制造业中的人工智能：更智能、更高效工厂的关键（中英文）

未来的工厂将直观、智能，并配备传感器——这一切都归功于制造业中的人工智能。了解人工智能对未来工厂的重要性。

The factory of the future is intuitive, smart, and loaded with sensors—all thanks to AI in manufacturing. Learn why it's important for future factories.

• 制造业的人工智能目前专注于管理特定流程的离散系统，而非完全自动化的工厂，从而提高效率并增强对工具磨损或系统故障等事件的响应能力。

• 制造业的人工智能通过处理重复性任务、提高安全性和效率，以及使人类能够专注于创造性和复杂的问题解决来支持工人。

• 制造业的人工智能用于预测性维护、实时监控和生成式设计，从而减少停机时间、优化流程并创建更智能、适应性更强的制造系统。

• Rather than fully autonomous factories, AI in manufacturing currently focuses on discrete systems that manage specific processes, enhancing efficiency and responsiveness to events like tool wear or system outages.

• Artificial intelligence in manufacturing supports workers by handling repetitive tasks, improving safety and efficiency, and allowing humans to focus on creative and complex problem-solving.

• AI in manufacturing is used for predictive maintenance, real-time monitoring, and generative design, which reduces downtime, optimizes processes, and creates smarter, more adaptable manufacturing systems.

——Andy Harris

全自动化工厂一直是一个颇具挑战性的愿景，经常出现在科幻小说中。它几乎无人值守，完全由人工智能 (AI) 系统指挥机器人生产线运行。但在实际规划期内，这不太可能成为人工智能在制造业的应用方式。

人工智能在制造业的现实构想更像是一系列紧凑、离散的系统应用程序，用于管理特定的制造流程。这些系统将或多或少地自主运行，并以越来越智能甚至更像人类的方式响应外部事件——从工具磨损、系统故障到火灾或自然灾害。

The fully autonomous factory has always been a provocative vision, much used in speculative fiction. It’s a place that’s nearly unmanned and run entirely by artificial intelligence (AI) systems directing robotic production lines. But this is unlikely to be the way AI will be employed in manufacturing within the practical planning horizon.

The realistic conception of AI in manufacturing looks more like a collection of applications for compact, discrete systems that manage specific manufacturing processes. They will operate more or less autonomously and respond to external events in increasingly intelligent and even humanlike ways—events ranging from a tool wearing out, a system outage, or a fire or natural disaster.

制造业中的人工智能是什么？

What is artificial intelligence in manufacturing?

电子数字积分计算机 (ENIAC) 是第一台数字电子可编程计算机，这里展示的是位于费城弹道研究实验室的计算机，大约于 1947 年至 1955 年间生产。

The Electronic Numerical Integrator and Computer (ENIAC) was the first digital electronic, programmable computer, shown here at the Ballistic Research Laboratory in Philadelphia, circa 1947–1955.

制造业中的人工智能是指机器能够像人类一样执行任务——自主响应内部和外部事件，甚至预测事件。机器可以检测到工具磨损或意外情况，甚至是预期发生的事情，并做出反应并解决问题。

历史学家追踪人类从石器时代到青铜时代、铁器时代等的演变过程，根据人类对自然环境、材料、工具和技术的掌握来衡量进化发展。人类目前正处于信息时代，也称为硅时代。在这个以电子为基础的时代，人类通过计算机得到了集体增强，利用前所未有的力量掌控自然世界，并具有协同能力来完成几代人以前无法想象的事情。

随着计算机技术的进步，人类能够更好地完成传统上人类自己做的事情，人工智能的发展也水到渠成。人们可以选择如何应用机器学习和人工智能。人工智能擅长的一件事是帮助有创造力的人做更多的事情。它不一定会取代人类；理想的应用能够帮助人们发挥其独特优势——在制造业中，这可能是在工厂制造零部件，也可能是设计产品或零件。

如今，人机协作日益重要。尽管工业机器人普遍被认为是自主且“智能”的，但大多数机器人都需要大量的监督。但通过人工智能创新，它们正变得越来越智能，这使得人机协作更加安全、高效。

AI in manufacturing is the intelligence of machines to perform humanlike tasks—responding to events internally and externally, even anticipating events—autonomously. The machines can detect a tool wearing out or something unexpected—maybe even something expected to happen—and they can react and work around the problem.

