人工智慧在製造業的應用

隨著智慧製造熱潮的到來，人工智慧應用已經貫穿設計、生產、管理和服務等製造業的各個環節。

人工智慧的概念第一次被提出，是在20世紀50年代，距今已六十餘年的時間。然而直到近幾年，人工智慧才迎來爆發性的成長，究其原因，主要在於日益成熟的物聯網、大數據、雲端運算等技術。

物聯網使得大量數據能夠被即時獲取，大數據為深度學習提供了數據資源及演算法支撐，雲端運算則為人工智慧提供了靈活的運算資源。這些技術的有機結合，驅動著人工智慧技術不斷發展，並且取得了實質的進展。 AlphaGo與李世石的人機大戰，更是將人工智慧推到了風口浪尖，引爆了新一輪的人工智慧熱潮。

此後的近幾年，關於人工智慧的研究和應用開始遍地開花。隨著智慧製造熱潮的到來，人工智慧應用已經貫穿設計、生產、管理和服務等製造業的各個環節。

PART ONE

產品缺陷偵測

由於深度學習的應用，製造業生產線的缺陷偵測過程變得越來越聰明。深度神經網路整合使電腦系統可以識別諸如刮擦、裂縫、洩漏等表面缺陷。

這個過程，透過應用影像分類，物件偵測和實例分割演算法，由資料科學家以給定缺陷檢測任務訓練視覺檢查系統來完成。深度學習驅動偵測系統，與高光學解析度相機和GPU結合，形成超越傳統機器視覺的感知能力。

例如，可口可樂建構的基於AI視覺檢測程序，已經可以診斷設施系統並檢測產線問題，及時把檢測到的問題回饋給技術專家進行解決。基於此，未來品質偵測人員被李開復列為將被人工智慧取代的工種。

新的偵測技術包括合成資料、遷移學習和自監督學習等。在合成資料中，生成對抗網路(Generative Adversarial Networks，GAN）資料生成工具會檢查質檢員認為「正常」的影像，並合成缺陷影像，用於訓練人工智慧模型。同時，遷移學習與自監督學習，用於解決特定問題。隨著數據積累，缺陷檢測演算法更加精確。

PART TWO

#智慧分類

##製造業有許多需要分類的作業，如果採用人工的作業，速度緩慢且成本高，而且還需要提供適當的工作溫度環境。如果採用工業機器人進行智慧分類，可以大幅降低成本，提高速度。

以分類零件為例。需要分類的零件通常並沒有被整齊擺放，機器人雖然有攝影機可以看到零件，但卻不知道如何把零件成功地撿起來。在這種情況下，利用機器學習技術，先讓機器人隨機進行一次分類動作，然後告訴它這次動作是成功分撿到零件還是抓空了，經過多次訓練之後，機器人就會知道按照怎樣的順序來分類才有更高的成功率；分類時夾哪個位置會有更高的撿起成功率；知道按照怎樣的順序分揀，成功率會更高。經過幾個小時的學習，機器人的分類成功率可以達到90%，和熟練工人的程度相當。

PART THREE

#庫房管理與物流

例如京東物流某庫房，需依照訂單和出貨地分揀成品，同時回收空的料箱，並把部分廢料、廢棄物丟進廢料堆放處。這項工作每個班次由兩名工人合作完成，倉庫內有粉塵和噪音，每天累計重複分揀動作要執行2000-3000次，雖然重物搬運由機械手完成，但仍是強度大、環境差、技術含量低的重複性工作。

The company uses a robot to replace two workstations that work in three shifts a day. The robot is equipped with a machine vision system. It can scan RFID codes when sorting orders and shipping locations. The judgment of finished products, empty boxes, and waste materials is learned by AI. The algorithm gradually improved the recognition rate. The initial recognition rate was only about 62%, and each shift required a worker to fill in the gaps. As data accumulated, the AI ​​recognition model continued to improve. After 9 months, the comprehensive recognition rate increased to 96%. The identification of finished products and the sorting of delivery places are completely accurate. There is no need to keep people in the warehouse to fill the vacancies. Only a very small number of empty boxes can be picked out during waste recycling.

PART FOUR

##Manufacturing

Ford once boasted : No matter what car you want, I will only produce it in black. This is a typical portrayal of assembly line mass production, but if Ford continues to think this way in the current situation, Ford cars will only die. Because there is more and more personalization now, but the cost of personalized production is very huge, then the only way is mass customization, which uses personal consumption data to analyze and form comprehensive orders, and then the platform distributes it for mass production to reduce costs. The unit price of finished products is the path Rhino Manufacturing is currently taking. However, although e-commerce has a large amount of consumer behavior data, the data always lags behind actual demand. This application scenario requires the analysis platform to maximize the accuracy in order to increase the accuracy.

PART FIVE

##Remote Operation and Maintenance Service

Remote Operation The dimensional platform uses technologies such as the Internet of Things, big data, and artificial intelligence algorithms to monitor key parameters of the production process and production equipment in real time, and provide timely alarms for faults. Functions such as predictive maintenance and auxiliary decision-making supported by industrial big data analysis and artificial intelligence algorithms can further reduce personnel travel and shutdown delays caused by unplanned downtime, making the operation and maintenance of industrial enterprises less manned, unmanned, and more efficient. Remote model change.

Looking around the world, companies involved in the field of industrial artificial intelligence have already proven the unique value of this technology. Artificial intelligence technology has great potential in improving the productivity, efficiency, quality and cost of enterprises, and will undoubtedly become a new engine empowering the future manufacturing industry. However, enterprises’ AI transformation journey has a long way to go. Companies that are the first to awaken must strengthen their beliefs, practice their internal skills diligently, and set out immediately to expand their territory in the field of industrial artificial intelligence, striving to turn themselves into a beacon shining the light of future intelligent manufacturing.

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