制造業(yè)中的人工智能：更智能、更高效工廠的關(guān)鍵（中英文）
未來的工廠將直觀、智能，并配備傳感器——這一切都歸功于制造業(yè)中的人工智能。了解人工智能對未來工廠的重要性。
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.
? 制造業(yè)的人工智能目前專注于管理特定流程的離散系統(tǒng)，而非完全自動化的工廠，從而提高效率并增強對工具磨損或系統(tǒng)故障等事件的響應(yīng)能力。
? 制造業(yè)的人工智能通過處理重復(fù)性任務(wù)、提高安全性和效率，以及使人類能夠?qū)Ｗ⒂趧?chuàng)造性和復(fù)雜的問題解決來支持工人。
? 制造業(yè)的人工智能用于預(yù)測性維護、實時監(jiān)控和生成式設(shè)計，從而減少停機時間、優(yōu)化流程并創(chuàng)建更智能、適應(yīng)性更強的制造系統(tǒng)。
? 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
全自動化工廠一直是一個頗具挑戰(zhàn)性的愿景，經(jīng)常出現(xiàn)在科幻小說中。它幾乎無人值守，完全由人工智能 (AI) 系統(tǒng)指揮機器人生產(chǎn)線運行。但在實際規(guī)劃期內(nèi)，這不太可能成為人工智能在制造業(yè)的應(yīng)用方式。
人工智能在制造業(yè)的現(xiàn)實構(gòu)想更像是一系列緊湊、離散的系統(tǒng)應(yīng)用程序，用于管理特定的制造流程。這些系統(tǒng)將或多或少地自主運行，并以越來越智能甚至更像人類的方式響應(yīng)外部事件——從工具磨損、系統(tǒng)故障到火災(zāi)或自然災(zāi)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.
制造業(yè)中的人工智能是什么？
What is artificial intelligence in manufacturing?
電子數(shù)字積分計算機 (ENIAC) 是第一臺數(shù)字電子可編程計算機，這里展示的是位于費城彈道研究實驗室的計算機，大約于 1947 年至 1955 年間生產(chǎn)。
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.
制造業(yè)中的人工智能是指機器能夠像人類一樣執(zhí)行任務(wù)——自主響應(yīng)內(nèi)部和外部事件，甚至預(yù)測事件。機器可以檢測到工具磨損或意外情況，甚至是預(yù)期發(fā)生的事情，并做出反應(yīng)并解決問題。
歷史學(xué)家追蹤人類從石器時代到青銅時代、鐵器時代等的演變過程，根據(jù)人類對自然環(huán)境、材料、工具和技術(shù)的掌握來衡量進化發(fā)展。人類目前正處于信息時代，也稱為硅時代。在這個以電子為基礎(chǔ)的時代，人類通過計算機得到了集體增強，利用前所未有的力量掌控自然世界，并具有協(xié)同能力來完成幾代人以前無法想象的事情。
隨著計算機技術(shù)的進步，人類能夠更好地完成傳統(tǒng)上人類自己做的事情，人工智能的發(fā)展也水到渠成。人們可以選擇如何應(yīng)用機器學(xué)習(xí)和人工智能。人工智能擅長的一件事是幫助有創(chuàng)造力的人做更多的事情。它不一定會取代人類；理想的應(yīng)用能夠幫助人們發(fā)揮其獨特優(yōu)勢——在制造業(yè)中，這可能是在工廠制造零部件，也可能是設(shè)計產(chǎn)品或零件。
如今，人機協(xié)作日益重要。盡管工業(yè)機器人普遍被認為是自主且“智能”的，但大多數(shù)機器人都需要大量的監(jiān)督。但通過人工智能創(chuàng)新，它們正變得越來越智能，這使得人機協(xié)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.
制造業(yè)的人工智能是如何發(fā)展的？
How has AI in manufacturing evolved?