Historians track human progress from the Stone Age through the Bronze Age, Iron Age, and so on, gauging evolutionary development based on human mastery of the natural environment, materials, tools, and technologies. Humankind is currently in the Information Age, also known as the Silicon Age. In this electronics-based era, humans are collectively enhanced by computers, leverage unprecedented power over the natural world, and have a synergistic capacity to accomplish things inconceivable a few generations ago.

As computer technology progresses to be more capable of doing things humans have traditionally done for themselves, AI has been a natural development. People have choices about how machine learning and AI are applied. One thing AI does well is helping creative people do more. It doesn’t necessarily replace people; the ideal applications help people do what they’re uniquely good at—in manufacturing, that could be making a component in the factory or designing a product or part.

Increasingly, it’s about the collaboration of humans and robots. Despite the pervasive popular impression of industrial robots as autonomous and “smart,” most of them require a great deal of supervision. But they are getting smarter through AI innovation, which is making collaboration between humans and robots safer and more efficient.

制造业的人工智能是如何发展的？

How has AI in manufacturing evolved?

有了更新的制造机器，人们可以在屏幕上直观地看到自己的工作，无论是在系统本身还是通过计算机。传感器提供各种因素的信息，包括材料供应和功耗。

With newer fabrication machines, people can visualize what they’re doing on a screen, either on the system itself or via a computer. Sensors provide information about a variety of factors, including material supply and power consumption.

如今，制造业中的大多数人工智能都用于测量、无损检测 (NDT) 和其他流程。人工智能正在辅助产品设计，但制造领域仍处于人工智能应用的早期阶段。机床仍然相对低效。自动化车间工装已成为热门话题，但全球许多工厂仍在依赖老旧设备，这些设备通常只配备机械或有限的数字接口。

较新的制造系统配备了屏幕——人机界面和电子传感器，可以提供有关原材料供应、系统状态、功耗以及许多其他因素的反馈。人们可以通过电脑屏幕或机器直观地看到他们正在做的事情。人工智能在制造业的应用场景也日渐清晰。

近期的应用场景包括实时监控加工过程以及监控刀具磨损等状态输入。此类应用属于“预测性维护”范畴。这对人工智能来说是一个显而易见的机会：算法通过分析来自传感器的连续数据流，找到有意义的模式，并应用分析来预测问题，并在问题发生前提醒维护团队予以解决。机器内部的传感器可以监测正在发生的事情。它可以是一个声学传感器，用于监测皮带或齿轮的磨损情况，也可以是一个传感器，用于监测工具的磨损情况。这些信息将与一个分析模型相链接，该模型可以预测该工具的剩余寿命。

在车间，增材制造正成为一种重要的生产方式，并促使许多新型传感器被添加到系统中，用于监测影响材料和制造技术的新条件，而这些技术在过去十年中才被广泛采用。

Today, most of the AI in the manufacturing industry is used for measurement, nondestructive testing (NDT), and other processes. AI is assisting in the design of products, but fabrication is still in the early stages of AI adoption. Machine tools remain relatively dumb. Automated shop tooling is in the news, but many of the world’s factories continue to rely on older equipment, often with only a mechanical or limited digital interface.

Newer fabrication systems have screens—human-computer interfaces and electronic sensors to provide feedback on raw material supply, system status, power consumption, and many other factors. People can visualize what they’re doing, either on a computer screen or on the machine. The way forward is becoming clear, as is the range of scenarios for how AI is used in manufacturing.

The nearer-term scenarios include monitoring the machining process in real time and monitoring status inputs like tool wear. Such applications fall under the heading of “predictive maintenance.” It’s an obvious opportunity for AI: Algorithms that consume continuous streams of data from sensors find meaningful patterns and apply analytics to predict problems and alert maintenance teams to resolve them before they happen. Sensors inside the machine can monitor that something’s happening. It could be an acoustic sensor listening for the belts or gears starting to wear out, or it could be a sensor monitoring the wear of the tool. That information would be linked to an analytic model that could predict how much life is left in that tool.

On the shop floor, additive manufacturing is becoming an important modality and has prompted adding many new types of sensors to the system, monitoring new conditions affecting materials and fabrication technology only widely adopted in the past 10 years.

人工智能在制造业的现状

The current state of AI in manufacturing

航空航天只是众多可以从创建制造流程链的数字孪生中受益的行业之一。

Aerospace is just one example of the many industries that can benefit from creating a digital twin of its manufacturing process chain.

