting live production

4) Use Data to Isolate and Quarantine Faulty Products to Improve Response

Identify the root cause of quality problems and isolate defective parts

Some defects are caught at end of line, or worse, once they are in the field. Data can be used to help Manufacturing Engineers quickly determine which parts should be quarantined when a quality spill occurs. The right data strategy can allow quick and efficient response:

Use data comparisons or add a new data feature to isolate the problem and identify which other parts exhibit the same defect. Quarantine bad parts before they leave the plant to minimize the quality spill, and identify any others for recall that may be already in the field. Response time is critical to minimize production delays and lower rework costs.

We have customers who have successfully reduced recalls to a small number of specific parts instead of a complete campaign affecting many customers.

Watch this webinar to see how they did it with Sciemetric Studio

5) Monitor Your Production Data to Improve Defect Detection

Detect defects as they occur and implement continuous improvement

Catching defects in real time boosts line efficiency and reduces rework costs. Data monitoring equipment can analyze data in real time and detect defects as they occur, ideally collecting and storing data to a central location for future analysis. Here’s how you do it:

Isolate key process variables that should be monitored in real time using warranty and quarantine data. Be sure to store data for every part to build process history.
Enable continuous process improvement to advance defect detection and information reporting. The key to continuous improvement is taking time to review and analyze your process data on a regular basis. Review the reasons for your passed and failed parts, and apply different features and test limits in an offline environment to identify problems at stations or inefficient processes to improve.

How Sciemetric Makes Your Life Easier

Sciemetric is here to help you close the information gap between problem to resolution on your production line. Our data monitoring and reporting tools were developed to help you produce high-quality parts with the highest efficiency possible. Let us know how we can help make your day easier.

Using NVH monitoring to identify faulty axle snap ring installations—catch the problems manual checks miss

Sciemetric Staff — Tue, 25 Apr 2023 04:00:00 GMT

Snap ring installation is an important part of axle function in vehicles. If the snap rings on an axle assembly are improperly seated, it can cause vibration issues or even the disconnection of the axle shaft during operation.

The reason why problems with snap ring applications often go unnoticed until the vehicle assembly floor, or a problem in the field, is that the conditions and function of the installation are challenging to monitor without the right tools.

We worked with an automotive manufacturer that was having major problems with their axles, resulting in warranty claims, production downtime, and rework costs. The problem was traced to the snap ring installation station. Sciemetric’s NVH (noise, vibration, harshness) monitoring solution provided them with accurate, reliable snap ring verification, catching any faulty parts before they moved further down the line. Here's how:

Adding automated measurement and analysis to snap ring installation station for consistent, reliable pass/fail results

Many manufacturers rely on manual methods of quality control during the snap ring application process, but the conditions of the process can make it very difficult to get a reliable pass/fail result using manual methods.

The particular manufacturer we were working with was using a combination of machine vision and manual operator monitoring. However, with so many problems being found down the line and in the field, this manual defect detection technique wasn’t working.

This manufacturer’s snap ring installation station had two phases of operation. The first phase of the station was externally visible and often (though not 100% of the time) able to be monitored using a machine vision camera to confirm correct placement. However, the second phase included snap rings securing the inner bearing race to the axle shaft and the outer race to the housing. In both cases it is impossible to insert a camera, making machine vision unusable. Instead, they had a human operator manually listening to confirm the two ‘click’ events as each snap ring was seated into place.

Unfortunately, what made this particularly hard to reliably monitor with the human ear is that these two snap events were happening within a tenth of a second of each other. With the surrounding noise on the manufacturing floor, these two distinct but necessary ‘click’ events of the snap rings seating into place become almost impossible for a human operator to reliably detect.

To fix the manufacturer’s problem, Sciemetric provided a way to take out the guesswork for the second phase of the operation, adding automated measurement and analysis to snap ring installation station for consistent, reliable pass/fail results.

NVH monitoring and signature waveform analysis provide accurate, reliable pass/fail results

Sciemetric’s solution was to measure press and vibration during the snap ring application processes, allowing the manufacturer to reliably detect the audible ‘clicks’ as each snap ring engaged into their proper seated position. Sciemetric used a draw-wire distance sensor and mounted accelerometer to measure the events. Using Sciemetric software, this data was then processed into signature waveforms for simple visual analysis (see screens below).