有了更新的制造機器，人們可以在屏幕上直觀地看到自己的工作，無論是在系統(tǒng)本身還是通過計算機。傳感器提供各種因素的信息，包括材料供應(yīng)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，制造業(yè)中的大多數(shù)人工智能都用于測量、無損檢測 (NDT) 和其他流程。人工智能正在輔助產(chǎn)品設(shè)計，但制造領(lǐng)域仍處于人工智能應(yīng)用的早期階段。機床仍然相對低效。自動化車間工裝已成為熱門話題，但全球許多工廠仍在依賴?yán)吓f設(shè)備，這些設(shè)備通常只配備機械或有限的數(shù)字接口。
較新的制造系統(tǒng)配備了屏幕——人機界面和電子傳感器，可以提供有關(guān)原材料供應(yīng)、系統(tǒng)狀態(tài)、功耗以及許多其他因素的反饋。人們可以通過電腦屏幕或機器直觀地看到他們正在做的事情。人工智能在制造業(yè)的應(yīng)用場景也日漸清晰。
近期的應(yīng)用場景包括實時監(jiān)控加工過程以及監(jiān)控刀具磨損等狀態(tài)輸入。此類應(yīng)用屬于“預(yù)測性維護”范疇。這對人工智能來說是一個顯而易見的機會：算法通過分析來自傳感器的連續(xù)數(shù)據(jù)流，找到有意義的模式，并應(yīng)用分析來預(yù)測問題，并在問題發(fā)生前提醒維護團隊予以解決。機器內(nèi)部的傳感器可以監(jiān)測正在發(fā)生的事情。它可以是一個聲學(xué)傳感器，用于監(jiān)測皮帶或齒輪的磨損情況，也可以是一個傳感器，用于監(jiān)測工具的磨損情況。這些信息將與一個分析模型相鏈接，該模型可以預(yù)測該工具的剩余壽命。
在車間，增材制造正成為一種重要的生產(chǎn)方式，并促使許多新型傳感器被添加到系統(tǒng)中，用于監(jiān)測影響材料和制造技術(shù)的新條件，而這些技術(shù)在過去十年中才被廣泛采用。
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.
人工智能在制造業(yè)的現(xiàn)狀
The current state of AI in manufacturing
航空航天只是眾多可以從創(chuàng)建制造流程鏈的數(shù)字孿生中受益的行業(yè)之一。
Aerospace is just one example of the many industries that can benefit from creating a digital twin of its manufacturing process chain.
通過使用數(shù)字孿生，人工智能 (AI) 可以實現(xiàn)更精確的制造流程設(shè)計，以及在制造過程中出現(xiàn)缺陷時的問題診斷和解決。數(shù)字孿生是物理零件、機床或正在制造零件的精確虛擬復(fù)制品。它不僅僅是一個 CAD 模型。它是零件的精確數(shù)字表示，能夠反映零件在出現(xiàn)缺陷等情況下的行為方式。（所有零件都有缺陷，這就是它們失效的原因。）在制造流程設(shè)計和維護中，數(shù)字孿生的應(yīng)用離不開人工智能。
大型企業(yè)可以從人工智能的采用中獲益良多，并且擁有資助這些創(chuàng)新的財務(wù)實力。但一些最具想象力的應(yīng)用是由中小企業(yè) (SME) 資助的，例如合同設(shè)計師或為航空航天等技術(shù)密集型行業(yè)供貨的制造商。
許多中小企業(yè)正試圖通過快速采用新機器或新技術(shù)來超越規(guī)模更大的競爭對手。提供這些服務(wù)在制造領(lǐng)域具有差異化優(yōu)勢，但在某些情況下，他們在缺乏必要知識或經(jīng)驗的情況下實施新的工具和流程。從設(shè)計或制造的角度來看，情況可能確實如此；正因如此，進入增材制造領(lǐng)域才充滿挑戰(zhàn)。在這種情況下，中小企業(yè)可能比大型企業(yè)更有動力采用人工智能：使用能夠提供反饋并協(xié)助設(shè)置和運行的智能系統(tǒng)，可以幫助小型初創(chuàng)公司在市場上站穩(wěn)腳跟。
本質(zhì)上，端到端的工程專業(yè)知識可以融入制造流程。也就是說，搭載人工智能的工具可以配備知識，指導(dǎo)其安裝、應(yīng)用、傳感器以及用于檢測運行和維護問題的分析。（這些分析可能包括所謂的“無監(jiān)督模型”，這些模型經(jīng)過訓(xùn)練，可以通過尋找待調(diào)查的奇怪或“錯誤”方面來尋找與已知問題無關(guān)的傳感器反饋模式。）
這一概念的一個現(xiàn)實示例是 DRAMA（航空航天數(shù)字可重構(gòu)增材制造設(shè)施），這是一項耗資 1430 萬英鎊（1940 萬美元）的合作研究項目，于 2017 年 11 月啟動。Autodesk 是與制造技術(shù)中心 (MTC) 合作的企業(yè)聯(lián)盟之一，旨在打造一個“數(shù)字學(xué)習(xí)工廠”的原型。整個增材制造流程鏈正在實現(xiàn)數(shù)字孿生；該設(shè)施將可重構(gòu)以滿足不同用戶的需求，并允許測試不同的硬件和軟件選項。開發(fā)人員正在構(gòu)建增材制造“知識庫”，以幫助采用技術(shù)和流程。
在 DRAMA 中，Autodesk 在設(shè)計、仿真和優(yōu)化方面發(fā)揮著關(guān)鍵作用，充分考慮了制造過程中發(fā)生的下游流程。了解制造過程對每個部件的影響是人類可以自動化并通過生成設(shè)計帶入設(shè)計過程的關(guān)鍵信息，從而使數(shù)字設(shè)計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.
