Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

In-process tool wear prediction system development in end milling operations

Three in-process tool wear monitoring systems have been developed in this research. They are: (1) the multiple linear regression based in-process tool wear prediction (MLR-ITWP) system; (2) the artificial neural networks based in-process tool wear prediction (ANN-ITWP) system; and (3) the statistics assisted fuzzy-nets based in-process tool wear prediction (S-FN-ITWP) system.;Before these above-mentioned systems were developed and evaluated, statistical approaches had been implemented to analyze and identify the most significant force signal for tool wearing monitoring system. This study demonstrates that the average peak cutting forces in the Y direction (the direction that is perpendicular to the table feed) is the most effective cutting force representation for tool wear monitoring.;Following with this discovery, the first system (MLR-ITWP system) was developed using a multiple linear regression model through 100 experimental data sets. Another nine data sets were used to test the system. The average tool wear prediction error of the MLR-ITWP system was +/-0.039 mm through the testing data. The second system (ANN-ITWM system) was developed using back-propagation artificial neural network through the same experimental data and tested with another nine data sets. The average tool wear prediction error of this ANN-ITWM system was +/-0.037 mm. The third system (S-FN-ITWM system) was developed using fuzzy-nets assisted statistically through the same experimental data and tested with another nine data sets. The average tool wear prediction error was +/-0.023 mm.;The scope of this research is to provide systems that can be integrated into smart computer numerical control (CNC) machine development in tool monitoring system. The success of this research provides the researcher better position in further related research

Internet-based solutions to support distributed manufacturing

With the globalisation and constant changes in the marketplace, enterprises are adapting themselves to face new challenges. Therefore, strategic corporate alliances to share knowledge, expertise and resources represent an advantage in an increasing competitive world. This has led the integration of companies, customers, suppliers and partners using networked environments. This thesis presents three novel solutions in the tooling area, developed for Seco tools Ltd, UK. These approaches implement a proposed distributed computing architecture using Internet technologies to assist geographically dispersed tooling engineers in process planning tasks. The systems are summarised as follows. TTS is a Web-based system to support engineers and technical staff in the task of providing technical advice to clients. Seco sales engineers access the system from remote machining sites and submit/retrieve/update the required tooling data located in databases at the company headquarters. The communication platform used for this system provides an effective mechanism to share information nationwide. This system implements efficient methods, such as data relaxation techniques, confidence score and importance levels of attributes, to help the user in finding the closest solutions when specific requirements are not fully matched In the database. Cluster-F has been developed to assist engineers and clients in the assessment of cutting parameters for the tooling process. In this approach the Internet acts as a vehicle to transport the data between users and the database. Cluster-F is a KD approach that makes use of clustering and fuzzy set techniques. The novel proposal In this system is the implementation of fuzzy set concepts to obtain the proximity matrix that will lead the classification of the data. Then hierarchical clustering methods are applied on these data to link the closest objects. A general KD methodology applying rough set concepts Is proposed In this research. This covers aspects of data redundancy, Identification of relevant attributes, detection of data inconsistency, and generation of knowledge rules. R-sets, the third proposed solution, has been developed using this KD methodology. This system evaluates the variables of the tooling database to analyse known and unknown relationships in the data generated after the execution of technical trials. The aim is to discover cause-effect patterns from selected attributes contained In the database. A fourth system was also developed. It is called DBManager and was conceived to administrate the systems users accounts, sales engineers’ accounts and tool trial monitoring process of the data. This supports the implementation of the proposed distributed architecture and the maintenance of the users' accounts for the access restrictions to the system running under this architecture

In-process pokayoke development in multiple automatic manufacturing processes

In this dissertation, three in-process pokayoke systems were developed to prevent defects from occurring, so as to ensure product quality for three automated manufacturing processes.;The first pokayoke development resulted in an in-process, gap-caused flash monitoring (IGFM) system for injection-molding machines. An accelerometer sensor was integrated in the proposed system to detect the difference of the vibration signals between flash and non-flash products. By sub-grouping every two consecutive molded parts with the vibration signal, the online statistical process control (OLSPC) was able to monitor 100% of the molded products. The threshold of this system established by the SPC approach can determine if flash occurred when the machine was in process. The testing results indicated that the accuracy of this IGFM system was 94.7% when flash is caused by a mold-closing gap.;The second pokayoke development led to an in-process surface roughness adaptive control (ISRAC) system for CNC end milling operations. A multiple linear regression algorithm was successfully employed to generate the models for predicting surface roughness and adaptive feed rate change in real time. Not only were the machining parameters included in the ISRAC pokayoke system, but also the cutting force signals collected by a dynamometer sensor. The testing results showed this proposed ISRAC system was able to predict surface roughness in real time with an accuracy of 91.5%, and could successfully implement adaptive control 100% of the time during milling operations.;The third pokayoke development brought an in-process surface roughness adaptive control (ISRAC) system in CNC turning operations. This system employed a back-propagation (BP) neural network algorithm to train the models for in-process surface roughness prediction and adaptive parameter control. In addition to the machining parameters, vibration signals in the Z direction used as an input variable to the neural network system were included for training. The test runs showed this pokayoke system was able to predict surface roughness in real time with an accuracy of 92.5%. The 100% success rate for adaptive control proved that this proposed system could be implemented to adaptively control surface roughness during turning operations

State of AI-based monitoring in smart manufacturing and introduction to focused section

Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area

Tribological Properties of Polymer Composites Using Non Traditional Optimization Technique: a review

Specific wear rate of composite materials plays a significant role in industry. The processes to measure it are both time and cost consuming. It is essential to suggest a modeling method to predict and analyze the effectiveness of parameters of specific wear rate. Nowadays, computational methods such as Grey Relational Analysis (GRA), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as applicable tools from modeling point of view. The objective of using ANN, ANFIS is also to apply this tool for systematic parameter studies in the optimum design of composite materials for specific applications. In the present review, various principles of the neural network approach for predicting certain properties of polymer composite materials are discussed. The aim of this review is to promote more consideration of using GRA, ANN and ANFIS in the field of polymer composite property prediction and design

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154104/1/kampfner1996.pd