通过使用数字孪生，人工智能 (AI) 可以实现更精确的制造流程设计，以及在制造过程中出现缺陷时的问题诊断和解决。数字孪生是物理零件、机床或正在制造零件的精确虚拟复制品。它不仅仅是一个 CAD 模型。它是零件的精确数字表示，能够反映零件在出现缺陷等情况下的行为方式。（所有零件都有缺陷，这就是它们失效的原因。）在制造流程设计和维护中，数字孪生的应用离不开人工智能。

大型企业可以从人工智能的采用中获益良多，并且拥有资助这些创新的财务实力。但一些最具想象力的应用是由中小企业 (SME) 资助的，例如合同设计师或为航空航天等技术密集型行业供货的制造商。

许多中小企业正试图通过快速采用新机器或新技术来超越规模更大的竞争对手。提供这些服务在制造领域具有差异化优势，但在某些情况下，他们在缺乏必要知识或经验的情况下实施新的工具和流程。从设计或制造的角度来看，情况可能确实如此；正因如此，进入增材制造领域才充满挑战。在这种情况下，中小企业可能比大型企业更有动力采用人工智能：使用能够提供反馈并协助设置和运行的智能系统，可以帮助小型初创公司在市场上站稳脚跟。

本质上，端到端的工程专业知识可以融入制造流程。也就是说，搭载人工智能的工具可以配备知识，指导其安装、应用、传感器以及用于检测运行和维护问题的分析。（这些分析可能包括所谓的“无监督模型”，这些模型经过训练，可以通过寻找待调查的奇怪或“错误”方面来寻找与已知问题无关的传感器反馈模式。）

这一概念的一个现实示例是 DRAMA（航空航天数字可重构增材制造设施），这是一项耗资 1430 万英镑（1940 万美元）的合作研究项目，于 2017 年 11 月启动。Autodesk 是与制造技术中心 (MTC) 合作的企业联盟之一，旨在打造一个“数字学习工厂”的原型。整个增材制造流程链正在实现数字孪生；该设施将可重构以满足不同用户的需求，并允许测试不同的硬件和软件选项。开发人员正在构建增材制造“知识库”，以帮助采用技术和流程。

在 DRAMA 中，Autodesk 在设计、仿真和优化方面发挥着关键作用，充分考虑了制造过程中发生的下游流程。了解制造过程对每个部件的影响是人类可以自动化并通过生成设计带入设计过程的关键信息，从而使数字设计的性能更接近物理部件。

AI is making possible much more precise manufacturing process design, as well as problem diagnosis and resolution when defects crop up in the fabrication process, by using a digital twin. A digital twin is an exact virtual replica of the physical part, the machine tool, or the part being made. It’s much more than a CAD model. It’s an exact digital representation of the part and how it will behave if, for example, a defect occurs. (All parts have defects; that’s why they fail.) AI is necessary for the application of a digital twin in manufacturing process design and maintenance.

Large enterprises have a lot to gain from AI adoption, as well as the financial strength to fund these innovations. But some of the most imaginative applications have been funded by small- to medium-size enterprises (SMEs), such as contract designers or manufacturers supplying technology-intensive industries like aerospace.

Many SMEs are trying to leapfrog larger competitors by rapidly adopting new machinery or new technology. Offering these services is differentiating in the fabrication space, but in some cases, they are implementing new tools and processes without the necessary knowledge or experience. This could be true from a design point of view or a manufacturing point of view; it’s challenging to break into additive manufacturing because of this. In this scenario, SMEs could have greater incentives for AI adoption than large enterprises: Using smart systems that can provide feedback and assist setup and operationalizing could help a small upstart establish a disruptive foothold in the market.

Essentially, end-to-end engineering expertise can be built into a manufacturing process. That is, the tooling with onboard AI can be delivered with the knowledge to direct its installation, adoption, sensors, and analytics for detecting operational and maintenance issues. (Those analytics are likely to include so-called “unsupervised models,” trained to look for patterns of feedback from the sensors not associated with known problems by looking for odd or “wrong” aspects to be investigated.)

A real-world example of this concept was DRAMA (Digital Reconfigurable Additive Manufacturing facilities for Aerospace), a £14.3 million ($19.4 million) collaborative research project started in November 2017. Autodesk is among a consortium of companies working with the Manufacturing Technology Centre (MTC) to prototype a “digital learning factory.” The entire additive-manufacturing process chain is being digitally twinned; the facility will be reconfigurable to meet the requirements of different users and to allow testing of different hardware and software options. Developers are building an additive manufacturing “knowledge base” to aid in technology and process adoption.

