As growing numbers of manufacturers embrace the principles of Industry 4.0, they are experiencing the benefits of its cornerstone concepts, including system and process integration, information transparency, and smart technology, all of which enable manufacturing across a variety of industries to become more streamlined and cost-efficient. The result is higher productivity and ROI, as well as improved quality control and reduced costs.
This holds true in product development as well. As products are being developed, production teams are looking at the various manufacturing processes and QC systems that need to be integrated to ensure optimal productivity and consistency. Checkpoints established throughout the production process help ensure that any potential problems are caught early and resolved to avoid expensive delays, waste, and rework further down the line.
This can be tricky for products in which surface quality is a critical factor for the use of adhesives, paints, sealants, and other coatings. Structural design and manufacturing process issues may be easier and quicker to identify and resolve, while mitigating circumstances can affect surface quality in subtle but significant ways that may not be apparent until late in the production cycle when they become more costly to correct.
This is why it’s so important for engineers, when designing and manufacturing new products, to determine the critical points throughout the development lifecycle where material surfaces can impact final product performance. Without surface quality at the QA/QC forefront, manufacturers often end up spending a lot of time troubleshooting while creating unnecessary waste and rework.
This can all be avoided by thinking about material surfaces from the very beginning of the product development process and putting in place controls using digital surface inspection.
Protecting Material Surface Quality
From the very beginning of product development, engineers should be thinking, “How does surface quality positively affect product performance, and how do we preserve that?”
Product design is often done iteratively, but sometimes product performance is not clearly defined throughout the process. To help ensure that surface quality is maintained throughout the product development lifecycle, it should be analyzed at any time:
- Materials for bonding are changed
- Adhesives/coatings are changed
- Product performance parameters are revised
Surface quality should always be upfront in the product designer’s mind because it is a key factor in achieving optimal product performance. But in many cases, it’s simply not on their radar screen, to the detriment of the product.
New Technology Selection for Continuous Improvement
A key component of Industry 4.0 is Design for Manufacturing (DFM) which is improving or optimizing a product in the design phase where it can be done more easily and at less cost. The fuel that drives the DFM engine is data, which is needed to identify, analyze, and resolve issues early in the product development lifecycle to prevent bigger problems later when the product enters manufacturing.
Technology selection plays a big role in DFM. Selecting the right tools and integrating them into the production process can make or break product success. For example, if a potential problem with surface quality is identified, there may be several viable courses of action: perhaps a step needs to be added to improve surface prep, such as plasma treatment or improved washing, or a current process needs to be tweaked for higher quality output; a third option might be to change materials.
Appropriate surface quality data is vital for choosing or revising a process. When the choice is influenced by factors such as regulatory compliance or cost-benefit, data not only enables you to confirm that an alternative process is equivalent or better than the current one, but it also helps reduce risk.
Process Design with Virtualization
One of the critical technology tools that are helpful for DFM early-stage optimizing is something called “digital twinning” – the digital replication of the entire production process in a virtual format, creating a digital twin that can be tested and modified as needed to optimize manufacturing. It’s essentially creating a flexible digital version of the factory, enabling you to model production and change elements and parameters to see how it affects manufacturing. Unfortunately, surface characterization is often overlooked, and without that data, surface quality can remain unknown and present a potential stumbling block to producing a consistent, quality product.
To further reduce risk, companies with a DFM strategy will utilize Design Failure Mode and Effects Analysis (DFMEA), a practice used to identify and assess development and manufacturing risks as early as possible so that mitigation can occur before the new product program is too far down the road to keep a reasonable cap on costs.
DFMEA is highly useful for gaining an accurate understanding of critical surface quality that enables appropriate controls to be developed to manage it. By incorporating it early in the product development cycle, design risks are identified, which, if left unattended, could result in failure. DFMEA is applied when:
- There is a new design with new content
- There is a current design with modifications, which also may include changes due to past failure
- There is a current design being used in a new environment or change in duty cycle (no physical change made to design)
Current risk models around cleanliness and surface prep are generally vague and incomplete. They might identify a need for a surface to be “shiny and smooth,” but they lack specific, qualitative, and experience-driven specs that enable a surface to be accurately assessed. The results can be deceptive because only the need for certain surface characteristics is identified, not what those specific characteristics are. Simply identifying the need for a surface to be “shiny and smooth” is not useful. Stating instead that a surface needs to have an average surface roughness (RA) value of X is. Once again, producing accurate surface-quality data is critical.