The two screens above show how clear it is to identify each of the required “click” events using signature waveform analysis. The screen on the left highlights the first click, and the screen on the right highlights the second click. These screens also illustrate how close together the two separate click events occur (within a tenth of a second), making manual analysis very difficult or unreliable.

By comparing signature waveforms of known bad parts to those of good parts, it became possible to match up the precise timing of good vibration events. Seeing the right waveform spike (‘click’) at the right place/time during the process confirmed it was a properly seated snap ring. NVH monitoring combined with signature waveform analysis provided an accuracy that manual verification methods couldn’t deliver.

Sciemetric’s solution enabled a consistent, reliable pass/fail reading at the station and offered the additional ability to better identify, analyze, and solve future problems that could arise.

To learn more about how Sciemetric solved this manufacturer’s problem with snap ring verification, read our application note >

Enabling non-destructive testing in medical device manufacturing with in-process testing

Sciemetric Staff — Tue, 14 Jun 2022 04:00:00 GMT

Historically, medical products have been manufactured in batches or lots, with a sample of the devices evaluated for quality using destructive, visual, or audit testing. More recently, however, we are seeing a paradigm shift.

With more and more medical device manufacturers taking advantage of the benefits of measurement and data management technologies on their lines, the result is a movement away from destructive end-of-line sample testing and towards non-destructive testing systems. Medical manufacturers are realizing the benefits of using in-process testing and process monitoring to catch production defects in real-time, on a part-by-part basis.

By analyzing the data gathered from your critical-to-quality processes, it becomes possible to develop a detailed understanding of the underlying physical processes, and how these process variables interact to affect product quality. This approach provides a number of key benefits, including reduced product costs and improved manufacturing efficiency, as well as improved product quality, traceability, and risk mitigation.

CONTACT US TO ENABLE NON-DESTRUCTIVE TESTING ON YOUR LINE >

Implications of the Destructive Testing, Sample Testing Approach

As mentioned above, the traditional approach to ensuring product quality has been based on testing a statistically representative sample of parts from each batch. Tests are designed to evaluate whether the samples meet product requirements, both from a function and performance perspective, as well as for reliability and durability. However, due to the nature of this testing, many parts are destroyed during this process.

Based on the results of these sample tests, the failure rate of the remaining parts is statistically estimated. If the estimated failure rate is above acceptable limits, the entire batch is removed from production, quarantined and, in many cases, scrapped.

The effectiveness of this approach relies strongly on how well the process is controlled, as it is assumed that the failure rate of the test sample is representative of the entire lot. The less controlled the process, the larger the required sample size, and the higher the cost of the testing. This includes the labor and capital costs associated with the performance of the test, as well as the cost of the sample parts that are destroyed in the test, the vast majority of which could be defect-free.

However, though we know that a well-controlled process is critical to controlling costs, unfortunately, without a means of directly monitoring the performance of the manufacturing process, maintaining this level of control is very difficult and prone to subjectivity, making this test approach problematic.

Benefits of a Non-Destructive Testing, Data-Enabled Approach (In-Process Testing & Monitoring)

The alternative to destructive sample testing at the end of the line is to collect data during your critical-to-quality processes. This represents a fundamentally different approach to managing risk and instills quality directly into the manufacturing process. Instead of relying on batch sample data, each part is evaluated individually, increasing the test coverage to 100%.

Since data acquired during manufacturing can be correlated to the precise step where the defect was created, this approach ensures that defects are caught before they get to the end of the line, when they are most cost-effective to fix. It also ensures that the quality of the manufactured product is controlled and maintained on a continuous, on-going basis.

Also, by consolidating and storing all the in-process test data associated with each part, it becomes a vital component of the device history record.

CONTACT US TO ENABLE NON-DESTRUCTIVE TESTING ON YOUR LINE >

How to Implement Non-Destructive Testing for Medical Device Manufacturing

The first step in implementing an in-process testing approach is identifying the critical steps in the manufacturing processes. For each of the critical processes, you must identify which parameters should be monitored to provide the best indicators of product quality, and then apply measurement tools to feed this data into your system.

