This work explores an extension of ML-optimized piecewise polynomial approximation by incorporating energy optimization as an additional objective. Traditional closed-form solutions enable continuity and approximation targets but lack flexibility in accommodating complex optimization goals. By leveraging modern gradient descent optimizers within TensorFlow, we introduce a framework that minimizes total curvature in cam profiles, leading to smoother motion and reduced energy consumption for input data that is unfavorable for sole approximation and continuity optimization. Experimental results confirm the effectiveness of this approach, demonstrating its potential to improve efficiency in scenarios where input data is noisy or suboptimal for conventional methods.

The oil and gas industry is one of the most dangerous and hazardous industries in the world. With a history of catastrophic accidents and a high rate of fatalities and injuries, the need for effective safety measures is paramount. Industrial automation technologies, such as robotics and artificial intelligence (AI), are being increasingly adopted to improve safety standards and reduce the risk of accidents and injuries. Industrial automation technologies have changed the way we work, with the oil and gas industry making use of robotics and AI to enhance safety measures. With a clear emphasis on improving safety in the workplace, these automation technologies have enabled advanced monitoring, analysis and process automation, eliminating human error and reducing accidents and injuries. Oil and gas production activities are inherently dangerous, with the possibility of explosions, oil spills, equipment failure, and other hazards.

A digital transformation (DX or less commonly DT) is the application of software, programmable hardware, and operational technologies (OTs) to fundamentally transfigure a company's operations and end products for the better. DX programs can be undertaken by industrial organizations, machine builders, or a vast array of other businesses; the involved OTs typically include machine-monitoring systems, connectivity, and online web and cloud access --especially that via tools with internet of things, IIoT, or Industrie 4.0 functionalities. The most successful digital transformations engage every employee at the organization from management to seasonal plant personnel and continually evolve in response to quantified results and personnel feedback. But whether instituted by a team internal to an organization or hired consultants, digital-transformation initiatives can face pushback at established companies -- especially from naturally skeptical engineers. Exacerbating this issue is the way in which products supporting DXs are inherently reliant on the adoption of complementary elements to work … so a given smart sensor (to give one example) can require adoption and integration of dozens of other disparate components and elements to support a grander initiative.

REIKU's Cable Saver Solution eliminates downtime, loss of revenue, expensive cable and hose replacement costs, maintenance labor costs. All of the robots cables and hoses are protected when routed through the Cable Saver corrugated tubing.The Cable Saver uses a spring retraction system housed inside the Energy Tube to keep this service loop out of harms way in safe location at the rear of the Robot when not required. The Cable Saver is a COMPLETE solution for any make or model of robot. It installs quickly-on either side of the robot and has been tested to resist over 15 million repetitive cycles. REIKU is committed to providing the most modular, effective options for ensuring your robotic components operate without downtime due to cable management.

Although factories and processing plants around the world are progressively adopting more automation to improve production, quality and efficiency, these efforts remain largely old-school, using hard-coded automation logic that endures statically once it is commissioned. Continuous marginal improvement is possible through the efforts of hands-on operations personnel, but once a system is in production, it is often difficult to initiate major changes. But what if an automation platform could provide the intelligence and visualization needed to empower operations personnel to make certain adjustments in real time? These improvements could include minor changes, often suggested by data visualization and analytics, or significant changes driven by machine learning-enabled automated decision-making. This would allow organizations to move beyond basic automation and toward a much more advanced state of automation, keeping them competitive.

The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perform tasks such as diagnosis in case of fault. A promising approach to deal with this complexity is the concept of causality. However, most research on causality has focused on inferring causal relations between parts of an unknown system. Engineering uses causality in a fundamentally different way: complex systems are constructed by combining components with known, controllable behavior. As CPS are constructed by the second approach, most data-based causality models are not suited for industrial automation. To bridge this gap, a Uniform Causality Model for various application areas of industrial automation is proposed, which will allow better communication and better data usage across disciplines. The resulting model describes the behavior of CPS mathematically and, as the model is evaluated on the unique requirements of the application areas, it is shown that the Uniform Causality Model can work as a basis for the application of new approaches in industrial automation that focus on machine learning.

The global automotive artificial intelligence market size is projected to grow from USD 2.3 Billion in 2022 to USD 7.0 Billion by 2027, …

Siemens, a leader in industrial automation and software, infrastructure, building technology and transportation, and NVIDIA, a pioneer in accelerated graphics and AI, announced an expansion of their partnership to enable the industrial metaverse and increase use of AI-driven digital twin technology that will help bring industrial automation to a new level. As a first step in this collaboration, the companies plan to connect Siemens Xcelerator, the open digital business platform, and NVIDIA Omniverse, a platform for 3D design and collaboration. This will enable an industrial metaverse with physics-based digital models from Siemens and real-time AI from NVIDIA in which companies make decisions faster and with increased confidence. The addition of Omniverse to the open Siemens Xcelerator partner ecosystem will accelerate the use of digital twins that can deliver productivity and process improvements across the production and product lifecycles. Companies of all sizes will be able to employ digital twins with real-time performance data; create innovative industrial IoT solutions; leverage actionable insights from analytics at the edge or in the cloud; and tackle the engineering challenges of tomorrow by making visually rich, immersive simulations more accessible.

GrAI Matter Labs, a pioneer of brain-inspired ultra-low latency computing, announced that it will be unveiling GrAI VIP, a full-stack AI system-on-chip platform, to partners and customers at GLOBAL INDUSTRIE, May 17th-20th, 2022. "GML's'Life-Ready' AI provides solutions that here-to-fore were simply impossible at such low footprint and power." At GLOBAL INDUSTRIE, GML will demonstrate a live event-based, brain-inspired computing solution for purpose-built, efficient inference in a real-world application of robotics using the Life-Ready GrAI VIP chip. GrAI VIP is an industry-first near-sensor AI solution with 16-bit floating-point capability that achieves best-in-class performance with a low-power envelope. It opens up unparalleled applications that rely on understanding and transformations of signals produced by a multitude of sensors at the edge in Robotics, AR/VR, Smart Homes, Infotainment in automobiles and more.

As everything from our day-to-day activities to manufacturing to consumption has entered the digital age, intelligently automated yet interconnected industrial production--also known as Industry 4.0 and smart factory--is gaining ground. However, given the gravity of this evolution, innovation is key to successfully bringing automation across sectors. In automation and interconnectivity, high-speed wireless communication plays a significant role, as it acts like a bridge between seamless yet scalable connectivity and machines, sensors, and users. It also connects the Internet of Things (IoT), robots, drones, and automated guided vehicles (AGVs). Another benefit comes in the form of eliminating cables from devices with limited mobility.