Visual inspection is the oldest method for quality control. Humans excel at detecting cracks, deformities, subtle flaws, and missing parts. Depending on the product, we can rely on taste and smell to spot differences. We adjust for unpredictability, are easily trained, and can quickly learn by example.

However, we make mistakes when we get tired, bored, and distracted. Human manual inspection tasks can exhibit error rates of up to 30%. Often, these “errors” are actually false positives where an inspector had started to question their decision-making. Our error rate is even higher for assembly tasks.

这些错误导致质量问题，并且由于浪费，额外的筛查和制造停机时间提高成本。考虑到这些成本，以及与产品质量差的相当大的金融，品牌，甚至潜在的消费者健康风险，对AI的技术越来越越来越大，为手动任务提供决策支持。特别是，这些技术非常适合较低的体积，更高的值和定制产品，其中全自动检查并不具有成本效益或实用。

AI品牌管理包装和标签

乳制品酿酒厂是一家加拿大精神制造商，拥有从乳制品副产品生产伏特加的独特过程。品牌外观在消费者选择中发挥着重要作用，制造商对抗更深入的营销预算的较大球员竞争。

此外，在食品和饮料市场中运营带来了另一套风险。大约60％的市场公司经历了召回。虽然召回与食品质量产生媒体头条新闻，并且可能会显着损害品牌的声誉，而且通常来自美国食品和安全检查的召回的三分之一的召回与包装和标签错误有关。这些误用或错误标记的产品可能不会影响消费者安全性，但它们可能导致昂贵的发货延误和返工的制造商。

对于酿酒厂来说，主要关注的是保持一致的品牌外观，因此可以确保商店货架上的高级斑点。酿酒厂使用旧式牛奶瓶后的瓶子，具有独特和引人注目的标签。这就是酿酒厂所有者称之为“货架谈话者”。瓶子讲述了业务的乳制品背景，具有独特的包装，为消费者脱颖而出，因为他们将货架留在当地商店。一个良好的寻找产品，有助于建立Cosnumer品牌意识和忠诚度。

The bottle has three brand elements, with a main label and cap label applied by machine. A human then has to accurately place an emblem logo that visually aligns with corresponding brand elements on the other labels to ensure a consistent and appealing shelf display. With multiple products and short manufacturing runs, it’s uneconomical for the distillery to fully automate its labelling process.

Over a shift, the emblem placement would begin to shift as the operator got tired or was focusing on other tasks. Mistakes were often not noticed until the final packaging, when staff was then tasked with manually removing and replacing labels. This resulted in downtime, production delays, and additional costs. Worse, there was always the risk a poorly labelled product could reach the store shelves and negatively impact a consumer buying decision.

基于AI的视觉检查为运营商提供了决策支持，以帮助消除标记错误。该系统包括相机，边缘处理，显示面板和来自常见检查要求的预打包AI插件。预设的检查技巧很容易培训，以验证组件，检查标签和检查组件，或针对特定要求定制。

在不需要任何编程技能的情况下，酿酒厂质量管理器和操作员培训了图像比较插件以增加其标签过程的决策支持。只需一个已知的好产品的一个图像 - “金色参考” - 系统会自动识别瓶子上的关键品牌元素。插件已被定制，然后在视觉显示屏上添加图形叠加，突出显示并指导操作员奖章标签的正确放置。

AI-based visual inspection ensures brand consistency and accuracy for the distillery, as well as cost-savings as labelling does not have to removed and replaced due to human error. The technology is also being used by the manufacturer as a training tool for new operators, so they can quickly understand the proper positioning of brand elements on the bottle and the difference between “good and bad” products.

With expanding production, the quality manager or operator can easily update the visual inspection system with additional “golden references” to provide labelling guidance for new bottles, labelling, and packaging. The operator simply chooses the correct plug-in for the product to be inspected. An additional image save plug-in could also be used to capture images of products at various stages of production for batch tracking. This will also provide the manufacturer with key data related to their manual assembly and inspection processes for root cause analysis and productivity management.

As the distillery adds more automation to its production, the visual inspection system can provide a valuable quality control (QC) check for in-process or finished goods to ensure all machines and humans are operating in sync. With this approach, the operator is using the AI system to inspect a number of bottles in various stages of production to ensure brand accuracy. This helps remove stressful subjective decision-making for operators, and will increase production as errors can be identified well before final packaging.

电子检查和图像比较

制造商可以将AI决策支持添加到进程中的最快方式之一是使用图像比较。目视检查系统将制造的产品与“金色大师”进行比较，并视觉突出显示器上的差异和偏差。

DICA is an electronics contract manufacturer located just outside of Ottawa, Ontario that services an expansive list of healthcare, industrial controls, telecommunications, security, and digital imaging companies located in “Silicon Valley North”. The company specializes in high quality electronic assembly services for the small-to-medium volume market.

Serving a high-value, lower volume market can pose inspection challenges for the company, as not all products are well-suited to automated processes. As a result, a number of products are primarily inspected by human operators. The company prides itself on its exemplary record for product quality, and views the automated visual inspection system as a method to add decision-support for its inspectors.

Like the distillery, the electronics manufacturer has trained the image compare plug-in with a known, good image of a final product. Operators and quality control staff have trained multiple image compare plug-ins to inspect different products. The AI capabilities are used to match the approved layout and final production for electronic assemblies. The system quickly compares the placement of components on the circuit board, and highlights differences and deviations for the human inspection before it moves to the next step in the manufacturing process or to final packaging.

制造商还用于质量检查来自供应商的传入组件的质量检查。此外，制造商使用该系统捕获并保存每个印刷电路板的图像。该数据与用于库存和装运管理和批量跟踪的可追溯性系统共享，如果在该字段中检测到潜在错误，则有助于减少根时间分析时间和成本。

对于酿酒厂和电子制造商来说，基于AI的视觉检查系统降低了人类运营商的主观决策，以帮助确保一致性和准确性。在生产的不同阶段可以检测到错误，并且系统可以快速扩展其他产品，不需要编程技能。在较低的卷或定制制造应用中，视觉检测系统是运营商快速和成本有效地利用新的AI技术以确保质量的关键方法。