Although intelligent machine learning techniques have been used for input-output modeling of many different manufacturing processes, these techniques map directly from the input process parameters to the outputs and do not take into consideration any partial knowledge available about the mechanisms and physics of the process. In this paper, a new approach is presented for taking advantage of the partial knowledge available about the mechanisms of the process and embedding it into the neural network structure. To validate the proposed approach, it is used to create a forward prediction model for the process of electrochemical micro-machining (μ-ECM). The prediction accuracy of the proposed approach is compared to the prediction accuracy of pure neural structure models with different structures and the results show that the Neural Network (NN) models with embedded knowledge have better prediction accuracy over pure NN models.

It can be observed from the experimental data of different processes that different process parameter combinations can lead to the same performance indicators, but during the optimization of process parameters, using current techniques, only one of these combinations can be found when a given objective function is specified. The combination of process parameters obtained after optimization may not always be applicable in actual production or may lead to undesired experimental conditions. In this paper, a split-optimization approach is proposed for obtaining multiple solutions in a single-objective process parameter optimization problem. This is accomplished by splitting the original search space into smaller sub-search spaces and using GA in each sub-search space to optimize the process parameters. Two different methods, i.e., cluster centers and hill and valley splitting strategy, were used to split the original search space, and their efficiency was measured against a method in which the original search space is split into equal smaller sub-search spaces. The proposed approach was used to obtain multiple optimal process parameter combinations for electrochemical micro-machining. The result obtained from the case study showed that the cluster centers and hill and valley splitting strategies were more efficient in splitting the original search space than the method in which the original search space is divided into smaller equal sub-search spaces.

Manufacturing factories are always in pursuit of achieving higher productivity in order to satisfy the growing demands of the consumers but are often meet with challenges in achieving these goals due to various complexities. These complexities arise from the limitations of human operator's i.e. their inability to handle uncertainties, complexity, and understand/memorize large data. Intelligent manufacturing systems are positioned to yield superior results than traditional manufacturing as they are capable of analyzing, self-learning, apprehending complexity, and self-driven for quality and cost. Unlike traditional manufacturing, intelligent manufacturing systems are also able to store and analyze large amounts of production data in order to achieve higher production rate and shorten the time-tomarket. This keynote is to provide a longitudinal perspective on various aspects of recent achievements in intelligent design, scheduling, maintenance, and control. For each aspect, concept, requirement, and application implemented, methodologies deployed are also presented along with their limitations and future research directions. This keynote also presents fundamental concepts and futuristic views that the authors are currently implementing in an intelligent manufacturing system for factories next-generations.