人工智能如何改變制造業(yè)
How AI could transform the manufacturing industry
這里展示的是增材制造“工具箱”的一個例子——集裝箱內(nèi)的機器人，準(zhǔn)備在建筑工地上工作。
Shown here is an example of an additive manufacturing “toolbox”—robots inside a shipping container, ready to get to work at a construction jobsite.
這一場景意味著有機會有效地打包端到端工作流程，并將其出售給制造商。它可以涵蓋從軟件到工廠中的物理機械、機械的數(shù)字孿生、與工廠供應(yīng)鏈系統(tǒng)交換數(shù)據(jù)的訂購系統(tǒng)，以及用于監(jiān)控制造方法并在輸入流經(jīng)系統(tǒng)時收集數(shù)據(jù)的分析系統(tǒng)。本質(zhì)上，就是創(chuàng)建“盒子工廠”系統(tǒng)。
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.
盒子里的工廠
Factory in a box
這樣的系統(tǒng)可以讓制造商查看今天生產(chǎn)的零件，將其與昨天生產(chǎn)的零件進行比較，確保產(chǎn)品質(zhì)量得到保證，并分析生產(chǎn)線上每個工序的無損檢測 (NDT)。反饋將幫助制造商準(zhǔn)確了解制造這些零件所使用的參數(shù)，然后根據(jù)傳感器數(shù)據(jù)找出缺陷所在。
該流程的理想愿景是，從一端裝載材料，從另一端取出零件。人們只需要維護系統(tǒng)，而大部分工作最終可以由機器人完成。但在目前的設(shè)想中，人們?nèi)匀回撠?zé)設(shè)計和決策、監(jiān)督生產(chǎn)，并在多個生產(chǎn)線職能部門工作。該系統(tǒng)可以幫助他們了解其決策的實際影響。
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.
機器學(xué)習(xí)與自主人工智能
Machine learning and autonomous AI
人工智能的強大之處在于，機器學(xué)習(xí)、神經(jīng)網(wǎng)絡(luò)、深度學(xué)習(xí)和其他自組織系統(tǒng)無需人工干預(yù)，就能從自身經(jīng)驗中學(xué)習(xí)。這些系統(tǒng)能夠快速從海量數(shù)據(jù)中發(fā)現(xiàn)人類分析師無法企及的重要模式。然而，在當(dāng)今的制造業(yè)中，人工智能應(yīng)用的開發(fā)仍然主要由人類專家主導(dǎo)，他們將自己在之前設(shè)計的系統(tǒng)中積累的專業(yè)知識進行編碼。人類專家會根據(jù)自身經(jīng)驗，分析發(fā)生了什么、哪些地方出了問題、哪些地方進展順利，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.
盡管人工智能由于能夠比人類更快地從大量數(shù)據(jù)中檢測出模式而變得越來越普遍和重要，但仍然需要人類專家來指導(dǎo)人工智能應(yīng)用程序的開發(fā)。
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.
最終，自主人工智能將以這些專業(yè)知識為基礎(chǔ)，例如，在增材制造領(lǐng)域，新員工將受益于操作反饋，因為人工智能會分析機載傳感器數(shù)據(jù)，進行預(yù)防性維護并改進流程。這是邁向諸如自動校正機器等創(chuàng)新的中間步驟——隨著工具磨損，系統(tǒng)會自我調(diào)整以保持性能，同時建議更換磨損的部件。
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.