In DRAMA, Autodesk plays a key role in design, simulation, and optimization, fully taking into account the downstream processes that occur in manufacturing. Understanding the effect of the manufacturing process on each part is critical information that humans can automate and then bring into the design process through generative design to allow the digital design to perform closer to the physical part.

人工智能如何改变制造业

How AI could transform the manufacturing industry

这里展示的是增材制造“工具箱”的一个例子——集装箱内的机器人，准备在建筑工地上工作。

Shown here is an example of an additive manufacturing “toolbox”—robots inside a shipping container, ready to get to work at a construction jobsite.

这一场景意味着有机会有效地打包端到端工作流程，并将其出售给制造商。它可以涵盖从软件到工厂中的物理机械、机械的数字孪生、与工厂供应链系统交换数据的订购系统，以及用于监控制造方法并在输入流经系统时收集数据的分析系统。本质上，就是创建“盒子工厂”系统。

This scenario suggests an opportunity to effectively package an end-to-end work process to sell to a manufacturer. It could include everything from software to the physical machinery in the factory, the digital twin of the machinery, the ordering system that exchanges data with the factory’s supply-chain systems, and the analytics to monitor manufacturing methods and collect data as inputs move through the system. Essentially, creating “factory in a box” systems.

盒子里的工厂

Factory in a box

这样的系统可以让制造商查看今天生产的零件，将其与昨天生产的零件进行比较，确保产品质量得到保证，并分析生产线上每个工序的无损检测 (NDT)。反馈将帮助制造商准确了解制造这些零件所使用的参数，然后根据传感器数据找出缺陷所在。

该流程的理想愿景是，从一端装载材料，从另一端取出零件。人们只需要维护系统，而大部分工作最终可以由机器人完成。但在目前的设想中，人们仍然负责设计和决策、监督生产，并在多个生产线职能部门工作。该系统可以帮助他们了解其决策的实际影响。

Such a system would allow a manufacturer to look at the part that made today, compare it to the part made yesterday, see that product quality assurance is being done, and analyze the NDT that’s been done for each process on the line. The feedback would help the manufacturer understand exactly what parameters were used to make those parts and then, from the sensor data, see where there are defects.

The utopian vision of that process would be loading materials in at one end and getting parts out the other. People would be needed only to maintain the systems where much of the work could be done by robots eventually. But in the current conception, people still design and make decisions, oversee manufacturing, and work in a number of line functions. The system helps them understand the actual impacts of their decisions.

机器学习与自主人工智能

Machine learning and autonomous AI

人工智能的强大之处在于，机器学习、神经网络、深度学习和其他自组织系统无需人工干预，就能从自身经验中学习。这些系统能够快速从海量数据中发现人类分析师无法企及的重要模式。然而，在当今的制造业中，人工智能应用的开发仍然主要由人类专家主导，他们将自己在之前设计的系统中积累的专业知识进行编码。人类专家会根据自身经验，分析发生了什么、哪些地方出了问题、哪些地方进展顺利，并提出自己的看法。

Much of the power of AI comes from the ability of machine learning, neural networks, deep learning, and other self-organizing systems to learn from their own experience, without human intervention. These systems can rapidly discover significant patterns in volumes of data that would be beyond the capacity of human analysts. In manufacturing today, though, human experts are still largely directing AI application development, encoding their expertise from previous systems they’ve engineered. Human experts bring their ideas of what has happened, what has gone wrong, what has gone well.

尽管人工智能由于能够比人类更快地从大量数据中检测出模式而变得越来越普遍和重要，但仍然需要人类专家来指导人工智能应用程序的开发。

Although AI is becoming more prevalent—and important—in manufacturing because of its ability to detect patterns in large amounts of data much quicker than humans, human experts are still needed to direct AI application development.

最终，自主人工智能将以这些专业知识为基础，例如，在增材制造领域，新员工将受益于操作反馈，因为人工智能会分析机载传感器数据，进行预防性维护并改进流程。这是迈向诸如自动校正机器等创新的中间步骤——随着工具磨损，系统会自我调整以保持性能，同时建议更换磨损的部件。

Eventually, autonomous AI will build on this body of expert knowledge so a new employee in, say, additive manufacturing benefits from operational feedback as the AI analyzes onboard sensor data for preventive maintenance and to refine the process. That’s an intermediate step toward innovations like self-correcting machines—as tools wear out, the system adapts itself to maintain performance while recommending replacement of the worn components.

