TE智库

在产业升级大潮中寻求自我突破的中国工业软件产业

报告撰写：TE·智库

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2023年10月

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TE智库

在产业升级大潮中寻求自我突破的中国工业软件产业

报告撰写：TE· 智库

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时 间：2023.10

2023年10 月

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苏州浩辰软件股份有限公司

Gstarsoft Co., Ltd.

（苏州工业园区东平街 286 号）

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首次公开发行股票并在科创板上市

招股说明书（注册稿）

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关于征求“十四五”国家重点研发计划“煤炭清洁高效利用技术”等24个重点专项2022年度项目申报指南意见的通知

发布时间： 2022年03月03日 来源：科学技术部

Consumers today want increasingly smart, efficient and customized products that can be delivered in a timely manner. We are seeing a new product generation emerge across industries, powered by a spectacular innovation wave, including ever-increasing mechatronics, new lightweight materials and digital tech-nologies like the cloud and the Internet of Things (IoT). This additional complex-ity is no longer manageable in a traditional, verification-centric develop-ment process, so manufacturers are deploying new approaches that enable them to predict product behavior on an individual level over the entire lifecycle, including all multi-physics. Test depart-ments are feeling the effects of this evolution in their work, both in volume and technical content. More than ever, they need innovative testing solutions that help them achieve maximum productivity.

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Today’s simulation methodologies often lack the realism to deliver prediction-capable models. Yet those models are essential for complex product develop-ment or for use in embedded software to enable smart behavior. Test departments play a crucial role in filling the current gaps. This goes way beyond measuring accurate data for standard structural correlation and model updating. Testing allows companies to explore uncharted design territories and build knowledge about new materials and all the addi-tional parameters that come with mechatronic components. This requires specialized, high-precision tools. And it adds an enormous amount of validation work for test departments on top of their standard tasks, which already are per-formed under growing time pressure. These include prototype validation and certification.

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It has never been more critical to optimize design in a system’s early development stages when it is still conceptual. There is immense pressure to reach the ever-increas-ing performance levels in the context of increasingly complex, interconnected and smart products. Any defect identified early in the process will be easier to solve and have little impact on the project timeline and cost. That cost will be negligible compared to product recalls and the negative impact on brand image if the issue is discovered later in the design cycle.

Smart products, tailored to personal needs, preferences and habits – that’s what consumers want today. As a manufacturer, you must fulfill this request, and design products with increasing complexity. These products include mechanical and electronic components, software and controls.

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The industry mandate for energy efficiency also persists. Environmental concerns such as the scarcity of fossil fuels and global warming continue to raise the importance of fuel efficiency. New lightweight materials and manufacturing methods are required to address this challenge, as are intelligent systems that optimize energy consumption.

Smart products also evolve over time. They include many sensors that optimize product performance after delivery, communicate with each other and their environment, and feed information back to design teams for further improvement. This scenario requires powerful data management and analytics in a digital solution that involves the entire product lifecycle.

All these challenges come in addition to classic performance requirements such as comfort and durability, and further pressure the design process. Engineering departments must continuously find ways to deal with more parameters, data and product variations in less time. The traditional verification and validation approach needs to gain efficiency and close the loop with the product in use.

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Addressing complex engineering challenges by enhancing simulation efficiency

Computer-aided engineering (CAE) has long proven its value as a troubleshoot-ing and analysis tool, but is generally perceived as slow, delivering accurate results too late to drive development. The simulation process with traditional CAE tools is slow due to tedious geom-etry cleanup processes, and simulation disciplines are disconnected from each other, hampering efficient workflows.

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To meet complex challenges, product engineering teams need a unified, shared platform for all simulation disciplines, with leading-edge analysis tools that are easy-to-use, incorporate more productive workflows and produce consistent results.

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