As a Standard Industries company, GAF is part of the largest roofing and waterproofing business in the world.
GAF is North America’s largest roofing and waterproofing manufacturer and a part of Standard Industries, a global company focused on building materials.
GAF’s products include a comprehensive portfolio of roofing and waterproofing solutions for residential and commercial properties as well as for civil engineering applications. The full GAF offering is supported by an extensive national network of factory-certified contractors. GAF continues to be a leader in quality and offers comprehensive warranty protection on its products and systems. The company’s success is driven by a commitment to empowering its people to deliver advanced quality and purposeful innovation.
Clayton McGratty, VP Corporate Communications , Said:
“GAF never stops researching, testing, prototyping and creating innovative new roofing and waterproofing solutions. With technology advancement at the core of Made Smarter Technology Accelerator, being an Industry challenge owner enables us to drive this innovation even further and explore the opportunities that advanced digital technologies has for our business as well as being at the forefront of industry. As part of Standards Industries this partnership enables us to take part in innovation with the chance to take this into a global scale of adoption.
GAF is the leading roofing manufacturer in North America with manufacturing plants located throughout the United States. Its diverse range of commercial roofing products are sold worldwide, of which a number are reliant on the production and manufacture of asphalt shingles. It is this production process which GAF is looking to innovate through engaging with the Made Smarter Technology Accelerator.
As asphalt is produced through the refining of petroleum, it is composed of heavy, long hydrocarbon chains from crude oil. Historically, asphalt streams relevant to the roofing industry have been characterised solely based upon falling within the ranges of specific physical properties for acceptable use. While various asphalt sources may indeed fall within these ranges and even meet the targets defined, there are inherent differences in how these raw materials are further processed and converted. These differences may be associated with crude slates, which vary by region, different refining techniques or processing capabilities, as managed by the refiners to meet market demand within their product portfolios or as mandated by seasonal operating constraints. During the manufacturing process, inconsistencies between batches of like asphalt streams can pose substantial challenges configuring machinery, resulting in waste, production delays and impact the final product quality.
For this challenge:
GAF is interested in developing a technology-based solution to characterise, identify and understand the critical to quality (CTQ) results and parameters associated with asphalt materials, which provide a durable surface for applications such as roads, pavements and car parks. This will help inform downstream manufacturing procedures and ensure a more dynamic process. The prototype will be used to predict and train models on how the ingredients are varying over time and, in turn, be used to inform modifications to the manufacturing process.
As part of the prototype phase of the programme, Volatile Technologies worked with GAF on the asphalt material characterisation challenge: Adapting existing chemical and olfactory testing instruments to deliver non-invasive, batch-on-batch asphalt profiling, without any additional specialist lab equipment.
The manufacture of roofing products is an integral part of the GAF process. It is a complicated and high speed operation involving many variations. It consists of many attributes that are compulsory to maintaining acceptable product performance where each requires operational management to ensure presence, location, alignment and verifiable dimensional characteristics. Several of these may be impacted by the compounding effect of one variation upon another. Quality attributes include verifying the presence of discreet and continuous colour patterns as well as the capture of any surface anomalies or defects. Employing real time and in-process measurement is highly desirable from a quality, cost and efficiency standpoint.
For this challenge:
The manufacture of high quality roofing shingles is significantly important to GAF in terms of market recognition and reputation. It is a complicated and high speed operation involving many variations. GAF is looking to employ machine vision and sensor technology to assess product conformance to specification; characterising surface anomalies and identifying the process conditions when they occur; and evaluating whether critical machine components are appropriate to continue using.
The desired solution must be able to demonstrate improved performance over today’s human and machine applications in relation to accuracy, repeatability, reproducibility, speed/frequency by using inline measurements at high frequency. The ability to threshold, identify and/or measure critical product attributes in both static mode (discreet unit by unit) and dynamic mode is important. Desired surface patterns are to be recognised and flagged for variation or missing elements and critical attributes are measured within 0.001″ in X, Y and Z dimensions. For dynamic assessment, surface anomalies greater than 1 sq in size to be flagged and identified via a lookup database.
The expectation is to improve product quality and consistency, reduction in the cost of poor quality and in related customer claims. Other expected benefits are eliminating latency associated with manual processes by providing real time information for manufacturing process control and troubleshooting, improved operating effectiveness (OEE & TEEP) and improved operating safety related to machine contact measurement by removing frequent manual and repetitive tasks and positions of people. Finally, to advance the digital capabilities for Smart Manufacturing in the areas of predictive and prescriptive analytics for artificial intelligence (AI) and machine learning (ML).
As part of the prototype phase of the programme, Zeta Motion, with Dakota Systems Inc. worked with GAF on the machine vision systems for product conformance and machine condition challenge: Machine vision and sensor technology to assess product conformance that is fast, accurate, affordable, and scalable.