Surface quality data drives decision-making throughout the product development and manufacturing lifecycle, including:
Prototyping New Products
Surface quality is important in prototyping because how that prototype performs will drive design decisions. Unfortunately, the uniqueness of prototyping can be challenging for reliably transitioning surface quality from prototyping to production because the tools used to create that specific surface can be very different in those two environments. For example, washing a prototype part in a beaker is not the same as using an industrial-stage cleaning process found in a manufacturing environment. Accurate surface data is critical for making design decisions at this stage, which can have a significant impact on product quality and the cost of manufacturing.
When design engineers are faced with substandard prototype performance, incorrect decisions can be made about the root cause of the poor performance without crucial surface quality data. For example, a barcode scanner customer of ours changed one of the substrate materials used in a popular product already being produced. They built a prototype, tested it, and it failed product performance. Because they did not have surface quality data, they did not know if the poor surface quality was the root cause and instead started looking at how they could change the product form factor to reduce stress. Changing the form factor could adversely affect the user experience and jeopardize sales of the product, all because they lacked key surface data.
Process Design and PFMEA
One of the most crucial objectives in Process Failure Mode and Effects Analysis (PFMEA) is maximizing process reliability and appropriately assessing the risks of a process not performing as designed or intended. However, in our experience, process engineers do not fully appreciate the risks associated with bonding, coating, cleaning, or painting, which are often more significant than they realize, and failures occur more often than they think. In reality, there is a critical need to monitor surface quality and put production process controls in place to gain insight into the occurrence and severity of surface quality issues.
This is the step in which the process design is transitioned to the plant floor, and the product begins to be manufactured at a speed, quality, and cost that is economical. Any bugs left in the system will shake out here. This can be very stressful for the product design team, which may have to make disruptive design changes to the product to help resolve any remaining manufacturing process issues. At this point, they cannot make form factor changes but can make other changes to improve the manufacturing process.
Often the product does not meet one of the three defined criteria of cost, speed, or quality. As a result, the manufacturing process may be tweaked to improve one of those three without affecting the other two. If product quality needs to go up to meet specifications, it needs to be done without increasing the cost. If there are performance issues related to bonding or painting, or coating, without surface quality data, you won’t know if you need to make improvements to the surface or process. Or, you won’t know if changes you make to improve performance will increase costs. If a process costs too much, you want to know if it changes surface quality. By making changes in one of the three criteria, you can negatively affect the other two. Accurate data can help resolve these issues more effectively and cost-efficiently.
Prior to product release, testing is generally done in-house using a surrogate to replicate and validate the user experience. Sometimes, however, surrogate testing fails in some way, and a less-than-optimal user experience results. Appropriate surface data can reveal if the problem results from the poor coating, bonding, or cleaning.
Component sourcing can be problematic, especially if different vendors are used for any reason. For example, different sub-components might be used for the test build and scaled-up production. If you suspect changes in quality, having accurate surface data can help determine the differences and inform any decisions about necessary adjustments.
Process Control and Optimization
Process control at this stage is primarily to prevent defect escape and significant production disruptions. Predictive indicators can help guide adjustments, and corrective actions before defects develop and escape your process. In-process water contact angle data is a fast, sensitive, non-destructive, and easy data stream for assessing and optimizing printing, cleaning, coating, bonding, and other manufacturing process quality.
Despite the backseat that surface quality often takes, it is a critical tool manufacturers can use to streamline and optimize product development and production. Accurate, timely surface quality data helps drive continuous improvement and cost-efficiency.
Properly implemented, surface quality data monitoring and analysis can enable cost-savings, process optimization, improved product performance reliability, and faster time to market.
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