工廠規(guī)劃與布局優(yōu)化
Factory planning and layout optimization
人工智能的應(yīng)用并不局限于制造流程本身。從工廠規(guī)劃的角度來看，設(shè)施布局受諸多因素影響，從操作員安全到工藝流程效率。這可能需要設(shè)施可重新配置，以適應(yīng)一系列短期項目或頻繁變化的工藝流程。
頻繁的變更可能導(dǎo)致不可預(yù)見的空間和材料沖突，進而引發(fā)效率或安全問題。但此類沖突可以通過傳感器進行跟蹤和測量，人工智能在工廠布局優(yōu)化中發(fā)揮著重要作用。
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.
人工智能可以在工廠車間布局和優(yōu)化中發(fā)揮作用，幫助發(fā)現(xiàn)潛在的操作員安全問題并提高流程效率。
AI can play a role in factory floor layout and optimization, helping to spot potential operator safety issues and improve process-flow efficiency.
傳感器捕獲數(shù)據(jù)用于實時AI分析
Sensors capture data for real-time AI analysis
在采用諸如增材制造等存在諸多不確定性的新技術(shù)時，一個重要的步驟是在零件制造完成后進行無損檢測 (NDT)。無損檢測成本可能非常高昂，尤其是在使用固定設(shè)備CT掃描儀（用于分析制造零件的結(jié)構(gòu)完整性）的情況下。機器中的傳感器可以連接到基于特定零件制造過程中學(xué)習(xí)到的大量數(shù)據(jù)集構(gòu)建的模型。一旦獲得傳感器數(shù)據(jù)，就可以利用傳感器數(shù)據(jù)構(gòu)建機器學(xué)習(xí)模型，例如，將其與CT掃描中觀察到的缺陷關(guān)聯(lián)起來。傳感器數(shù)據(jù)可以標(biāo)記分析模型認為可能存在缺陷的零件，而無需對零件進行CT掃描。只需掃描這些零件，而不是在所有零件下線后進行例行掃描。
該操作還可以監(jiān)控人員如何使用設(shè)備。制造工程師在設(shè)計設(shè)備時會假設(shè)機器的運行方式。如果采用人工分析，可能會出現(xiàn)額外的步驟或跳過某個步驟。傳感器可以精準(zhǔn)捕捉這些信息，用于人工智能分析。
人工智能還能幫助制造流程和工具適應(yīng)各種應(yīng)用環(huán)境。例如，濕度。增材制造技術(shù)的開發(fā)者發(fā)現(xiàn)，某些機器在某些國家/地區(qū)無法按設(shè)計運行。工廠中的濕度傳感器被用于監(jiān)測環(huán)境條件，有時會發(fā)現(xiàn)一些違反直覺的情況。在一個案例中，濕度在本應(yīng)控制濕度的環(huán)境中造成了問題：結(jié)果發(fā)現(xiàn)有人在外出吸煙時忘了關(guān)門。
有效利用傳感器數(shù)據(jù)需要開發(fā)有效的人工智能模型。這些模型必須經(jīng)過訓(xùn)練，才能理解它們在數(shù)據(jù)中看到的內(nèi)容——導(dǎo)致這些問題的原因、如何檢測原因以及應(yīng)該采取的措施。如今，機器學(xué)習(xí)模型可以使用傳感器數(shù)據(jù)預(yù)測問題何時發(fā)生，并向人工故障排除人員發(fā)出警報。最終，人工智能系統(tǒng)將能夠預(yù)測問題并實時做出反應(yīng)。人工智能模型很快將承擔(dān)起創(chuàng)造主動方法來解決問題和改進制造流程的任務(wù)。
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.
生成式設(shè)計
Generative design
人工智能在生成式設(shè)計中扮演著重要的角色。生成式設(shè)計是指設(shè)計工程師輸入一組項目需求，然后設(shè)計軟件進行多次迭代的過程。最近，Autodesk 收集了大量用于增材制造的材料數(shù)據(jù)，并利用這些數(shù)據(jù)來驅(qū)動生成式設(shè)計模型。該原型能夠“理解”材料屬性如何根據(jù)制造過程對各個特征和幾何形狀的影響而變化。
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，生成設(shè)計軟件可以在相同時間內(nèi)創(chuàng)造出比設(shè)計師更多的設(shè)計迭代，同時還能自動執(zhí)行日常任務(wù)。
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.