By monitoring and analyzing the critical data points in real-time, defective parts are identified and rejected on the production line, before continuing to the next step. This provides the advantage of detecting defects as they happen, allowing defective parts to be removed from the process as early as possible. This improves the efficiency of the manufacturing line, since resources at subsequent steps are not wasted on parts that are already defective. Furthermore, the in-process data provides critical feedback that can be used to control and optimize the manufacturing processes during production.

Once the in-process tests results have been validated against the destructive end-of-line tests, it is possible to eliminate them without any loss of product quality. Instead, quality is guaranteed by monitoring, analyzing, and recording the in-process data on each part as it is being manufactured. This represents a profound change in the way product quality is controlled. The end results are significant cost savings both immediately and in the longer term.

CONTACT US TO ENABLE NON-DESTRUCTIVE TESTING ON YOUR LINE >

Results After Implementing Non-Destructive Testing for Medical Device Manufacturing

When you implement non-destructive testing, no longer is it necessary to destroy 5-10% of the manufactured parts, the vast majority of which would pass requirements. In most cases, this cost savings alone is enough to generate 100% payback within the first year, if not sooner.

Product costs also decrease over time due to continuous improvements on the production line, leading to higher yields. This is accomplished by optimizing and controlling all of the critical manufacturing processes based on the real-time feedback provided. SPC analysis can now be based on individual parts, instead of batch-based data, providing more accurate statistics and more rapid feedback. In essence, this is equivalent to reducing the batch size from thousands down to one.

The operator costs of manual destructive testing are also eliminated. And the fundamental shift away from end-of-line sampling to 100% in-process testing ensures a higher level of product quality, which in the long run is guaranteed to save manufacturing costs overall.

Want to learn how to implement non-destructive testing on your medical manufacturing line? Contact us.

In the end, the combined advantages of a more cost-effective, higher-quality product provide the competitive edge necessary to succeed in today’s competitive medical devices market. Our specialists would be happy to speak with you about any challenges you’re facing and how in-process testing could be implemented for major improvements on your line.

See below, a couple of examples of how we've helped medical manufacturers
eliminate destructive pull-tests on their implanted devices production lines:
⦁ Crimp & Stake Verification >
⦁ Weld Resistance Monitoring >

How to improve defect detection on your assembly line, starting with these 7 common tasks

Sciemetric Staff — Thu, 05 May 2022 04:00:00 GMT

Regardless of where your plant lies on the digital transformation scale, your most essential tool for achieving practical and profitable change is your production data. Below, we look at 7 common tasks required on nearly any assembly line and how you can use your production data to do them better and more efficiently, achieving new levels of product quality and profitability.

Effectively Calculate Yield

When looking at yield, simply looking at production yield can be deceiving. It doesn’t break out the added costs associated with those parts or assemblies that had to be reworked or retested. A production yield of 98% may sound great, but if 10% or more of the parts required some amount of re-work, the average cost to produce each unit may be quite expensive.

Learn why calculating first time yield (FTY) is more accurate, and how to use your production data for significant cost savings each year >

Set Better Test Limits

Setting the right test limits is of the utmost importance for your product quality. And while setting limits often involves and lot of guesswork, and trial and error, this process can be drastically improved using your production data. More accurate pass/fail means you can reduce false rejects, catch actual defects and learn how to avoid them in the future.

Learn how manufacturing analytics software makes it easy to identify where limits should be tightened (or loosened) and to determine what new feature checks may need to be added to a process or test >

Reduce Cycle Time

The need to increase production capacity often forces manufacturers to reduce cycle times at their stations. The tricky part is achieving the careful balance of reducing cycle time while also maintaining the quality and accuracy of the process.

Learn how manufacturers use their historical part data and the right analytics tools to analyze test cycles and determine where time can be saved, with real insight on the trade-offs >

Prevent EOL Failures

Catching failures at the end of the line is a huge drain on overall productivity and efficiency. The sooner you detect and correct a product quality issue, the less disruptive it will be, and the less costly it will be to fix. For overall quality and efficiency gains, your team’s focus must be on more than just a final quality check at the end of the line.

