A prominent global automotive component manufacturer faced challenges in seat inspection due to manual feeding issues, lighting variations, and the need for expensive machine vision expertise. Their solution included integrating AIC technology on NVIDIA Jetson Xavier, a customized software framework, Basler cameras, a user-friendly machine-mounted UI, and Domino inkjet printers. This transformation resulted in 100% inspection accuracy, a streamlined process for capturing images of new seat variants, and remote updates for cost-effective model modifications. The company’s innovative approach to automation and precision has established them as a leader in automotive component inspection
A personal care products factory in Khamgaon, Maharashtra, operated by a prominent company, exemplifies a remarkable transformation in workplace safety. Committed to the vision of zero accidents, the company faced safety challenges in the finished goods area. To address these concerns, they implemented a state-of-the-art safety solution. By integrating Advanced Inspection Camera (AIC) technology with an NVIDIA RTX GPU, a custom Health, Safety, and Environment (HSE) software framework, and high-resolution CCTV cameras, the company achieved 100% violation detection. Immediate alarms and event clips facilitated timely interventions, while digitized monitoring streamlined safety practices. This case study illustrates how digital safety monitoring can elevate workplace safety and align with a commitment to zero accidents, making the facility a safer and more efficient environment for its employees.
A renowned Indian automotive manufacturer, referred to as Global AutoTech, embarked on a journey to elevate its tractor manufacturing division to meet the demands of Industry 4.0. Facing the complexities of inspecting diverse tractor variants and intricate engine components, Global AutoTech initially struggled with traditional inspection methods. However, their adoption of Advanced Inspection Camera (AIC) technology on Nvidia Xavier, coupled with a custom Deep Inspection framework, brought remarkable transformation. With a flawless 100% detection accuracy, the new system eliminated false positives and negatives, making Global AutoTech a frontrunner in advanced manufacturing technology. This case study illustrates how innovation and deep learning can revolutionize quality control in manufacturing, ensuring precision and excellence in the production of tractors and engine parts
A renowned manufacturer specializing in Sheet Metal and Tubular Fabricated Assemblies faced a unique challenge in its manufacturing operations. With over 200 welding robots working in close proximity to human operators, safety concerns loomed large. Conventional safety curtains proved inadequate and led to interruptions due to frequent robot resets. In response, the company deployed an innovative Robotic Perception solution. Leveraging Advanced Inspection Camera (AIC) technology on Nvidia Xavier and a custom Robotic Perception Framework, the system used 3D and 2D camera arrays to track human movements in real-time. This enabled robots to understand human 3D locations, detect human presence in unsafe zones, and pause operations for safety. The result was not only enhanced safety but also reduced downtime, fostering seamless collaboration between humans and robots. This case study exemplifies the transformative power of AI-driven Robotic Perception in manufacturing, making human-robot interaction safer and more efficient
Robotic Guidance for Engine Sealant Application
A leading manufacturer in heavy machinery faced a pivotal challenge in their tractor engine manufacturing plant: achieving precise sealant application along engine block edges. To automate this crucial task, they introduced a robotic system equipped with a nozzle, but the dimensional variations in fixtures and conveyors posed a significant hurdle. In response, the company developed an ingenious solution incorporating a cutting-edge 2.5D profile sensor and custom software. The sensor, attached to the robot's end effector via a motor mechanism, captured precise engine block dimensions. The software then processed this data, solving algebraic equations to determine 3D measurements, which were seamlessly transmitted to the UR-robot for accurate sealant application. This case study showcases how innovative technology can revolutionize manufacturing, ensuring precision and consistency in a critical production process
A prominent automotive manufacturer faced the intricate challenge of inspecting 37 distinct variants of a vehicle model, each with unique front and rear bumpers. With a complex child part assembly featuring 140 inspection points, the company needed a solution to automate variant classification and child part inspection. Their innovative response was a robotic vision system, in partnership with ABB robots. This system seamlessly integrated machine vision, enabling continuous, on-the-go inspections without production line interruptions. To tackle parts with the same color as the background, a structured pattern projector and machine learning pattern recognition algorithm were deployed, drastically reducing inspection time from 8 minutes to just 52 seconds. The system's adaptability, allowing for remote updates to accommodate new models, solidified its role in enhancing manufacturing efficiency and quality control in the automotive industry
A powerhouse in the Indian business landscape embarked on a mission to revolutionize quality control in their premium cookie manufacturing division. Faced with the intricate task of automating cookie inspection for features like circularity, topping concentration, and bake value, they rose to the challenge with ingenious solutions. Their novel inspection system, seamlessly integrated into the cookie manufacturing line, operates at an impressive speed of 360 parts per minute. What truly sets it apart is a pioneering Support Vector Machine algorithm that classifies bake values based on color data, ensuring precision in quality assessment. Moreover, its adaptability allows operators to configure the system for various cookie types. The company's dedication to innovation and quality assurance shines through in this case study, solidifying their position as a leader in the premium cookie manufacturing industry
A family-owned enterprise specializing in precision seal manufacturing undertook a remarkable endeavor to enhance o-ring quality. Amidst producing a significant volume of o-rings monthly, ranging from 2.5mm to 20mm, they prioritized automating inspection processes. Their goal was to ensure o-rings met exacting standards, encompassing dimensions and detecting imperfections like flashes, dents, no-fills, and short molds. Through meticulous development, innovation in optical systems, and the creation of an advanced defect-detection algorithm, the company achieved a remarkable inspection speed of 450 parts per minute. This case study exemplifies their unwavering commitment to precision and innovation, firmly establishing them as leaders in precision seal manufacturing.
Global Manufacturing Solutions, a renowned multinational conglomerate, embarked on a journey to automate the gauging of small yet vital forging components, including connecting rods, crankshafts, and input shafts. The challenge was met with an innovative machine vision system, complete with specialized mechanical fixtures and back lighting. This ingenious solution achieved a remarkable measurement accuracy of 100 microns, setting a new standard for precision in the manufacturing industry. Global Manufacturing Solutions' commitment to innovation and excellence shines through in this case study, reaffirming their position as a leader in diverse industrial sectors, including their recent foray into defense manufacturing Know More
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