Artificial intelligence (AI) and machine learning (ML) are quickly becoming common parts of the manufacturing process. The latest technologies are simplifying the way that engineers solve problems, reducing downtime and increasing production throughput.

一个 rtificial人工智能(AI)和机器学习(ML)正在Swift成为制造过程中的公共部分。 最新技术正在简化工程师解决问题的方式，减少停机时间并提高生产吞吐量。

Many of the benefits of AI and ML technologies stem from the ability to replace experience-based decision-making with fact-based decision-making. Precision and efficiency are essential characteristics in the manufacturing industry. Engineering decisions are often made based on a careful analysis of the facts.

AI和ML技术的许多好处源于将基于经验的决策替换为基于事实的决策的能力。 精度和效率是制造业的基本特征。 工程决策通常是基于对事实的仔细分析而做出的。

When you are dealing with components that require precise dimensions and large-scale production runs, you cannot rely on guesses or estimates. Unfortunately, engineers do not always have the luxury to analyse every detail.

当您处理需要精确尺寸和大规模生产运行的组件时，您不能依靠猜测或估计。 不幸的是，工程师并不总是拥有分析每个细节的能力。

There are times when you need to use your experience to make decisions, which increases the risk of defects, waste, and delays. Machine learning is helping to remove those risks by eliminating the need for experience-based decisions.

有时候，您需要利用自己的经验来做决定，这会增加缺陷，浪费和延误的风险。 机器学习通过消除对基于经验的决策的需求，正在帮助消除这些风险。

Developing Products with More Value

开发更具价值的产品

Artificial intelligence can enhance manufacturing at every stage, starting with the product development stage. Engineers can use input from AI software to aid in the development of products that deliver more value to customers.

从产品开发阶段开始，人工智能可以在每个阶段增强制造业。 工程师可以使用来自AI软件的输入来协助开发产品，从而为客户带来更多价值。

AI software is already being used to gain more insight into customer behaviour and needs. Manufacturers and engineers can use this insight to get a better understanding of the end users. They can eliminate unnecessary features and focus on meeting customer demands. After analysing the needs of customers, AI and ML technologies can assist with the design of the product.

人工智能软件已被用于获得对客户行为和需求的更多见解。 制造商和工程师可以利用此见解来更好地了解最终用户。 它们可以消除不必要的功能，并专注于满足客户需求。 在分析了客户的需求之后，人工智能和机器学习技术可以协助产品设计。

For example, instead of using experience to determine the potential risks or costs associated with adding or removing a feature, engineers can rely on software to evaluate the decision. Companies may harness the power of big data to strengthen their decision-making processes during product planning, strategising, and modelling.

例如，工程师无需依靠经验来确定与添加或删除功能相关的潜在风险或成本，而是可以依靠软件来评估决策。 公司可以利用大数据的力量在产品计划，战略制定和建模过程中加强其决策过程。

Improving Quality Control and Reducing Defects

改善质量控制并减少缺陷

Smart manufacturing is helping to reduce the need for manual input and labour when it comes to quality control and quality assurance processes. Quality control is intended to identify defects and prevent faulty products from reaching customers. Machine learning technology assists with finding defects and the root causes of them.

当涉及质量控制和质量保证过程时，智能制造有助于减少对人工输入和人工的需求。 质量控制旨在识别缺陷并防止有缺陷的产品到达客户手中。 机器学习技术有助于发现缺陷及其根源。

Traditionally, an engineer may analyse the output of a production run to detect defects. They then review each process to determine the cause, which is a time-consuming task. Even with a thorough quality control process, engineers may overlook certain details or fail to catch the problem.

传统上，工程师可以分析生产运行的输出以检测缺陷。 然后，他们检查每个过程以确定原因，这是一项耗时的任务。 即使采用了彻底的质量控制流程，工程师也可能忽略了某些细节或未能发现问题。

Engineers no longer need to use their instinct and experience to detect faults and defects. Machine learning software monitors machinery and product data throughout the entire manufacturing process. This technology can even help predict quality control issues before they arise.

工程师不再需要凭直觉和经验来检测故障和缺陷。 机器学习软件在整个制造过程中监视机械和产品数据。 这项技术甚至可以帮助在质量控制问题出现之前对其进行预测。

Revolutionising Predictive Maintenance

革命性的预测性维护

One of the roles of the engineer is as a maintenance worker. They need to ensure that machinery and equipment can meet production needs to prevent bottlenecks and reduce downtime.

工程师的角色之一是作为维护人员。 他们需要确保机械设备能够满足生产需求，以防止瓶颈并减少停机时间。

Traditional procedures involve manually set thresholds and scheduled maintenance for handling the upkeep of machinery and equipment. Engineers may also use their gut and experience to estimate how frequently machines require inspections.