Learn how manufacturers use their data to analyze production and identify additional checks to be implemented throughout the line to ensure issues are caught before the EOL test >

Improve Repair Bays

Repair bay operations can be viewed as much more than a reactive function. An effective repair bay in the modern plant should operate with a two-way flow of data. When part-specific data is available, the repair bay can become a proactive part of the production line.

Learn how to use your production data more effectively, to drive your defect resolution and data management strategy for continuous quality improvement >

Respond Quickly to Quality Problems

What if a defect isn’t caught until after a product is on the way out of the factory or already in the field? With the right data strategy in place, you will be able to respond quickly and efficiently. Using data, we’ve seen customers able to pull specific parts from the loading bay – what they called a “trailer pull” – within an hour of discovering an issue.

Learn how to set up your data management strategy to enable quick investigation and resolution of product quality problems >

Digitize Paper Records

Storing production data in the form of paper and folders poses challenges when it comes to analyzing your production data in the event of a quality problem and for enabling continuous improvement. When records are available in a digital, organized database, the analysis process could take minutes instead of taking days or weeks of sifting through paper records.

Learn how Sciemetric helps manufacturers digitize paper records for greater access to their data and deeper insights to improve production >

Enable these benefits on your line with Sciemetric’s scalable data collection, management, and analytics solutions

Sciemetric has worked with manufacturers all over the world, in a wide variety of industries, to help them make better use of their production data. Sciemetric delivers the insight to conquer your most critical issues and enable continuous improvement on your line.

Looking to solve problems more efficiently, improve product quality, and make your line more profitable? Contact us! We’d be happy to discuss your challenges.

See how manufacturers get real results using their production data

In this e-book, we discuss the above 7 tasks in more detail, and provide case study examples to illustrate steps taken and the results that can be achieved when a manufacturer’s line is equipped to collect, correlate and analyze the right data, to achieve new levels of quality, efficiency and profitability.

DOWNLOAD E-BOOK: 7 common tasks made easier using your part production data

Fundamentals of press-fit monitoring: How to locate defects during press-fit or joining operations

Sciemetric Staff — Thu, 21 Apr 2022 04:00:00 GMT

Press-fit or press force operations are very common on the manufacturing line where parts are inserted or joined using force. While it can be a fairly simple operation, there are a variety of variables that can cause problems during the process, leading to product quality defects. Issues such as incorrect orientation, improper insertion or alignment can cause leaks, unwanted noise vibration or otherwise affect the part’s performance. The goal is to catch these defects as they occur, not further down the production line or in the field, when they are more difficult and costly to fix.

Read on to learn how to use process monitoring matched with data analytics to improve defect detection during press operations.

What to monitor and measure during the press-fit operation

Process monitoring requires that the press station be equipped with the appropriate sensors and the digital process signature analysis software that can interpret and visualize that sensor data.

We can use a load cell to capture the force and a Linear Variable Differential Transformer (LVDT), as seen in image above, to digitize the distance as the ram forces the pin into place. This creates the force vs. time and distance vs. times curves. Combining these, we can create a force vs. distance waveform, or digital process signature.

These checks provide a more granular view of those three primary features: Force vs. Time, Distance vs. Time, and Force vs. Distance. These features tell us:

Force vs. Time: How much pressure and for how long, had to be applied for the operation (or process cycle) to complete?
Distance vs. Time: How far did the component move and how quickly, during the operation?
Force vs. Distance: How much pressure did it require to move the component a specific distance?

Together, these features create a digital signature covering the critical elements of the pressing process.

Analyzing the data to catch defects during press-fit operations

The next step is to understand how the unique attributes of the signature, its shape, correspond to information about the real physical process.

In a press-fit operation that is error or defect free, the values for each of these checks will consistently fall into the same range. When they are visualized as a process signature, the signature will be repeatable. This means that the profile or signature of a “good” press-fit will be as distinct as a fingerprint. It doesn’t matter if the press machine in question is hydraulic, servo electric, pneumatic (air over oil), or mechanical.

