Fictiv's robust digital supply chain infrastructure is growing every day. We have a highly vetted network of US-based additive manufacturing partners like Jabil Additive that enable customers to get the highest quality 3D printed parts delivered quickly
The conventional deep learning model is a supervised model. It takes months of time to develop and train the model before it is ready for the production line. Here, Karina Odinaev explains why the conventional deep learning model is broken and what the alternatives are.
By leveraging Generative AI, manufacturers can initiate a wide array of use cases in areas such as customer service, product and quality control, supply chain, logistics, predictive maintenance, marketing and sales, finance, compliance and legal, and research and development
AI can be programmed to learn from the data ERP software gathers to make deeper and more accurate predictions regarding customers, buying habits, inventory levels, markets, material purchasing and more.
By integrating AR/VR (augmented reality and virtual reality), robotics, machine learning, and AI (artificial intelligence), AaaS solutions automate repetitive tasks, optimize processes, and facilitate data-driven decision-making.
Board members and C-suite executives are asking when AI pilot projects can begin, with a preference for a start date of yesterday. Meanwhile, the plant floor reality is nowhere near ready for AI.
In this blog article, we delve into how AI can counteract inflation, offering robust solutions to the complex challenges of today's manufacturing industry.
Data Guys Versus Production Pros - Who is the Best Person to Train AI Programs for Defect Detection?
Artificial intelligence has the potential to deliver game-changing results for quality inspection and defect detection. However, until now the process of training the model has proven problematic.
Artificial intelligence (AI) can help manufacturers handle quality control while managing larger volumes. Here are some compelling reasons people use visual inspection AI tools in modern production facilities.
Our goal is to simplify AI so organizations can start deploying new technologies to save time and money. What we offer is a platform of AI and vision-based inspection and traceability apps that are easily customized
Now more than ever, manufacturers are integrating new technologies, such as IoT, cloud computing and analytics, and AI and machine learning into their production facilities and operations.
Artificial intelligence applied to designs of additively manufactured products goes beyond standard optimization to produce dramatic quality and performance gains in much less time
This piece delves into Vision AI's pivotal role in reshaping safety paradigms in manufacturing. But before we dive in, let's first decode the workings of Vision AI.
Manufacturers are adopting AI/ML to increase efficiencies and cut costs. Using AI/ML to monitor and analyze plant operations reveals production chokepoints, potential equipment failures, and areas where automation can streamline operations.
Every manufacturing operation needs an efficient and resilient supply chain. Stock shortages, shipping delays and similar disruptions can prolong production, raise costs and impact customer satisfaction, so manufacturers must prevent them as much as possible.
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An effective, reliable and efficient drive system is essential to any material handling equipment, whether it's a motor-assisted tow truck, a barrel lifter, a robotic vehicle or a complete high-capacity parcel sorting hub; Parvalux designs and manufactures an exciting range of AC, DC brushless and brushed motors and our drives are valued for their performance and reliability.