SIs in this field can help customers determine exactly the right system for their needs including the best lighting strategy, lens selection, camera selection, and equipment arrangement.
In addition to selling directly to end customers or OEMs, many AI-enabled machine vision providers work with systems integrators (SI). SIs in this field can help customers determine exactly the right system for their needs including the best lighting strategy, lens selection, camera selection, and equipment arrangement.
Shweta Kabadi
Senior Director
and Business Unit Manager
Vision SW and Accessories
Cognex
“During the initial assessment phase, the integrator should develop a thorough understanding of the client’s vision needs, including unique part and process variations and precise defects that need to be detected,” said Shweta Kabadi, Senior Director and Business Unit Manager of Vision SW and Accessories at
Cognex. “It is important for the integrator to fully understand exactly what the customer is trying to achieve, including any custom specifications and pass/fail rate success criteria. Integrators should also provide value by offering long term consultation with the end customer to make sure the vision system is running at maximum efficiency and with the most appropriate technology.”
Rick Brookshire, Director of Product Development at
Epson America stressed the importance of
AI-based algorithms in vision systems to help improve precision, defect detection, cycle time improvements and how SIs can leverage this.
“SIs continually [need to] push the envelope for faster cycle times and more reliable results,” Brookshire said. “Many factories today require auditing of quality which can translate to recording every placement, every vision result and much more. AI helps improve this quality and performance which helps make systems integration jobs easier at a time when they are expected to deliver faster, more precision solutions.”
Know the verticals to target
AI-enabled cameras are used in virtually all manufacturing industry sectors. For SIs, a deep understanding of the different verticals, and the potential of machine vision in each of them, is critical to success. Below are a few examples of how these cameras are used in different industries, according to Kabadi.
Logistics: This is the fastest-growing industry for machine vision technology. The explosion of eCommerce has driven retailers to use automation and technology to fulfill customer orders inexpensively and quickly, often within 24 hours. Vision is increasingly used in retail distribution warehouses for package dimensioning, damage inspection, looking for hazardous labels and identifying other defects on packages.
Automotive: From assembly to final inspection, nearly every system and component within an automobile is manufactured with machine vision and barcode reading technology. AI-enabled cameras can be used to guide robots to pick parts out of racks, verify that they are the correct parts, inspect the parts for quality, and then position the parts for assembly.
“With advancements in artificial intelligence, many manufacturers are also leveraging deep learning-based image analysis software to solve complicated part location, cosmetic inspection, and classification challenges,” Kabadi pointed out. “Specific applications within this industry include tire and wheel systems, safety systems, powertrain systems, chassis systems, and electronic systems (ex. battery inspection).”
Consumer Electronics: Some of the most advanced applications for the technology are in the consumer electronics market. Specific applications within this industry include display manufacturing, mobile, and wearable device assembly, and OEM and machine building (ex. OLED gauging).
Consumer products: The consumer product and packaged goods industry demand high throughput, cost efficiency, and accuracy for its material handling, inspection, labeling, and assembly needs. Machine vision is used to help manufacturers deliver the highest level of performance in product safety, product quality, and productivity improvements for consumer-packaged-goods (CPG) manufacturers and the machine builders and SIs that serve them. Specific applications for machine vision within this industry include material handling, automated assembly, packaging, filling, labeling, and marking and distribution.
Food and beverage: Successful food and beverage operations embrace innovations in product quality inspection, packaging inspection, assembly verification, allergen management, traceability, and food safety to minimize downtime and deliver consistently high quality, safe products with fewer defects and less waste.
Machine vision systems and industrial barcode readers offer powerful solutions to protect product quality and safety, ensure package integrity, manage allergens, and maintain traceability. Specific application examples in this industry include product quality inspection, packaging inspection, assembly verification, allergen management, and traceability, and warehousing and distribution.
Pharmaceutical/medical: The need to comply with patient safety and traceability requirements in a cost-effective way is a major business driver for
pharmaceutical, medical device, and bio-science product manufacturers. Specific application examples in this industry include ensuring package integrity, tracking serialized products from manufacture to patient, or ensuring label accuracy with barcode and text verification.
Life sciences: OEMs in this industry rely on machine vision and automatic ID solutions to meet the demanding requirements of their customers and deliver accurate, reliable, and high-performance machines. Specific applications in this industry include IVD lab automation, microscopy, medical imaging, and automatic identification.