生成式設(shè)計是一種適應(yīng)性強的優(yōu)化技術(shù)。許多傳統(tǒng)的優(yōu)化技術(shù)著眼于更通用的零件優(yōu)化方法。而生成式設(shè)計算法則可以更加具體，專注于單個特征，并運用基于材料測試和與高校合作對該特征機械特性的理解。盡管設(shè)計是理想化的，但制造過程發(fā)生在現(xiàn)實世界中，因此條件可能并非恒定不變。有效的生成式設(shè)計算法會融入這種層次的理解。
生成式設(shè)計可以在軟件中創(chuàng)建最優(yōu)設(shè)計和規(guī)格，然后將該設(shè)計分發(fā)到多個配備兼容工具的工廠。這意味著規(guī)模較小、地理位置分散的工廠可以生產(chǎn)更大范圍的零件。這些工廠可以靠近需求地點；一個工廠可能今天生產(chǎn)航空航天零件，明天又生產(chǎn)其他必需產(chǎn)品的零件，從而節(jié)省配送和運輸成本。例如，這在汽車行業(yè)正成為一個重要的概念。
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.
靈活且可重構(gòu)的流程和工廠車間
Flexible and reconfigurable processes and factory floors
人工智能還可用于優(yōu)化制造流程，使其更加靈活且可重構(gòu)。當(dāng)前需求可以決定工廠車間布局并生成流程，這也可以用于未來需求。然后，這些模型可用于比較和對比。分析結(jié)果決定是減少大型增材制造機器的數(shù)量更好，還是使用大量小型機器更好，后者可能成本更低，并且在需求放緩時可以轉(zhuǎn)移到其他項目?！凹僭O(shè)”分析是人工智能的常見應(yīng)用。
模型將用于優(yōu)化車間布局和流程排序。例如，增材制造部件的熱處理可以直接在3D打印機上進行。材料可能是預(yù)回火的，也可能需要重新回火，從而需要另一個熱循環(huán)。工程師可以運行各種假設(shè)情景來確定工廠應(yīng)該配備哪種設(shè)備——將部分流程分包給附近的其他公司可能更合理。
這些人工智能應(yīng)用可能會改變商業(yè)模式，決定一家工廠是專注于單一流程，還是同時承接多個產(chǎn)品或項目。后者將增強工廠的韌性。以航空航天業(yè)為例，這個正在經(jīng)歷低迷的行業(yè)，其制造業(yè)務(wù)或許也可以通過生產(chǎn)醫(yī)療部件來適應(yīng)。
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.
預(yù)測性維護
Predictive maintenance
人工智能在制造業(yè)的另一個重點關(guān)注領(lǐng)域是預(yù)測性維護。這使得工程師能夠為工廠機器配備預(yù)先訓(xùn)練的人工智能模型，這些模型融合了該工具的累積知識。基于來自機器的數(shù)據(jù)，這些模型可以學(xué)習(xí)現(xiàn)場發(fā)現(xiàn)的新的因果模式，從而預(yù)防問題的發(fā)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.
制造業(yè)和人工智能：應(yīng)用和優(yōu)勢
Manufacturing and artificial intelligence: Applications and benefits
結(jié)合人工智能，虛擬現(xiàn)實 (VR) 和增強現(xiàn)實 (AR) 可以幫助縮短設(shè)計時間，并通過提高生產(chǎn)線工人的速度和精度來優(yōu)化裝配線流程。
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.