Any anomaly will show up clearly in the signature as a deviation from this established norm. That makes it easy to determine accurate pass/fail in real time. Quality issues can be detected and action taken before the production line ends up with a pile of bad parts.

For example, using Sciemetric’s signature and data analysis, a manufacturer was able to detect that the chatter in their press-fit operation was due to spikes in force occurring as the pin rocked during the insertion.

Common feature checks for press-fit defect detection

Here are the common feature checks for press-fit with the defects that any deviation from the norm may indicate:

Zero Force (the mean force before the parts come into contact): Worn or misaligned equipment or a worn load cell.
Alignment Force (measures the maximum force when the parts come into contact and self-align): Misalignment of parts or fixture.
Minimum Insertion Force (measures the minimum force after alignment and before the end of travel): Broken parts or loose-fitting parts.
Maximum Insertion Force (measures the maximum force after alignment and before the end of travel): Tight-fitting parts or debris.
Maximum Force (measures maximum overall force): Improper ram pressure, worn or faulty equipment.
Insertion Work (measures total work applied to press the components together): Out-of-tolerance or broken parts, debris.
Insertion Depth (measures maximum displacement): Out-of-tolerance parts, broken parts, debris or worn equipment.

Using this data for continuous improvement—at the station, and across the line

If you’re finding defects at leak test or end-of-line tests and want to be able to quickly trace the root source of the defect, the collective signature data from your press-fit station can also be archived and further analyzed. Using Sciemetric Studio, hundreds, thousands, even tens of thousands of files can be quickly overlaid to spot trends or patterns that may point to issues that haven’t yet manifested as outright fails or quality problems, enabling continuous improvement on your line.

Want to improve defect detection at your press operation? Contact us! Our specialists would be happy to discuss your application and solutions for improvement.

How to maximize the value of your Statistical Process Control (SPC) data

Sciemetric Staff — Thu, 31 Mar 2022 04:00:00 GMT

A key concern for any manufacturing or quality engineer is keeping the production line running smoothly and minimize the impact of any process deviations. Being able to respond quickly—to put out the fires that inevitably happen regularly—is what manufacturers want from the tools they use.

SPC to monitor trends, provide alerts

Statistical Process Control (SPC) is an industry-standard methodology for measuring and controlling quality during assembly operations. SPC is great at helping to flag an issue, identifying manufacturing issues by monitoring trends in key process variables and can alert plant staff about any operations that are deviating from acceptable parameters—a sign that there may be an issue with the machine that will lead to quality problems.

While SPC is great at finding these deviations, it isn’t easy to find out why something happened and fix it quickly. Some say the data is too limited and doesn’t provide the visibility necessary when trying to dig into the root cause of an issue. For example, SPC would tell you if the standard deviation on the rundown operation on bolt 2 is too high, but not what is causing it. It’s only possible to trace the root cause of a process or quality issue in a reactive and indirect fashion using statistical correlation.

Using Sciemetric Studio to upgrade and enhance SPC capabilities

Sciemetric Studio is a real-time SPC and manufacturing analytics software that bridges the gap between information and action. In addition to monitoring the process, Sciemetric Studio focuses on monitoring the parts being manufactured. In conjunction with QualityWorX, it provides a serial number-based birth history repository that captures digital process signatures, data, test limits, part parameters, defect and repair information, pass/fail results, operation counts, and more.

Having this data means you can see a deviation in SPC and in a few clicks drill down into the affected parts to analyze the root cause of the problem. Looking at the part data for bolt 2 you received an alert on, you could pinpoint where the issue is occurring in the torque process and see, for example, that it is being tightened less than bolt 1.

Using part data in Sciemetric Studio, two rundown stations are compared. Bolt 1 (grey) is being tightened more than Bolt 2, indicating an adjustment is required at the station.