工厂规划与布局优化

Factory planning and layout optimization

人工智能的应用并不局限于制造流程本身。从工厂规划的角度来看，设施布局受诸多因素影响，从操作员安全到工艺流程效率。这可能需要设施可重新配置，以适应一系列短期项目或频繁变化的工艺流程。

频繁的变更可能导致不可预见的空间和材料冲突，进而引发效率或安全问题。但此类冲突可以通过传感器进行跟踪和测量，人工智能在工厂布局优化中发挥着重要作用。

AI applications aren’t limited to the fabrication process itself. Think of this from a factory-planning standpoint. Facility layout is driven by many factors, from operator safety to the efficiency of process flow. It may require that the facility is reconfigurable to accommodate a succession of short-run projects or frequently changing processes.

Frequent changes can lead to unforeseen space and material conflicts, which can then create efficiency or safety issues. But such conflicts can be tracked and measured using sensors, and there is a role for AI in the optimization of factory layouts.

人工智能可以在工厂车间布局和优化中发挥作用，帮助发现潜在的操作员安全问题并提高流程效率。

AI can play a role in factory floor layout and optimization, helping to spot potential operator safety issues and improve process-flow efficiency.

传感器捕获数据用于实时AI分析

Sensors capture data for real-time AI analysis

在采用诸如增材制造等存在诸多不确定性的新技术时，一个重要的步骤是在零件制造完成后进行无损检测 (NDT)。无损检测成本可能非常高昂，尤其是在使用固定设备CT扫描仪（用于分析制造零件的结构完整性）的情况下。机器中的传感器可以连接到基于特定零件制造过程中学习到的大量数据集构建的模型。一旦获得传感器数据，就可以利用传感器数据构建机器学习模型，例如，将其与CT扫描中观察到的缺陷关联起来。传感器数据可以标记分析模型认为可能存在缺陷的零件，而无需对零件进行CT扫描。只需扫描这些零件，而不是在所有零件下线后进行例行扫描。

该操作还可以监控人员如何使用设备。制造工程师在设计设备时会假设机器的运行方式。如果采用人工分析，可能会出现额外的步骤或跳过某个步骤。传感器可以精准捕捉这些信息，用于人工智能分析。

人工智能还能帮助制造流程和工具适应各种应用环境。例如，湿度。增材制造技术的开发者发现，某些机器在某些国家/地区无法按设计运行。工厂中的湿度传感器被用于监测环境条件，有时会发现一些违反直觉的情况。在一个案例中，湿度在本应控制湿度的环境中造成了问题：结果发现有人在外出吸烟时忘了关门。

有效利用传感器数据需要开发有效的人工智能模型。这些模型必须经过训练，才能理解它们在数据中看到的内容——导致这些问题的原因、如何检测原因以及应该采取的措施。如今，机器学习模型可以使用传感器数据预测问题何时发生，并向人工故障排除人员发出警报。最终，人工智能系统将能够预测问题并实时做出反应。人工智能模型很快将承担起创造主动方法来解决问题和改进制造流程的任务。

When adopting new technologies where there’s a lot of uncertainty, like additive manufacturing, an important step is using NDT after the part’s been made. Nondestructive testing can be very expensive, especially if it incorporates capital equipment CT scanners (used to analyze the structural integrity of manufactured parts). Sensors in the machines can link to models that are built up from a large data set learned from the manufacturing process for specific parts. Once sensor data is available, it’s possible to build a machine-learning model using the sensor data—for example, to correlate with a defect observed in the CT scan. The sensor data can flag parts that the analytic model suggests are likely to be defective without requiring the part to be CT-scanned. Only those parts would be scanned instead of routinely scanning all parts as they come off the line.

The operation can also monitor how people are using the equipment. Manufacturing engineers make assumptions when the equipment is designed about how the machinery will be operated. With human analysis, there may be an extra step happening or a step being skipped. Sensors can accurately capture that information for AI analysis.