設(shè)計、工藝改進、減少機器磨損以及優(yōu)化能耗都是人工智能在制造業(yè)的應(yīng)用領(lǐng)域。這場變革已經(jīng)開始。
機器正變得越來越智能，彼此之間以及與供應(yīng)鏈和其他業(yè)務(wù)自動化之間的集成度也越來越高。理想的情況是材料進，零件出，傳感器監(jiān)控著鏈條上的每一個環(huán)節(jié)。人們控制著流程，但不一定在環(huán)境中工作。這釋放了重要的制造資源和人員，使他們能夠?qū)Ｗ⒂趧?chuàng)新——創(chuàng)造新的組件設(shè)計和制造方法——而不是重復(fù)性的工作，因為重復(fù)性工作可以自動化。
與任何根本性的轉(zhuǎn)變一樣，人工智能的采用也遭遇了阻力。人工智能所需的知識和技能可能價格昂貴且稀缺；許多制造商并不具備這些內(nèi)部能力。他們認為自己在專業(yè)能力方面很高效，因此為了證明投資制造新產(chǎn)品或改進工藝的合理性，他們需要詳盡的證據(jù)，并且可能不愿擴大工廠規(guī)模。
這使得“盒子工廠”的概念對企業(yè)更具吸引力。越來越多的企業(yè)，尤其是中小企業(yè)，可以自信地采用端到端的打包流程，讓軟件與工具無縫協(xié)作，利用傳感器和分析技術(shù)進行改進。數(shù)字孿生功能讓工程師可以模擬嘗試新的制造流程，這也降低了決策風(fēng)險。
人工智能在質(zhì)量檢測中也發(fā)揮著作用，而質(zhì)量檢測會產(chǎn)生大量數(shù)據(jù)，因此非常適合機器學(xué)習(xí)。以增材制造為例：一次制造會生成多達 TB 的數(shù)據(jù)，這些數(shù)據(jù)涉及機器如何生產(chǎn)零件、現(xiàn)場條件以及制造過程中發(fā)現(xiàn)的任何問題。如此大的數(shù)據(jù)量超出了人類的分析范圍，但人工智能系統(tǒng)現(xiàn)在可以做到。適用于增材制造工具的方法也同樣適用于減材制造、鑄造、注塑成型以及其他各種制造工藝。
當(dāng)虛擬現(xiàn)實 (VR) 和增強現(xiàn)實 (AR) 等互補技術(shù)加入時，人工智能解決方案將縮短設(shè)計時間并優(yōu)化裝配線流程。生產(chǎn)線工人已經(jīng)配備了 VR/AR 系統(tǒng)，可以讓他們直觀地看到裝配過程，通過視覺指導(dǎo)來提高工作速度和精度。操作員可能戴著 AR 眼鏡，可以投射圖表來解釋如何組裝零件。系統(tǒng)可以監(jiān)控工作并向工人發(fā)出提示：你把這個扳手擰得夠多了，你擰得不夠，或者你還沒有扣動扳機。
大型企業(yè)和中小企業(yè)在人工智能應(yīng)用方面有不同的側(cè)重點。中小企業(yè)往往生產(chǎn)大量零件，而大型企業(yè)通常會組裝大量從其他地方采購的零件。也有例外；汽車公司會進行大量的底盤點焊，但也會購買和組裝其他零件，例如軸承和塑料部件。
就零件本身而言，一個新興趨勢是使用智能部件：零件中嵌入了傳感器，可以監(jiān)測自身的狀況、壓力、扭矩等。這個想法對汽車制造業(yè)尤其具有啟發(fā)性，因為這些因素更多地取決于汽車的駕駛方式，而不是行駛里程；如果每天都要經(jīng)過很多坑洼，則可能需要更多的維護。
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 使用冷金屬熔合增材技術(shù)將傳感器嵌入機器。如圖所示的嵌入式傳感器可以發(fā)送各種數(shù)據(jù)，例如溫度和其他環(huán)境條件。圖片由 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.
智能組件可以通知制造商其已達到使用壽命或需要檢查。部件本身無需外部監(jiān)控這些數(shù)據(jù)點，而是會偶爾與人工智能系統(tǒng)進行核對，報告正常狀態(tài)，直到出現(xiàn)問題，需要關(guān)注。這種方法減少了系統(tǒng)內(nèi)部的數(shù)據(jù)流量，而數(shù)據(jù)流量在規(guī)?；l(fā)展后可能會嚴(yán)重拖累分析處理性能。
人工智能增值的最大、最直接的機會在于增材制造。增材制造工藝是主要目標(biāo)，因為其產(chǎn)品價格更高，體積更小。未來，隨著人類不斷發(fā)展和完善人工智能，它很可能在整個制造業(yè)價值鏈中發(fā)揮重要作用。
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.
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