SEE SCIEMETRIC STUDIO IN ACTION! GET A DEMO

Performing real continuous improvement in manufacturing

The benefits of a solution that combines SPC and part data don’t end with fast tracing of root cause. The archived data can be used for further analysis and process improvement so your team can:

Use historical data to see the impact of test changes on quality and cycle time before implementing them
Determine better test limits
Increase first-time yield and cut rework costs
Reduce cycle times
Monitor and manage machine performance
Avoid downtime and boost efficiency
Improve response time to issues
Pinpoint defective units to avoid mass recalls
Drive continuous improvement across the enterprise

See how you can upgrade and enhance your SPC to make it easier to manage quality on your production line.

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How to catch and prevent defects during injection molding

Sciemetric Staff — Wed, 16 Mar 2022 04:00:00 GMT

From bottle caps to toys and complex automotive components, injection molding is a common manufacturing process. Defects and scrap can also be common without appropriate quality checks. How do you ensure a proper mold in production real-time without costly quality inspection delays?

Common problems with injection mold applications

Injection molding can suffer from a number of issues that impact part/product quality. The variance from a good part may be purely aesthetic – such as superficial surface defects that impact product appearance and require additional finishing steps, or that lead to outright scrap. While these can be caught with visual inspection, such inspection methods can be time-consuming, costly, and inconsistent depending on the diligence of the inspector.

Then there are the hazards that may lie unseen below the surface and risk part failure. For example, simple visual inspection can’t easily detect defects such as air pockets, or mold materials that have been compromised by overheating. In these cases, quality issues are more likely to not show up until there’s a warranty claim from the field.

The key to reliable injection molding quality assurance is being able to effectively catch defects before they leave the factory, or even better, before they leave the station or process in which a quality problem has occurred. The closer to its point of origin you can detect any defect, the less costly and disruptive to the line it will be to fix.

But how do you apply such real-time quality checks to an injection molding station?

Easy. By adding to the station digital pressure sensors and analytics software that will track the common feature checks that can flag a poor or otherwise defective mold.

The proof is in the production data: Monitoring an injection nozzle can highlight many problems

With the addition of these tools, the station’s injection nozzle becomes a rich source of real-time production data. Injection pressure, flow, temperature, and vibration can all be captured, measured, and visualized to provide the station operator with quick insight to detect the most common injection molding problems, such as:

The above shows a sigPOD PSV™ (process signature verification) software screen, using the Injection Mold template, designed to monitor the injection molding process. Warping
Sink marks
Voids
Part shrinkage
Brittle parts
Poor appearance
Mold and machine nozzle wear
Debris or hardened material

Case Study: Automaker turns to Sciemetric to tackle its injection-molding issues

Take this example, from the plant floor of a global auto maker that worked with Sciemetric to tackle quality issues with its injection-molded car bumpers.

The injection molding operation for bumpers and other plastic parts didn’t always produce at the desired level of quality. In addition, the mold machine would shut down and stop production due to unexpected machine component failure.

The automaker lacked the data or insight to determine why these issues were occurring and how best to address them.

Sciemetric’s data-driven intelligence identifies issues early

We added digital pressure sensors to the injection nozzle of the mold station, connected to a Sciemetric sigPOD set up to measure and monitor clamp pressure, injection pressure, injector position, injector temperature, and cavity temperature. This setup could detect the overheating, air pockets, over/under fill of the mold and other indicators of a flawed process and a defective part.

The sigPOD captures the full digital process signature, or waveform, of each injection cycle, and monitors for any variance outside of the accepted norm for a “good” part. Additional analytics could also be performed offline with our QualityWorX software to proactively review and continuously improve the process.

sigPOD has led to more reliable and consistent defect detection for quality issues such as excessive flash, parting lines, weld lines, shrinkage, warping and sink marks in molded parts. Time-consuming subjective testing has been replaced with hard data and real-time analysis that prevents down-time, cuts scrap rates and improves preventative machine maintenance.

Contact us to learn more

Sciemetric offers a free pre-configured sigPOD template for injection mold monitoring applications. This template is easily customized to get you up and running in record time.

5 reasons to choose Sciemetric for machine retools, expansions, retrofits, and upgrades of your test stations

Sciemetric Staff — Thu, 24 Feb 2022 05:00:00 GMT

Is it time to update your test station to improve quality and efficiency? Sciemetric offers turnkey on-site and off-site retools, expansions, retrofits, and upgrades. We work with what you have to find the most efficient and economical way to deliver on your requirements and implement advanced in-process test and manufacturing data solutions.

