20世纪80年代，所有主要制造商都实现了设备自动化，并建立了复杂的计算机系统，以支持其员工在世界市场上竞争。在这十年里，竞争将更加激烈。除了人类智能、计算机计算能力和机电系统的良好可重复性，还需要制造设备的自学习能力。介绍了反向传播神经网络，讨论了它的三个应用。这些应用包括准静态机床误差建模、铣削操作中刀具间隙检测和车削颤振发展预测。

ALL THE MAJOR MANUFACTURERS AUTOMATED THEIR FACILITIES AND ESTABLISHED COMPLEX COMPUTER SYSTEMS TO SUPPORT THEIR PERSONNEL IN THE 1980S TO COMPETE IN THE WORLD MARKETS. IN THIS DECADE, COMPETITION WILL BE EVEN MORE DEMANDING. IN ADDITION TO HUMAN INTELLIGENCE, COMPUTATIONAL POWER OF COMPUTERS, AND EXCELLENT REPEATABILITY OF ELECTROMECHANICAL SYSTEMS, THE SELF-LEARNING CAPABILITY OF THE MANUFACTURING EQUIPMENT WILL BE NEEDED. BACK PROPAGATION NEURAL NETWORKS ARE INTRODUCED AND THREE APPLICATIONS ARE DISCUSSED. THESE APPLICATIONS ARE MODELING OF QUASI STATIC MACHINE TOOL ERROR, DETECTION OF TOOL BREAAKAGE IN MILLING OPERATIONS, AND PREDICTION OF CHATTER DEVELOPMENT IN TURNING.