Guest post by LiveWorx Sponsor Dimensional Control Systems

The Industrial Internet of Things connects design and manufacturing to give true production visibility

The Industrial Internet of Things (IIoT) is machines talking to machines. This is the application of the Internet of Things for manufacturing. It takes the human element out of the middle, allowing for automated systems to communicate with one another and deliver valuable information for making key decisions regarding quality, manufacturing and design.

No Such Thing as Perfect Parts

When manufactured, parts are never made to exact specifications. Variation caused by material characteristics and manufacturing processes, like stamping and machining, causes parts to be made larger or smaller than their nominal design specification. This variation is defined in design as tolerances, representing the range of variation acceptable to function.

Tolerance Analysis in Design – Quality Improvement

Tolerance analysis is the name given to a number of processes used to determine the overall variation and effect of variation on products stemming from imperfections in manufactured parts and variation in assembly process.

As part of the tolerance analysis process, both original sources of the variation, as well as a stack-up, that is, the combined variation of all parts in a given assembly are determined. By analyzing the effects of dimensional part and process variation (tolerance analysis), manufacturers can better understand the source of dimensional variability within their design, rooting out potential issues before they show up as; scrap, assembly fit issues and missed customer expectations for dimensional quality.

Once complete, the engineering team sends the designs downstream to manufacturing. Engineering is often disconnected from the manufacturing process and any issues that arise during production.

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Many manufacturers use tolerance analysis tools like 3DCS in Creo to reduce the effect of variation

Manufacturing Quality – Remote Monitoring and Production Visibility

As the Industrial Internet of Things is adopted more widely by manufacturers and OEM’s, QMS – Quality Management Systems – are being implemented to monitor, standardize and report production information from a variety of sources. This once manual process, done with hand measurements and check fixtures, outputting hand written sheets and paper quality reports of listed points and measurements, is becoming fully automated.

The new software systems ‘crunch’ all of these numbers, which are often difficult to process without context or compare to other values and make decisions from, and produce easy to use standardized reports, dashboards and alerts. This gives contextual outputs that can be used to make quick decisions and monitor production quality in real time giving managers visibility across the organization

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Using CMMs – Coordinate Measuring Machines – to take measurements is a common practice

Automated Systems Create Quality Improvement and Production Visibility

This production visibility is key to monitoring data trends in order to determine when a production issue begins and can then be remedied before parts need to be scrapped or the manufacturing lines shut down. By systematically tracking, reporting and monitoring the production process, it can be incrementally improved until it runs smoothly.

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Quality Management Systems offer manufacturing visibility to monitor production quality

Using IIoT to Connect Tolerance Analysis and Manufacturing Quality

As quality management systems collect data and centralize it, companies can utilize this data for other purposes. Having a single source of information that can be accessed across the enterprise gives other staff members in the company access to quality data that was traditionally difficult or time consuming to obtain.

In typical tolerance analysis, CAD models are designed to simulate the manufacturing variation using design tolerances. These represent the design objectives, assumed and historical tolerances from similar parts and products, but not the actual variation being observed on the current parts.

By giving engineers and designers access to the quality system, they can incorporate the actual measurement results and statistical ranges of variation into the simulated models, and update the designs to resolve production issues and root cause manufacturing problems. Utilizing these feedback loops can improve current and future designs, and drastically decrease the downtime of plants that have manufacturing problems by finding the source of the problem quickly, and the solution through simulation.

Simulating Supplier Quality

This process of collecting, centralizing and then distributing quality data works for an organization and their plants, but can become even more valuable by incorporating the supply chain. By including suppliers in the quality management system, organizations can standardize their measurement plans by providing those through the system, and thereby creating a standard for measuring features and locators that allow the organization to analyze the measurement data from the suppliers’ parts before they are shipped, and can address quality issues before parts ship from the supplier.

This also gives the manufacturer a powerful tool for collaborating with suppliers. The measurement plans can contain measurement features and locators, as well as ranges and statistical metric tolerances, effectively communicating both how to measure the part and what constitutes an acceptable or unacceptable part. By negotiating using these plans, the supplier and the manufacturer can discuss using the same data, reducing confusion and ambiguity. Finally, as the supplier quality data is linked to the quality management system, it can be collected and centralized for use by designers and incorporated into executive dashboard summaries.

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Dashboard summaries give up-to-the minute feedback on supplier quality

Closing the Loop on Quality 

Connecting devices and systems using the IIoT creates an interconnected system of automated processes that includes feedback loops of data to validate and quality check along the product life cycle. This promotes a continuous improvement process that can reduce expensive quality issues found in traditional manufacturing processes that end up creating scrap and defects. Manufacturers are using these methods to improve their bottom line and increase profits without having to sell additional product.

Dimensional Control Systems Supports Plm Closed Loop
A Closed Loop Quality approach combines QMS and CAD simulation to improve quality by reducing manufacturing defects

Learn more about how IIot is bringing tolerance analysis and manufacturing quality together at http://www.3dcs.com/automated-spc-systems-qdm.