传统的程序包括手动设置阈值和安排维护以处理机器和设备的维护。 工程师也可以利用自己的直觉和经验来估计需要检查机器的频率。

AI and machine learning software are changing the maintenance process. Along with constantly monitoring the output of the machinery to detect product defects, artificial intelligence can detect changes in the performance of the machinery. This allows the software to predict when a machine may require maintenance.

AI和机器学习软件正在改变维护过程。 除了不断监视机器的输出以检测产品缺陷外，人工智能还可以检测机器性能的变化。 这使软件可以预测何时需要维护机器。

AI software may also assist with reallocating resources to take a machine offline without impacting production. These decisions could take an engineer an incredible amount of time and may not yield satisfactory results.

AI软件还可以协助重新分配资源以使机器脱机而不会影响生产。 这些决定可能会使工程师花费大量时间，并且可能不会产生令人满意的结果。

ML software provides instant feedback, allowing engineers to adapt quickly to unexpected maintenance issues and other setbacks. Experts predict that these benefits will lead to a 38% increase in the use of machine learning for predictive maintenance.

ML软件提供即时反馈，使工程师能够快速适应意外的维护问题和其他挫折。 专家预测，这些好处将导致 机器学习用于预测性维护的使用率增加38％ 。

Increasing the Efficiency of Manufacturing

提高制造效率

The combination of advantages discussed helps create more efficient manufacturing processes. There is less of a need for human input at every stage from product development to production and quality control.

所讨论的优点的组合有助于创建更有效的制造过程。 从产品开发到生产和质量控制的每个阶段都不需要人工投入。

Engineers can use big data to aid product development, quality control, and predictive maintenance. This takes some of the guesswork out of engineering, which saves time and reduces human errors.

工程师可以使用大数据来帮助产品开发，质量控制和预测性维护。 这消除了工程上的一些猜测，从而节省了时间并减少了人为错误。

Using AI equipment to automate manufacturing processes provides machine learning software with consistently accurate data. Instead of requiring engineers to spend hours evaluating the outputs of sensors and equipment, the software can quickly compile and review data spanning multiple years.

使用AI设备自动化制造过程可为机器学习软件提供始终如一的准确数据。 该软件无需花费工程师数小时来评估传感器和设备的输出，而是可以快速编译和查看跨越多年的数据。

Engineers can easily adjust production processes and designs to accommodate the insight provided by the software. This results in faster response to potential issues and the ability to adapt manufacturing processes for custom orders.

工程师可以轻松调整生产流程和设计，以适应软件提供的见解。 这样可以更快地响应潜在问题，并能够针对定制订单调整制造流程。

ML software can also be used as a simulation tool within digital twins. After obtaining data from various sources, the ML software creates a virtual representation of the manufacturing process and simulates multiple scenarios. The software “learns” from these scenarios to help improve the efficiency of any manufacturing stage.

ML软件也可以用作数字双胞胎中的仿真工具。 从各种来源获取数据后，ML软件将创建制造过程的虚拟表示并模拟多种情况。 该软件从这些情况中“学习”，以帮助提高任何制造阶段的效率。

Last Thoughts on AI and ML in Engineering and Manufacturing

关于工程和制造中AI和ML的最新思考

People often associate AI and ML with automation on the manufacturing room floor. Smart manufacturing is helping to reduce the need for manual operations. However, the use of machine learning extends through every stage of manufacturing.

人们通常将AI和ML与生产车间的自动化联系在一起。 智能制造有助于减少手动操作的需求。 但是，机器学习的使用遍及制造的每个阶段。

Engineers now have powerful tools that make their jobs easier. The assistance of ML software can aid or automate a wide range of decision-making tasks. From planning and modelling a product to monitoring its production run, engineers can use big data to boost efficiency and quality.

工程师现在拥有功能强大的工具，使他们的工作更加轻松。 ML软件的协助可以协助或自动化各种决策任务。 从产品的计划和建模到监视其生产运行，工程师可以使用大数据来提高效率和质量。

With enterprises increasing AI spending by 62% in the last year, more companies are adopting these technologies. As smart manufacturing becomes the standard, you can expect new benefits to appear.

去年 ，随着企业将AI支出增加62％ ，越来越多的公司开始采用这些技术。 随着智能制造成为标准，您可以期待出现新的好处。

In the next few years, AI and ML may completely change the way that engineers work. Instead of depending on their experience, they can rely on the facts provided by big data.

在未来几年中，AI和ML可能会完全改变工程师的工作方式。 他们可以依靠大数据提供的事实，而不必依靠他们的经验。

Feel free to also check out our other posts:

还可以查看我们的其他帖子：

• Machine Learning Projects in Manufacturing: Expectations vs. Reality

  制造业中的机器学习项目：期望与现实

• The Future of Six Sigma — Machine Learning Is Redefining Industrial Precision

  六西格码的未来-机器学习正在重新定义工业精度

• Digital Twins in Quality Control — What If You Could Predict Changes in Quality

  质量控制中的数字双胞胎—如果可以预测质量的变化该怎么办