Field Service Engineer - High Voltage Business

4 legged robot that can climb ship hulls for welding & painting built by Kawasaki shipyard. Latest videos. Japanese manufacturing firm Kawasaki Heavy Industries has unveiled a four-legged robot that can climb ship hulls and carry out welding work. The company is developing the robot as Japan's shipbuilding industry faces a shortage of skilled workers, particularly experienced welders. The prototype was shown to the public for the first time on October 6 during an update on Kawasaki's Group Vision 2030 strategy. The company plans to begin testing the robot at its Sakaide Works in Kagawa Prefecture, Japan, in 2027, with practical use targeted for 2028. The robot uses physical artificial intelligence (AI) to move across vertical and curved ship surfaces and decide how much welding is needed. Kawasaki aims to automate tasks that are difficult to carry out using conventional equipment. The prototype measures 60 cm by 44 cm by 29 cm and uses electromagnets to attach itself to a ship's hull. Cameras and sensors allow it to inspect weld seams and determine how much welding is required. Shipyard workers usually use high-reach vehicles to weld and assemble large prefabricated sections. Kawasaki's robot is designed to work directly on the hull, including areas that are difficult to reach. The company also hopes the technology will help workers carry out shipbuilding tasks during high summer temperatures. Kawasaki Heavy Industries President Yasuhiko Hashimoto said the number of people working in Japan's shipbuilding industry was continuing to decline over the medium term. He also pointed to the difficulty of passing skilled workers' knowledge and techniques on to younger generations. The company plans to use the experience of veteran workers to develop physical AI systems that can control robots and carry out tasks autonomously. In July, Kawasaki announced a partnership with US chipmaker NVIDIA to combine its shipbuilding and robotics expertise with physical AI, digital-twin and simulation technologies. The programme aims to connect vessel design with production at the Sakaide facility and increase the use of robots and autonomous systems in ship construction. Kawasaki has also said it hopes to extend physical AI beyond shipbuilding to vessel operations, maintenance and servicing. These plans form part of what the company calls a "next-generation digital shipyard". Other Japanese companies are also developing physical AI technology. Hitachi is working with industrial robot maker Fanuc to develop machines for manufacturing, logistics and agriculture, with deployment planned from fiscal 2027. Kawasaki plans to begin demonstration testing of its shipbuilding robot in 2027 before targeting practical use in 2028. References: portnews, newsonjapan Disclaimer: The information on this website is for general purposes only. While efforts are made to ensure accuracy, Marine Insight make no warranties of any kind regarding completeness, reliability, or suitability. Any reliance you place on such information is at your own risk. Marine Insight is not liable for any loss or damage arising from the use of this website. Aakriti is a journalist and news writer at Marine Insight, with over three years of experience covering the global shipping and maritime ...Read More ->

Hitachi launches AML monitoring service for cryptocurrencies and stablecoins. By: coinpost.jp | 10/05/2026 02:29:36 Trade global markets with WEEX: Crypto · Stocks · Gold · ETFs Start with 0.1 USDT Key Points of This Article * Evaluation through three functions: pre-transaction, post-transaction, and token circulation * Information shared among businesses excludes personal information such as addresses Hitachi commercializes AML monitoring service. Hitachi Ltd. announced on the 5th that it has started providing an anti-money laundering (AML) monitoring service called "Digital Asset AML Platform" targeting transactions involving cryptocurrencies and stablecoins as of October 1. The service is aimed at financial institutions and cryptocurrency-related businesses. When the results of the proof of concept were announced in July, the launch was planned for October. The service consists of three functions. It provides "post-monitoring" to continuously check for risk changes after transactions, and "online instant evaluation" to assess the counterparty address before transactions. It also includes "token monitoring" to check the circulation risks after the issuance of stablecoins and others. Risk assessments utilize external blockchain analysis information and sanctions/crime-related data, combined with AI and machine learning to analyze transaction behaviors. The information shared among participating businesses is expected to include risk signals such as wallet addresses, transaction hashes, and reasons for detection. Personal attribute information is excluded from sharing. Two proofs and support from the Financial Services Agency. This service is based on the results of proofs conducted by Hitachi since 2025 in collaboration with cryptocurrency-related businesses and AML technology providers. The first proof, which began in February 2025, confirmed the effectiveness of information collaboration across businesses. The second proof, conducted from March to May 2026, was carried out as a project supported by the Financial Services Agency's "FinTech Proof of Concept Hub," with 17 companies participating. Hitachi positions the evaluation results as "decision-making materials" and explains that they do not determine fraudulent transactions or legal violations. The final response is decided by the participating businesses. The corresponding blockchain, tokens, and evaluation functions vary depending on the contract terms and conditions. This content is provided for general informational purposes only and doesn't constitute financial, investment, legal, or tax advice. Any events, rewards, online promotions, or related information mentioned herein should not be considered a recommendation, solicitation, or invitation to purchase, sell, trade, or otherwise deal in any crypto assets. Crypto assets are highly volatile and may result in loss. The availability of WEEX services, products, and related events may vary by region. You are responsible for ensuring that your participation is in accordance with applicable local laws and regulations. You may also like. Contents. 10/05/2026

