讓製造更具智慧:AI 和印刷電路板世界

PCB 製造業正呈現出巨大的機遇,能讓您運用人工智慧 (AI) 簡化生產流程,並以前所未有的方式改善生產結果。AI 可以說是市場顛覆者;PCB 製造中引進 AI 的重要性就如同邁向工業 4.0(或「智慧工廠」),自動化系統彼此之間或與人類能即時地通訊和合作、分散決策制定流程並提供眾多優勢。然而,想要在 PCB 領域成功實現 AI,必須在 PCB 製造和 AI 基礎知識方面擁有豐富且深厚的專業知識,而這正是奧寶科技的優勢所在。

什麼是 人工智慧 (AI)?

在過去十年中,AI 已經從未來主義概念進展成真正可應用、相關和有效的技術。此突破主要歸功於增多且不算貴的計算能力、更複雜的學習演算法及大數據的出現,大數據本身則提供了讓AI 系統「學習」的基礎資訊。

AI 描述了模仿人類認知功能的機器或軟體,例如解決問題和學習。許多重要的創新正在成為 AI 的附加產物,包括機器學習和深度學習。機器學習是一種使用各種演算法技術的 AI,能讓電腦使用資料來提高任務效能,而不必明確地設計程式來完成。在 PCB 製造中,機器學習帶來了許多優勢,包括改進操作、降低廢品率、優化生產線營運、減少「非精密勞動力」,以及更有效地管理資產、庫存和供應鏈。這些相關功能皆與工業 4.0 密切相關。

深度學習是一種更為複雜的 AI 形式,讓電腦可以非常有效地學習以複雜的方式呈現出來的資料中的深入資訊、模式和背景,而這些都可以用來改進製造流程。深度學習利用多方位的多層人工神經網路模仿人類大腦的學習、理解和推斷能力。

奧寶科技能為 PCB 製造商帶來什麼樣的 AI

AI 非常複雜,需要在技術本身,以及使用 AI 的行業領域中擁有豐富的專業知識。作為 PCB 製造和 AI 專家,奧寶科技目前正在為 PCB 製造業的工業 4.0 開發 AI 解決方案。

要成功實施 AI 解決方案,必須通過嚴格的操作整合以融合流程專業知識。若要在複雜的製造環境中建立 AI 系統,需要經歷廣泛而漫長的培訓階段。這些培訓過程對操作要求非常高,且需要投入大量的計算和人力才能獲得最佳性能。奧寶科技可結合其 AI 專業知識,確保能夠優化 AI 神經網路架構開發的關鍵培訓階段。

資料是關鍵

在上述 各種形式的AI中,資料存取都是成功和效率的關鍵因素。若欠缺極高品質的標示資料,AI 就無法成功。例如,在 PCB 製造中,瑕疵分類是自動光學檢測 (AOI) 解決方案的關鍵領域。奧寶科技 AOI 電腦會將可能有瑕疵的 PCB 影像傳送到遠端多重影像驗證 (RMIV) 站台,站台的操作人員會檢視影像,然後將其分類為「真正的瑕疵」或「假點」。

然而,操作人員很昂貴,且很容易在分類時出錯。當用於瑕疵分類的 AI 解決方案從操作人員的數千個決策中學習後,就可以自動並以一致的精確度做判斷。此類 AI 系統依賴於準確的資料模式,以便隨著時間推移學習正確的演算法行為。必須仔細驗證和輸入分類資料(手動分類瑕疵的範例),才能彰顯 AI 的優勢。即使資料出現最小的變化,也可能導致 AI 系統造成遊戲規則改變的結果。

協同合作實現AI成功

雖然資料品質是 AI 成功的必要條件,但每個客戶與其 AI 解決方案供應商之間的密切合作,對于長期成功也至關重要。鑑於 AI 的複雜性,解決方案供應商必須應用其 PCB 製造流程和 AI 知識,指導客戶實施 AI 解決方案。例如,在對瑕疵進行分類時,奧寶科技及其客戶將合作找出每個操作人員分類評估結果背後的深刻意涵,並集思廣益以創建一個有意義的系統,從而排定應優先檢測的 PCB 特性。客戶的協作和參與度才能為 AI 引擎產生出最佳的結果。

未來

鑑於 AI 將不可避免地為 PCB 製造商帶來巨大優勢,奧寶科技持續在研發領域投注大量心力,完美整合 AI 與奧寶科技智慧工廠-公司的工業 4.0 解決方案,以及其他解決方案。在此過程中,奧寶科技正在評估許多方法,以便將 AI 應用於总体製造,透過測試進行學習,並協助推動行業朝更智能、更強大的製造流程邁進,從而協助其提高產量並降低營運成本。

作者:奧寶科技演算法團隊經理 Dima Pundik

 

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