Carpenter Technology (NYSE:CRS) Q1 2026 Earnings Call October 23, 2025 10:00 AM ET Company ParticipantsTim Lain - SVP and CFOTony Thene - Chairman and CEOBennett Moore - Equity ResearchJohn Huyette - VP of Investor RelationsConference Call ParticipantsJosh Sullivan - AnalystPhil Gibbs - Equity Research AnalystGautam Khanna - AnalystScott Deuschle - Equity AnalystAndre Madrid - AnalystOperatorThank you for standing by. At this time, I would like to welcome everyone to today's Carpenter Technology Q1 Fiscal Y ...

Carpenter Technology (NYSE:CRS) Q1 2026 Earnings Call October 23, 2025 10:00 AM ET Speaker1Thank you for standing by. At this time, I would like to welcome everyone to today's Carpenter Technology Q1 Fiscal Year 2026 earnings presentation. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session. If you'd like to ask a question during this time, simply press star followed by the number one on your telephone keypad. Once agai ...

近日，甘肃省金昌市"镍都科创周"启动仪式举行，现场含"科"量十足。金川集团镍合金有限公 司"手撕镍"等创新成果亮相，双创大赛、科技招商、人才夜市等活动同步开展，政企学研各界共谋创 新。 近年来，金昌市围绕"强科技"行动，激发创新活力、培育产业动能、优化创新生态，推动科技与经 济深度融合。2025年上半年，在甘肃省营商环境考核中，金昌市2项科技创新指标均为满分，排名并列 全省第一。 培育创新主体 在金川集团镍合金有限公司的车间里，伴随着机器轰鸣声，0.05毫米厚的"手撕镍"如丝绸般从生产 线滑落。这种超薄材料，正广泛应用于新能源电池的"心脏"部位。 科技日报记者 颉满斌 为提升企业创新能力，金昌市还支持企业联合高校院所开展产学研合作，企业牵头承担的省级、市 级重大科技项目比例超过95%；开展高技术、高成长性、高附加值企业培育壮大三年行动，建立优质企 业培育库，获批认定"三高"企业14家；实施科技型企业倍增计划，全市高新技术企业总量达到87家，省 级科技创新型企业达到61家。 此外，金昌市还积极举办科技成果对接会，促进科技成果转化。在新能源电池、化工循环、高品质 菜草畜等领域科技成果对接会上，现场签订多份科技成果 ...

Company Overview - Chuangcai Deep Learning (Suzhou) Technology Co., Ltd. focuses on "AI + new material solutions" and was established in 2021, comprising experts from materials science, artificial intelligence, and mechanical engineering [3] - The company has developed a high-throughput R&D platform and intelligent computing engine to accelerate the development and application of new materials [3] Recent Financing - The company recently completed several million yuan in Series A financing, led by Heshijia Capital and Chenhui Capital [2] - The funds will be used for new material R&D iteration, high-throughput automated laboratory upgrades, AI model development, and large-scale applications in vertical industry scenarios [2] Product Development - Chuangcai Deep Learning has developed three new materials in just six months, showcasing its rapid innovation capabilities [2] - The company plans to launch its self-developed integrated hardware and software material intelligence platform (DM Agent) at the TCT Shenzhen Exhibition on September 10 [2] Material Offerings - The company offers 3D printing metal powder materials, high-throughput R&D equipment, and high-end metal material R&D services [5] - Notable products include: - Medium and high-strength aluminum alloys with excellent performance and cost-efficiency - Nickel-based high-temperature alloys that prevent cracking during printing and heat treatment - Tool steels with high toughness and excellent thermal conductivity [5] AI-Driven R&D - The company employs an AI-driven approach to accelerate the development of advanced materials in key sectors such as aerospace and mold manufacturing [6] - Utilizing AI for Science's fifth paradigm in materials informatics, the company innovates a "AI + metal materials" R&D model, leveraging material computation, machine learning, and deep neural networks [7] High-Throughput R&D System - Chuangcai Deep Learning has established a comprehensive high-throughput R&D system that collects vast amounts of diverse material data quickly and cost-effectively [9] - The company provides a one-stop service covering product R&D, sample design, metal powder, production processing, quality testing, and performance analysis [9] Investment Insights - Heshijia Capital recognizes the company's ability to integrate AI with digital manufacturing, viewing the DM Agent platform as a potential game-changer in low-cost material research and application innovation [9] - Chenhui Capital sees significant potential in the company's disruptive "AI + new materials" model, which transforms traditional trial-and-error R&D into predictive R&D, addressing long development cycles and high costs [10]