AI also has a role in adapting manufacturing processes and tooling to various environmental conditions where they might be applied. Take, for example, humidity. Developers of additive-manufacturing technology have found that some machines don’t work as designed in certain countries. Humidity sensors in the factories have been used to monitor conditions, sometimes discovering counterintuitive things. In one case, humidity created issues in what was supposed to be a moisture-controlled environment: It turned out that somebody was leaving the door open when he or she went outside to smoke.

Effectively using sensor data requires the development of effective AI models. Those models have to be trained to understand what they’re seeing in the data—what can cause those problems, how to detect the causes, and what to do. Today, machine-learning models can use sensor data to predict when a problem is going to occur and alert a human troubleshooter. Ultimately, AI systems will be able to predict issues and react to them in real time. AI models will soon be tasked with creating proactive ways to head off problems and to improve manufacturing processes.

生成式设计

Generative design

人工智能在生成式设计中扮演着重要的角色。生成式设计是指设计工程师输入一组项目需求，然后设计软件进行多次迭代的过程。最近，Autodesk 收集了大量用于增材制造的材料数据，并利用这些数据来驱动生成式设计模型。该原型能够“理解”材料属性如何根据制造过程对各个特征和几何形状的影响而变化。

AI has an important role in generative design, a process in which a design engineer enters a set of requirements for a project and then design software creates multiple iterations. Recently, Autodesk has collected large volumes of materials data for additive manufacturing and is using that data to drive a generative-design model. This prototype has an “understanding” of how the material properties change according to how the manufacturing process affects individual features and geometry.

得益于人工智能，生成设计软件可以在相同时间内创造出比设计师更多的设计迭代，同时还能自动执行日常任务。

Thanks to AI, generative-design software can create more design iterations than a designer can come up with in the same amount of time while also automating routine tasks.

生成式设计是一种适应性强的优化技术。许多传统的优化技术着眼于更通用的零件优化方法。而生成式设计算法则可以更加具体，专注于单个特征，并运用基于材料测试和与高校合作对该特征机械特性的理解。尽管设计是理想化的，但制造过程发生在现实世界中，因此条件可能并非恒定不变。有效的生成式设计算法会融入这种层次的理解。

生成式设计可以在软件中创建最优设计和规格，然后将该设计分发到多个配备兼容工具的工厂。这意味着规模较小、地理位置分散的工厂可以生产更大范围的零件。这些工厂可以靠近需求地点；一个工厂可能今天生产航空航天零件，明天又生产其他必需产品的零件，从而节省配送和运输成本。例如，这在汽车行业正成为一个重要的概念。

Generative design is an adaptable optimization technique. A lot of traditional optimization techniques look at more general approaches to part optimization. Generative-design algorithms can be much more specific, focusing on an individual feature, applying an understanding of the mechanical properties of that feature based on materials testing and collaboration with universities. Although designs are idealized, manufacturing processes take place in the real world, so conditions might not be constant. An effective generative-design algorithm incorporates this level of understanding.

Generative design can create an optimal design and specifications in software, then distribute that design to multiple facilities with compatible tooling. This means smaller, geographically dispersed facilities can manufacture a larger range of parts. These facilities could be proximal to where they’re needed; a facility might make parts for aerospace one day and the next day make parts for other essential products, saving on distribution and shipping costs. This is becoming an important concept in the automotive industry, for example.

灵活且可重构的流程和工厂车间

Flexible and reconfigurable processes and factory floors

人工智能还可用于优化制造流程，使其更加灵活且可重构。当前需求可以决定工厂车间布局并生成流程，这也可以用于未来需求。然后，这些模型可用于比较和对比。分析结果决定是减少大型增材制造机器的数量更好，还是使用大量小型机器更好，后者可能成本更低，并且在需求放缓时可以转移到其他项目。“假设”分析是人工智能的常见应用。

模型将用于优化车间布局和流程排序。例如，增材制造部件的热处理可以直接在3D打印机上进行。材料可能是预回火的，也可能需要重新回火，从而需要另一个热循环。工程师可以运行各种假设情景来确定工厂应该配备哪种设备——将部分流程分包给附近的其他公司可能更合理。

这些人工智能应用可能会改变商业模式，决定一家工厂是专注于单一流程，还是同时承接多个产品或项目。后者将增强工厂的韧性。以航空航天业为例，这个正在经历低迷的行业，其制造业务或许也可以通过生产医疗部件来适应。

AI can be also used to optimize manufacturing processes and to make those processes more flexible and reconfigurable. Current demand can determine factory floor layout and generate a process, which can also be done for future demand. Those models can then be used to compare and contrast them. That analysis then determines whether is it better to have fewer large additive machines or lots of smaller machines, which might cost less and be diverted to other projects when demand slows. “What-if” analysis is a common application for AI.