Physical AI in industrial robotics: revolutionizing manufacturing! October 5, 2026. A new vision for manufacturing automation from Hitachi and FANUC. Hitachi and FANUC have forged a groundbreaking partnership in industrial automation. This collaboration aims to integrate physical AI systems into manufacturing environments, automating complex part handling and production line changes. Focusing on discrete manufacturing and process industries, the initiative seeks to develop robots capable of autonomously performing high-variation assembly, logistics, and material handling tasks. Limitations of current robots and the solution. Standard industrial robots typically rely on predefined programs and simulations. This limits their adaptability when processing non-uniform parts or during frequent batch changes. To overcome these operational constraints, Hitachi and FANUC will develop closed-loop control architectures that continuously learn using shop-floor data. Partnership details. * Hitachi's Contribution: Will provide the digital infrastructure framework, including specialized models from its HMAX Industry package, production line engineering capabilities, and operational software. * FANUC's Contribution: Will directly integrate Hitachi's algorithms with its programmable industrial robots, robotic motion controllers, and machine-level sensing platforms. Technical architecture and integration. The new system will combine AI inference modules with machine controllers, enabling robot arms to perceive spatial variance, calculate gripping trajectories, and manipulate components with inconsistent geometries. Instead of relying on rigid coordinate paths, robots will use continuous sensor feedback to dynamically adapt to component deviations. Initial applications will include unstructured bin picking, randomly oriented part alignment, and rapid tool reconfiguration. This integrated digital infrastructure will reduce the programming burden typically required during product changeovers while maintaining deterministic cycle times. Field validation and deployment schedule. Integration tests are being conducted at Hitachi Group manufacturing facilities under a "Customer Zero" validation protocol. These tests expose robotic systems to real-world operational variables like mechanical vibrations, ambient light changes, and varying component tolerances, allowing for iterative parameter tuning before external deployment. Commercial deployment of validated systems is planned to commence in fiscal year 2027. Key application sectors include automotive sub-assembly, semiconductor packaging, pharmaceutical processing, logistics sorting, food packaging, and advanced materials manufacturing. Executive insights. Toshiaki Tokunaga, President and CEO of Hitachi, Ltd.: "By combining FANUC's world-class advanced industrial robotics technologies with Hitachi's manufacturing expertise, production line engineering capabilities, and digital technologies, we will accelerate the implementation of Physical AI in manufacturing sites." Kenji Yamaguchi, President and CEO of FANUC CORPORATION: "The practical deployment of Physical AI, where AI perceives, thinks, and acts in the physical world through robots and machines, is becoming a reality across various industries. This strategic partnership will accelerate the deployment of Physical AI by combining Hitachi's advanced AI technologies with FANUC's highly reliable products, sophisticated control technologies, AI expertise, and extensive robotics experience." Impact on industrial operations. The deployment of adaptive robots targets structural labor constraints in high-mix production. By replacing manual teaching routines with adaptive trajectory generation, facilities will enhance overall equipment effectiveness, stabilize cycle repeatability, and maintain production flexibility amidst fluctuating batch volumes.