Whether you have an older system that needs to be retooled for model year upgrades, an expansion required to add a new test into your line, or upgrades needed to bring aging or obsolete controls and mechanical systems up to specifications, Sciemetric offers flexible, customizable services and solutions to meet your needs—without having to do a costly rip-and-replace on the line.

Learn more about our machine retool, expansion, retrofit, and upgrade services >

1. We are dedicated to your success, provide full support

At the beginning of the process, our team will sit down with you to understand your goals and requirements. The extent of the consultation phase will depend on your specifications and requirements. Our in-house engineers can design and develop the specifications for your project along with your team as needed.

Once you have signed off on the build, our team delivers the new system to your facility, completes the installation/integration, and performs final runoff. Our team ensures that this is an efficient process with minimal to no negative impacts to current production.

We cater our post-production support to ensure your team is comfortable and satisfied with the new system. Post-production support includes training for your team, final documentation, and final project sign-off. After sign-off, our support team remains at your disposal for questions or further consultation.

2. Process monitoring, measurement, IPT experts

Process monitoring and measurement is a core specialty at Sciemetric. Our products are built to enable visibility into every moment of production, enabling the best root cause analysis capabilities possible. We can apply measurement tools to nearly any application, granting you access to the quality insights you need.

We can monitor virtually anything and work with virtually any technology you may have on your line.

Experts in manufacturing process, in-process testing

Sciemetric is the industry expert on part and components manufacturing processes and in-process testing. This expertise enhances all of our offerings, as we have a deep understanding of how the manufacturing line works and where problems are most likely to occur. We then apply measurement and alerts at these stations to make sure faulty processes are identified before they move further down the line.

We can measure and monitor nearly any process on your manufacturing line, including metrics such as: temperature and humidity, force and distance monitoring, torque/torque-to-turn, noise and vibration (NVH), profile, vision, and more.

The result? An efficient production line, a healthy bottom line, and an end product you can count on.

3. Manufacturing data analytics experts

Sciemetric is a specialist in data acquisition, data management, and data analysis from part production processes.

It starts with data acquisition. Whether it is building data acquisition into a new system or applying modern data technologies to an older system, we help you access the insights you need to ensure quality products are leaving your line and that you can continuously optimize your processes.

Then we deal with data management and analytics. Looking to gain more insight into the processes on your line? Our data solutions are flexible and scalable to your needs. With our QualityWorX suite of data management and analytics tools, we can enable real-time data insights and alerts, visualized defect detection, and continuous improvement at one station, or across a production line. Our team can help you determine the best approach for your line.

4. All electrical, mechanical, and controls design capabilities in-house

Sciemetric offers design, build, integration, and support, all under one roof. We have specialized in-house electrical, mechanical, and controls design capabilities to deliver projects quickly and efficiently, including providing 3D drawings.

We usually build on-site at our ISD design lab and integration assembly and runoff area in Rochester Hills, Michigan, USA, or at one of our other global facilities, including runoff with the customer to confirm proof of process.

We also offer custom fixturing, connectors, and seals, designed in-house to provide the best test results for your applications as needed. When a custom fitting is required, we work with your team to design a solution that will deliver reliable test results, as well as meet corporate ergonomic requirements, etc.

5. Proven expertise

Our ISD (Integrated Systems Division) team has done thousands of projects across the globe, with local delivery, installations, and support. Our projects have ranged across various industries, including automotive, EV, off-highway, medical, HVAC/refrigerants, appliances, consumer products, and more.

We have performed machine retools, expansions, retrofits, and upgrades for a variety of applications. We can work with virtually any application or technology you may have on your line to make sure it is running the way you need it to, and you are receiving the data insights you require.

We are also proud to offer best-in-class delivery times due to direct contract with our customers.

Have an upcoming project requiring machine retools, expansions, retrofits, and upgrades of your test systems? Contact the specialists at Sciemetric >

Learn more about our machine retool, expansion, retrofit, and upgrade services >