Sealand: humanoid Siasun Robot & Automation enters the first year of mass production, and the six-dimensional force torque sensor is expected to be massively launched at the same time.

Sealand released a research report stating that, driven by leading companies such as Tesla, Inc. (TSLA.US), the humanoid Siasun Robot & Automation is expected to enter the year of mass production. As an important application on the humanoid Siasun Robot & Automation, the six-dimensional force-torque sensors and their raw materials are expected to see synchronized volume increase with the large-scale production of the humanoid Siasun Robot & Automation. Considering the stimulating effect of the volume increase of six-dimensional force-torque sensors and their raw materials on the chemical industry, the "recommended" rating for the basic chemical industry is maintained. Sealand's main points are as follows: Six-dimensional force-torque sensors are the top sensors of the pyramid. Six-dimensional force-torque sensors are the most optimal in performance and provide the most comprehensive force feedback information. They work based on the strain effect, collecting signals from the small deformation of strain gauges and obtaining accurate measurement results through decoupling algorithms. Six-dimensional force-torque sensors have high industry barriers, including technical and equipment barriers, with a high technological threshold. Six-dimensional force-torque sensors are expected to become standard on humanoid Siasun Robot & Automation. Six-dimensional force-torque sensors are widely used in aerospace, automotive testing, biomechanics, and Siasun Robot & Automation, among many other technology fields, with industry shipments continuing to rise and market size growing. In the future, humanoid Siasun Robot & Automation will become an important application area for six-dimensional force-torque sensors. Tesla, Inc.'s Optimus Gen2 is equipped with four six-dimensional force-torque sensors, each applied to the wrist and ankle. Six-dimensional force-torque sensors require precise structural design. Strain-gauge-based six-dimensional force-torque sensors are mainstream in the market, usually consisting of core components such as the elastic body, strain gauges, PCB board, casing, and interfaces. The optimization of the elastic body's design (materials, structural dimensions, etc.) directly affects the sensor's performance; at the same time, the placement of the strain gauges also affects the sensor's accuracy. The manufacturing process of six-dimensional force-torque sensors is complex. The manufacturing process of six-dimensional force-torque sensors includes designing and manufacturing components, installing sensor elements, connecting circuits, performance testing, calibration, packaging, etc. It needs to go through multiple processes such as processing the elastic body, chip mounting, wire bonding, sealing, calibration, etc., before packaging, and also requires manufacturing processes like anodizing/electrolytic polishing/sandblasting, etching, lithography, chemical vapor deposition, chemical mechanical polishing, etc. There are various types of raw materials for six-dimensional force-torque sensors. The report detailed various raw materials needed for the processing and manufacturing process of six-dimensional force-torque sensors: aluminum alloy/alloy steel, sulfuric acid/phosphoric acid/chromium oxide, semiconductor silicon wafer, etching solution, photoresist and supporting reagents, electronic specialty gases (silane/high-purity ammonia), polishing pads/polishing liquids, epoxy resin, phenolic resin, electronic grade fiberglass, polytetrafluoroethylene, electrolytic copper foil, etc., are key raw materials in this process. Risk Warning Risks include the application of six-dimensional force-torque sensors in humanoid Siasun Robot & Automation not meeting expectations, cost control not meeting expectations, production and demand growth of humanoid Siasun Robot & Automation not meeting expectations, fluctuations in raw material prices, and a focus on company performance not meeting expectations.