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A Perspective of the Manufacturing Future: Product Quality

Moving data from silos, connecting as-manufactured product data, and ensuring quality are just some of the ways IoT is changing manufacturing.

· IoT Zone

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This is the fourth post in a series where I’m presenting my perspective of what the future has in store for various manufacturing topics. I’ve tried to paint a picture of what is possible if we were to fully utilize what we know to be technically possible today.

To date, I have discussed a futuristic system for handling cutting tools in machining operations, a fully integrated and intelligent production scheduling system, and a system for maintaining low inventory levels.

In this post, I’m going to describe a vision for a future for product quality. In this future state:

  • A product’s manufacturability will be known during the design stage.

  • As-manufactured product data will be shared between manufacturing operations to reduce non-conformances.

  • Manufacturing process variability will be monitored and controlled to minimize lost productivity.

  • As-manufactured product data will be utilized to optimize product performance for customers.

The Current State of Product Quality

Today, data exists in silos. In many cases, the useful information is in the heads of a few key individuals who may not know or communicate with each other. Often, product designers have had limited experience in manufacturing — their primary concern is product performance, and they have no good way of assessing manufacturability or manufacturing costs.

Within manufacturing, some amount of process variability and drift is always present, but when an operation is developed, it is assumed that the incoming parts are in the nominal state because, typically, there is no sharing of as-manufactured product data from one operation to the next.

Additionally, the control of the process is manual. A person will review the as-manufactured product data and decide if an adjustment is needed and how to make the adjustment based on experience. This person may make a decision using limited data from the history of the process, they may make an error in their decision process, and they may make a mistake when performing the adjustment.

Typically, a part has a few critical specifications for the product that ultimately goes to the customer. However, quality checks are performed throughout the manufacturing process in order to catch non-conformances at the source and before extra processing costs are incurred on a part that would be scrapped. Specifications are then created to control the quality of intermediate steps and quality checks are performed. Those specifications have an acceptable range associated with them. The stack-up of the variability from each process step can cause the end-product to be out-of-tolerance, even though the process-steps are acceptable. Or, the specifications on the intermediate steps are very conservative, ultimately leading to higher production costs. Each process step is developed assuming that a nominal part was created at the prior step.

After the product is manufactured and the customer is utilizing it, there is a limited amount of data that returns to the producer regarding how products are used and, if they fail, the root cause of the failure.

The Future of Manufacturability

In the future, as designers are creating 3D models of products in CAD systems, they will be presented with information regarding the manufacturability of the features and products being designed. The quality data from the manufacturing operations inside the factories will be correlated with product materials, feature shape, and tolerances.

When similar materials are used, features created, or tolerances associated with dimensions in the design space, notifications will be presented to the designer to inform them of the capability and costs of existing processes to create similar features on existing products. This will allow designers to make better-informed decisions regarding whether or not existing manufacturing processes are able to produce the part/features as designed. Coupled with an assessment of customer value for the features and specifications, they will be able to make an informed cost-benefit assessment.

The Future of Manufacturing Quality

In the future, as product is moving through the manufacturing process, the data measured after each operation will be utilized in subsequent operations to improve the product’s end-quality. Typically, a part has a few critical specifications for the product that ultimately goes to the customer.

However, quality checks are performed throughout the manufacturing process in order to catch non-conformances at the source and before extra processing costs are incurred on a part that would be scrapped. Specifications are then created to control the quality of intermediate steps and quality checks are performed. Those specifications have an acceptable range associated with them.

The stack-up of the variability from each process step can cause the end-product to be out-of-tolerance, even though the process-steps are acceptable. Or, the specifications on the intermediate steps are very conservative, ultimately leading to higher production costs. Each process step is developed assuming that a nominal part was created at the prior step.

The measurements from prior processing steps will be used to adjust downstream processing steps to ensure that each step is aligned with producing products that meet the critical requirements of the product that goes to the customer. This will allow more flexibility in upstream processes because the in-process tolerances will not need to be as conservative.

If customer requirements drive more stringent tolerances, this capability may enable existing equipment to meet the more stringent requirements. This could apply beyond dimensional measurements.

For example, material composition or hardness, measured at the foundry, could be used in subsequent machining operations to optimize speeds and feeds on a per-part basis. As an individual operation output begins to drift or shift, the process control system will automatically adjust the operation to ensure that it stays in control.

Systems exist for this today, but they rely on simple rules for making adjustments. For example, if a dimension is trending high or low, then an adjustment will be made to the machine to compensate. However, in relation to complex parts, dimensions have multiple relationships to other dimensions and adjusting one may have a negative impact on another.

Future systems will utilize artificial intelligence to recognize the relationships between dimensions and make appropriate adjustments to processing equipment.

The Future of Life-Cycle Quality

In the future, the customer will monitor products while in use and the data will be used throughout the entire business for improving design, production, and end-use. Monitoring the quality of a product’s performance will have benefits for the customer with regards to predictions associated with product malfunctions and product downtime. This will lead to better scheduling and parts inventory management for the customer.

Other benefits will come from a databased understanding of how the customer utilizes the product. This knowledge will drive further product development, which is more focused on customer uses. The data from real usage and any failures will be analyzed and correlations with manufacturing quality data will be analyzed to alert factories of issues.

In this post I’ve explored a few possibilities of how quality data will be utilized in the future. It will permeate throughout the product’s entire life cycle. It will enable design to create more producible parts, manufacturing costs will decrease, and customer data will complete the loop for improved future products.

In my next post I will describe the value behind each of these futuristic scenarios.

The IoT Zone is brought to you in partnership with GE Digital.  Discover how IoT developers are using Predix to disrupt traditional industrial development models.

Topics:
iiot ,manufacturing ,quality assurance ,iot

Published at DZone with permission of Andy Henderson, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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