【正文】 均使用串口與計算機進(jìn)行通信。對于兩個進(jìn)行通行的端口，這些參數(shù)必須匹配： a，波特率：這是一個衡量通信速度的參數(shù)。 b，數(shù)據(jù)位：這是衡量通信中實際數(shù)據(jù)位的參數(shù)。 c，停止位：用于表示單個包的最后一位。對于偶和奇校驗的情況，串口會設(shè)置校驗位（數(shù)據(jù)位后面的一位），用一個值確保傳輸?shù)臄?shù)據(jù)有偶個或者奇?zhèn)€邏輯高位。主要配置串口波特率、數(shù)據(jù)位、停止位、奇偶校驗位。寫入節(jié)點的程序框圖如圖36所示。圖 37讀取節(jié)點框圖在完成對VISA串口初始化、VISA串口寫入、VISA串口讀取的配置后，串口的收發(fā)就能夠進(jìn)行。CPK具有一定的評級標(biāo)準(zhǔn)，利用公式計算出來的CPK值愈大，代表過程綜合能力愈好，并可根據(jù)其值所在的等級做出相應(yīng)的調(diào)整對策[11]。評等參考：Ca值愈小，品質(zhì)愈佳。通過得到的SPC參數(shù)，對加工的工件進(jìn)行系統(tǒng)的檢測。正常波動是偶然性原因（不可避免因素）造成的。首先得根據(jù)下位機通過串口傳送來的數(shù)據(jù)，將這些數(shù)據(jù)存放到全局變量3中，等待后面的計算。Ca的計算框圖Cp的計算框圖Cpk的計算框圖圖 39 SPC參數(shù)計算程序框圖由于要對下位機通過串口上傳的數(shù)據(jù)進(jìn)行不斷的測量，顯示，統(tǒng)計，所以應(yīng)該在整個程序外添加一個循環(huán)結(jié)構(gòu)。上位機軟件部分前面板的界面如圖311所示。 信號處理濾波器設(shè)計由于在數(shù)據(jù)采集通道中容易產(chǎn)生各種噪聲，從而對測量數(shù)據(jù)的精度造成不同程度的影響，導(dǎo)致產(chǎn)生誤差。而在上位機程序編寫的部分，主要介紹了兩塊：串口通信部分和SPC參數(shù)統(tǒng)計部分。該檢測儀器在工作時，先在上位機的組態(tài)表達(dá)式中輸入所需的函數(shù)，接著，下位機的傳感器會接收到一定的信號，這個信號經(jīng)過一系列的轉(zhuǎn)換處理后經(jīng)串口傳輸給上位機，此時，結(jié)合輸入的函數(shù)即可在窗口中顯示所需要的對應(yīng)值。而且電路簡單，具有成本低、采集速度快、操作方便的特點，有較高的使用價值。參考文獻(xiàn)1 [D].天津大學(xué),:1~32 Tucker W Statistical Process Control an Elaboration[J].Technometrics, :7~83 李玉玲基于過程能力指數(shù)的工序質(zhì)量控制研究[D].重慶大學(xué),:3~54 李朝青單片機原理及接口技術(shù)第三版[M].:152~1645 [M].:164~2386 . 集成測試技術(shù)與虛擬式儀器面向21世紀(jì)的儀器系統(tǒng)[M]:102~1157 楊樂平，李海濤，楊磊. LABVIEW程序設(shè)計與應(yīng)用[M].第二版 :122~2848 趙勇，李海濤，[M].:134~1589 虛擬儀器及LabVIEW概況[J]2002. 9:1~510 National Instruments Corp PXI E Series User Manual[J] 1998:1~311 虛擬儀器編程基礎(chǔ)——VISA標(biāo)準(zhǔn)與應(yīng)用航空計測技術(shù)[J]:23~3512 測試儀器的發(fā)展方向——以TCP/IP作為通訊方式的虛擬儀器測試系統(tǒng)工業(yè)儀表與自動化裝置[J]:33~37附錄A:下位機電路原理圖附錄B:上位機軟件運行圖附錄C:外文文獻(xiàn)原文A FRAMEWORK of an INTELLIGENT REALTIME DECISION SUPPORT SYSTEM for PROCESS CONTROL in ELECTRONICS ASSEMBLYAbstractThis paper reports a framework of an intelligent realtime decision support system for process control in electronics assembly. A process control is an essential ingredient to ensure a successful operation. Most process control systems can neither evaluate environmental variables nor learn past successful experience. The major objective of this system is to provide a decision support for process control in PCB assembly. The system will be expected to have the abilities to acmodate environmental and process variability and to learn past successful experience. The major methodologies used in the system are the statistical process control (SPC), artificial neural networks (ANNs) and expert systems (ESs).1. IntroductionThe major functions of PCBs are to provide electronic connection among electronic ponents and external circuits, and to hold the electronic ponents (Groover, 1996). A PCB assembly is the major process in electronics manufacturing. Compared with other types of manufacturing processes, the PCB assembly process consists of several plex steps that have strong interrelationships with each other and a large number of ponents are assembled on PCBs. There are approximately 2000 solder joints in an assembly. In practice, a demand failure rate for overall processes, including adhesive dispensing, insertiod placement, and soldering actions, has to be less than 20 defects per million (dpm). If the processes are not adequately controlled, large efforts in testing and repairing that may cost 30% to 50% of total manufacturing costs must be undertaken. A strong mitment to robust process control is required (Feldmann et al, 1994).The difficulties of the process control in PCB assembly are (Conway et al, 1990。 (3) to infer theprocesses with environmental variables.2. Research ObjectivesThe objective of this research is to design and to implement an intelligent realtime decision support system for PCB assembly process control. The expected functions of the system include abilities (1) to identify process performance by monitoring process variables, (2) to decide necessary adjustments, and (3) to make suggestions by using the ES and the ANNs for different steps of PCB assembly. The system will have a generalized architecture that allows users to create customized systems for different steps such as adhesive dispensing, cleaning, etc. The inference process is expected to be accurate and fast enough to response optimal or nearoptimal values of process input variables.3. Proposed MethodologiesThis proposed realtime decision support system for PCB assembly process control will be able to analyze the output data of a process and recognize the data if there is any process output that exceeds desired specification or any pattern that shows the process is going to be out of control. If the process is not declared out of control, the process input would be used for the production process. If there is any signal that shows a situation of out of control, the output data will be sent to the inference engine to detect possible reasons or faults. After the reasons or faults have been detected, remendations and explanations will be provided to the users. Finally, users can adjust the process input based on the remendations. The process control flow is shown in Figure 1 Statistical process control (SPC)Statistical process control (SPC) is a proven statistical technique for any process to detect the signals that show the situations of out of control or the patterns of potential problems. It consists of systematic collections of data related to a process and graphical summaries of the data for analysts39。 Cecil et al, 1994). These investigations are still short of the following capabilities: (1) to be applied in overall processes。感謝測控081班所有的同學(xué)，四年來對我無微不至的照顧和的關(guān)懷，伴我度過充實而美好的大學(xué)時光?！敖Y(jié)論”以前的所有正文內(nèi)容都要編寫在此行之前。以及根據(jù)參數(shù)判斷和對生產(chǎn)過程等級評定。如圖312所示： (38) (39)圖 312 濾波器用MATLAB通過仿真實驗我們能夠清晰的看到經(jīng)過濾波器和未經(jīng)濾波器濾波的信號的巨大差距，我們用正弦信號和隨機信號疊加，其中隨機信號作為噪聲信號，仿真實驗的結(jié)果如圖313：圖 313 濾波器仿真實驗 本章小結(jié)本章內(nèi)容主要介紹了上位機編程語言的選擇。再根據(jù)SPC各個參數(shù)的評等參考，對工業(yè)現(xiàn)場生產(chǎn)過程進(jìn)行評價（如A等，B等，C等，D等），結(jié)合評價等級，確定對生產(chǎn)過程的處理決定。通過對上位機界面的操作就能夠?qū)崿F(xiàn)上位機軟件部分各個不同的功能，如接收下位機發(fā)來的數(shù)據(jù)，進(jìn)行數(shù)據(jù)波形實時顯示和SPC參數(shù)統(tǒng)計。通過原先規(guī)定的兩個全局變量5，和規(guī)格上限USL以及規(guī)格下限LSL，對SPC參數(shù)Ca、Cp、Cpk、CPU、CPL進(jìn)行計算。從而利用SPC參數(shù)提高生產(chǎn)效率，降低次品率。它是由人、機器、材料、方法和環(huán)境等基本因素的波動影響所致。通過由上位機經(jīng)過SPC過程統(tǒng)