Models will be used to optimize both shop floor layout and process sequencing. For example, applying thermal treatment on an additive part can be done straight from the 3D printer. It could be that the material comes in pre-tempered or it needs to be retempered, requiring another heat cycle. Engineers could run various what-if scenarios to determine what kind of equipment the facility should have—it may make more sense to subcontract parts of the process to another company nearby.

These AI applications could change the business case that determines whether a factory focuses on one captive process or takes on multiple products or projects. The latter would make the factory more resilient. In the example of aerospace, an industry that’s experiencing a downturn, it may be that its manufacturing operations could adapt by making medical parts, as well.

预测性维护

Predictive maintenance

人工智能在制造业的另一个重点关注领域是预测性维护。这使得工程师能够为工厂机器配备预先训练的人工智能模型，这些模型融合了该工具的累积知识。基于来自机器的数据，这些模型可以学习现场发现的新的因果模式，从而预防问题的发生。

Another key area of focus for AI in manufacturing is predictive maintenance. This allows engineers to equip factory machines with pretrained AI models that incorporate the cumulative knowledge of that tooling. Based on data from the machinery, the models can learn new patterns of cause and effect discovered on-site to prevent problems.

制造业和人工智能：应用和优势

Manufacturing and artificial intelligence: Applications and benefits

结合人工智能，虚拟现实 (VR) 和增强现实 (AR) 可以帮助缩短设计时间，并通过提高生产线工人的速度和精度来优化装配线流程。

Combined with AI, virtual reality (VR) and augmented reality (AR) can help reduce design time and optimize assembly-line processes by improving the speed and precision of line workers.

设计、工艺改进、减少机器磨损以及优化能耗都是人工智能在制造业的应用领域。这场变革已经开始。

机器正变得越来越智能，彼此之间以及与供应链和其他业务自动化之间的集成度也越来越高。理想的情况是材料进，零件出，传感器监控着链条上的每一个环节。人们控制着流程，但不一定在环境中工作。这释放了重要的制造资源和人员，使他们能够专注于创新——创造新的组件设计和制造方法——而不是重复性的工作，因为重复性工作可以自动化。

与任何根本性的转变一样，人工智能的采用也遭遇了阻力。人工智能所需的知识和技能可能价格昂贵且稀缺；许多制造商并不具备这些内部能力。他们认为自己在专业能力方面很高效，因此为了证明投资制造新产品或改进工艺的合理性，他们需要详尽的证据，并且可能不愿扩大工厂规模。

这使得“盒子工厂”的概念对企业更具吸引力。越来越多的企业，尤其是中小企业，可以自信地采用端到端的打包流程，让软件与工具无缝协作，利用传感器和分析技术进行改进。数字孪生功能让工程师可以模拟尝试新的制造流程，这也降低了决策风险。

人工智能在质量检测中也发挥着作用，而质量检测会产生大量数据，因此非常适合机器学习。以增材制造为例：一次制造会生成多达 TB 的数据，这些数据涉及机器如何生产零件、现场条件以及制造过程中发现的任何问题。如此大的数据量超出了人类的分析范围，但人工智能系统现在可以做到。适用于增材制造工具的方法也同样适用于减材制造、铸造、注塑成型以及其他各种制造工艺。

当虚拟现实 (VR) 和增强现实 (AR) 等互补技术加入时，人工智能解决方案将缩短设计时间并优化装配线流程。生产线工人已经配备了 VR/AR 系统，可以让他们直观地看到装配过程，通过视觉指导来提高工作速度和精度。操作员可能戴着 AR 眼镜，可以投射图表来解释如何组装零件。系统可以监控工作并向工人发出提示：你把这个扳手拧得够多了，你拧得不够，或者你还没有扣动扳机。

大型企业和中小企业在人工智能应用方面有不同的侧重点。中小企业往往生产大量零件，而大型企业通常会组装大量从其他地方采购的零件。也有例外；汽车公司会进行大量的底盘点焊，但也会购买和组装其他零件，例如轴承和塑料部件。

就零件本身而言，一个新兴趋势是使用智能部件：零件中嵌入了传感器，可以监测自身的状况、压力、扭矩等。这个想法对汽车制造业尤其具有启发性，因为这些因素更多地取决于汽车的驾驶方式，而不是行驶里程；如果每天都要经过很多坑洼，则可能需要更多的维护。

Design, process improvement, reducing the wear on machines, and optimizing energy consumption are all areas AI will be applied in manufacturing. That evolution has already begun.

