Design methodology for smart actuator services for machine tool and machining control and monitoring

This paper presents a methodology to design the services of smart actuators for machine tools. The smart actuators aim at replacing the traditional drives (spindles and feed-drives) and enable to add data processing abilities to implement monitoring and control tasks. Their data processing abilities are also exploited in order to create a new decision level at the machine level. The aim of this decision level is to react to disturbances that the monitoring tasks detect. The cooperation between the computational objects (the smart spindle, the smart feed-drives and the CNC unit) enables to carry out functions for accommodating or adapting to the disturbances. This leads to the extension of the notion of smart actuator with the notion of agent. In order to implement the services of the smart drives, a general design is presented describing the services as well as the behavior of the smart drive according to the object oriented approach. Requirements about the CNC unit are detailed. Eventually, an implementation of the smart drive services that involves a virtual lathe and a virtual turning operation is described. This description is part of the design methodology. Experimental results obtained thanks to the virtual machine are then presented

A fault diagnosis system for CNC hydraulic machines: a conceptual framework

The fault diagnosis process in Computer Numerical Control (CNC) hydraulic machines for steel processing relies on skills, experiences, and maintenance technicians' understanding of the machine. The problem is many junior maintenance technicians are inexperienced and unskilled. This paper proposes a conceptual framework for a fault diagnosis system for the CNC hydraulic machine to help a maintenance technician in a fault diagnosis process. The framework uses association rule mining to discover hidden association patterns between fault symptoms and causes from historical machine fault data. The framework has consisted of data standardization, knowledge acquisition, and a model of the fault diagnosis system. The data standardization aims to make the data ready to be mined by assigning a fault tag for each record of historical fault data. The tagged repair records are used to produce symptoms–cause associative knowledge. The produced knowledge is refined by corrective actions acquired from expert knowledge. The knowledge is then stored in the fault knowledge database in the form of IF-THEN rules. The reasoning machine is developed to map the fault symptoms as IF and the causes as THEN. Production operators can fill in the fault symptoms by choosing the standardized fault symptom tag. When a maintenance technician reviews a fault report, the system, through a reasoning machine, will access the appropriate IF-THEN rules based on the fault symptoms that the production operator has filled in. The system concludes the fault cause and recommends suitable corrective action

A fault diagnosis system for CNC hydraulic machines: a conceptual framework

The fault diagnosis process in Computer Numerical Control (CNC) hydraulic machines for steel processing relies on skills, experiences, and maintenance technicians' understanding of the machine. The problem is many junior maintenance technicians are inexperienced and unskilled. This paper proposes a conceptual framework for a fault diagnosis system for the CNC hydraulic machine to help a maintenance technician in a fault diagnosis process. The framework uses association rule mining to discover hidden association patterns between fault symptoms and causes from historical machine fault data. The framework has consisted of data standardization, knowledge acquisition, and a model of the fault diagnosis system. The data standardization aims to make the data ready to be mined by assigning a fault tag for each record of historical fault data. The tagged repair records are used to produce symptoms–cause associative knowledge. The produced knowledge is refined by corrective actions acquired from expert knowledge. The knowledge is then stored in the fault knowledge database in the form of IF-THEN rules. The reasoning machine is developed to map the fault symptoms as IF and the causes as THEN. Production operators can fill in the fault symptoms by choosing the standardized fault symptom tag. When a maintenance technician reviews a fault report, the system, through a reasoning machine, will access the appropriate IF-THEN rules based on the fault symptoms that the production operator has filled in. The system concludes the fault cause and recommends suitable corrective action

Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System

This paper presents the design, development and SIMULINK implementation of the lumped parameter model of C-axis drive from GEISS five-axis CNC machine tool. The simulated results compare well with the experimental data measured from the actual machine. Also the paper describes the steps for data acquisition using ControlDesk and hardware-in-the-loop implementation of the drive models in dSPACE real-time system. The main components of the HIL system are: the drive model simulation and input – output (I/O) modules for receiving the real controller outputs. The paper explains how the experimental data obtained from the data acquisition process using dSPACE real-time system can be used for the development of machine tool diagnosis and prognosis systems that facilitate the improvement of maintenance activities

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Predictive Maintenance on the Machining Process and Machine Tool

This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces

Multiple Knowledge Acquisition Strategies in MOLTKE

In this paper we will present a design model (in the sense of KADS) for the domain of technical diagnosis. Based on this we will describe the fully implemented expert system shell MOLTKE 3.0, which integrates common knowledge acquisition methods with techniques developed in the fields of Model-Based Diagnosis and Machine Learning, especially Case-Based Reasoning

A Modeling and Analysis Framework To Support Monitoring, Assessment, and Control of Manufacturing Systems Using Hybrid Models

The manufacturing industry has constantly been challenged to improve productivity, adapt to continuous changes in demand, and reduce cost. The need for a competitive advantage has motivated research for new modeling and control strategies able to support reconfiguration considering the coupling between different aspects of plant floor operations. However, models of manufacturing systems usually capture the process flow and machine capabilities while neglecting the machine dynamics. The disjoint analysis of system-level interactions and machine-level dynamics limits the effectiveness of performance assessment and control strategies. This dissertation addresses the enhancement of productivity and adaptability of manufacturing systems by monitoring and controlling both the behavior of independent machines and their interactions. A novel control framework is introduced to support performance monitoring and decision making using real-time simulation, anomaly detection, and multi-objective optimization. The intellectual merit of this dissertation lies in (1) the development a mathematical framework to create hybrid models of both machines and systems capable of running in real-time, (2) the algorithms to improve anomaly detection and diagnosis using context-sensitive adaptive threshold limits combined with context-specific classification models, and (3) the construction of a simulation-based optimization strategy to support decision making considering the inherent trade-offs between productivity, quality, reliability, and energy usage. The result is a framework that transforms the state-of-the-art of manufacturing by enabling real-time performance monitoring, assessment, and control of plant floor operations. The control strategy aims to improve the productivity and sustainability of manufacturing systems using multi-objective optimization. The outcomes of this dissertation were implemented in an experimental testbed. Results demonstrate the potential to support maintenance actions, productivity analysis, and decision making in manufacturing systems. Furthermore, the proposed framework lays the foundation for a seamless integration of real systems and virtual models. The broader impact of this dissertation is the advancement of manufacturing science that is crucial to support economic growth. The implementation of the framework proposed in this dissertation can result in higher productivity, lower downtime, and energy savings. Although the project focuses on discrete manufacturing with a flow shop configuration, the control framework, modeling strategy, and optimization approach can be translated to job shop configurations or batch processes. Moreover, the algorithms and infrastructure implemented in the testbed at the University of Michigan can be integrated into automation and control products for wide availability.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147657/1/migsae_1.pd