The machines are getting smarter and more integrated, with each other and with the supply chain and other business automation. The ideal situation would be materials in, parts out, with sensors monitoring every link in the chain. People maintain control of the process but don’t necessarily work in the environment. This frees up vital manufacturing resources and personnel to focus on innovation—creating new ways of designing and manufacturing components—rather than repetitive work, which can be automated.

As with any fundamental shift, there has been resistance to AI adoption. The knowledge and skills required for AI can be expensive and scarce; many manufacturers don’t have those in-house capabilities. They see themselves as effective in specialized competencies, so to justify the investment to make something new or improve a process, they need exhaustive proof and may be risk-averse to upscaling a factory.

This can make the concept of “factory in a box” more attractive to companies. More enterprises, especially SMEs, can confidently adopt an end-to-end packaged process where the software works seamlessly with the tooling, using sensors and analytics to improve. Adding the digital twin capability, where engineers can try out a new manufacturing process as a simulation, also makes the decision less risky.

There’s also a role for AI in quality inspection, a process that generates a lot of data so is naturally suited to machine learning. Consider additive manufacturing: One build generates as much as a terabyte of data on how the machine produced the part, the on-site conditions, and any issues discovered during the build. That volume of data is beyond human scope for analysis, but AI systems can do it now. What works for additive tools can easily work for subtractive manufacturing, casting, injection molding, and a broad range of other manufacturing processes.

When complementary technologies such as virtual reality (VR) and augmented reality (AR) are added, AI solutions will reduce design time and optimize assembly-line processes. Line workers have already been equipped with VR/AR systems that let them visualize the assembly process, providing visual guidance to improve the speed and precision of their work. The operator might have AR glasses that project diagrams explaining how to assemble the parts. The system can monitor work and offer prompts to the worker: You’ve turned this spanner enough, you’ve not turned it enough, or you’ve not pulled the trigger.

Larger companies and SMEs have different focus areas for AI adoption. SMEs tend to make a lot of parts whereas bigger companies often assemble a lot of parts sourced from elsewhere. There are exceptions; automotive companies do a lot of spot-welding of the chassis but buy and assemble other parts such as bearings and plastic components.

Regarding the parts themselves, an emerging trend is the use of smart components: parts with embedded sensors that monitor their own condition, stress, torque, and so on. This idea is especially provocative for auto manufacturing, as these factors depend more on how the car is driven rather than how many miles it goes; if driven over a lot of potholes every day, more maintenance will probably be required.

Tri-D Dynamics 使用冷金属熔合增材技术将传感器嵌入机器。如图所示的嵌入式传感器可以发送各种数据，例如温度和其他环境条件。图片由 Tri-D Dynamics 提供。

Tri-D Dynamics uses cold metal fusion additive technology to embed sensors into machines. The embedded sensors, like the one shown here, can send a variety of data, such as temperature and other conditions of the environment. Image courtesy of Tri-D Dynamics.

智能组件可以通知制造商其已达到使用寿命或需要检查。部件本身无需外部监控这些数据点，而是会偶尔与人工智能系统进行核对，报告正常状态，直到出现问题，需要关注。这种方法减少了系统内部的数据流量，而数据流量在规模化发展后可能会严重拖累分析处理性能。

人工智能增值的最大、最直接的机会在于增材制造。增材制造工艺是主要目标，因为其产品价格更高，体积更小。未来，随着人类不断发展和完善人工智能，它很可能在整个制造业价值链中发挥重要作用。

A smart component can notify a manufacturer that it has reached the end of its life or is due for inspection. Rather than monitoring these data points externally, the part itself will check in occasionally with AI systems to report normal status until conditions go sideways, when the part will start demanding attention. This approach cuts down on the volume of data traffic within the system, which at scale can become a significant drag on analytic processing performance.

The greatest, most immediate opportunity for AI to add value is in additive manufacturing. Additive processes are primary targets because their products are more expensive and smaller in volume. In the future, as humans grow AI and mature it, it will likely become important across the entire manufacturing value chain.