Quality and inspection of machining operations: Review of condition monitoring and CMM inspection techniques 2000 to present

In order to consistently produce quality parts, many aspects of the manufacturing process must be carefully monitored, controlled, and measured. The methods and techniques by which to accomplish these tasks has been the focus of numerous studies in recent years. With the rapid advances in computing technology, the complexity and overhead that can be feasibly incorporated in any developed technique has dramatically improved. Thus, techniques that would have been impractical for implementation just a few years ago can now be realistically applied. This rapid growth has resulted in a wealth of new capabilities for improving part and process quality and reliability. In this paper, overviews of recent advances that apply to machining are presented. Moreover, due to the relative significance of two particular machining aspects, this review focuses specifically on research publications pertaining to using tool condition monitoring and coordinate measurement machines to improve the machining process. Tool condition has a direct effect on part quality and is discussed first. The application of tool condition monitoring as it applies to turning, drilling, milling, and grinding is presented. The subsequent section provides recommendations for future research opportunities. The ensuing section focuses on the use of coordinate measuring machines in conjunction with machining and is subdivided with respect to integration with machining tools, inspection planning and efficiency, advanced controller feedback, machine error compensation, and on-line tool calibration, in that specific order and concludes with recommendations regarding where future needs remain

One MES model in Digital Manufacturing

Digital manufacturing is base for Industry 4.0, based on advanced digital-oriented technologies, smart products (advanced production mode and new characteristics), and smart supply - chain (procurement of raw materials and delivery of finished products). Bidirectional exchange of information in collaborative manufacturing, using it exchange also for digital platforms of design of the innovative products. In this paper we are show developed model of Serbian digital factory with selected examples for the MES area

A Dual-Mode Model Predictive Control Algorithm Trajectory Tracking in Discrete-Time Nonlinear Dynamic Systems

In this paper, a dual-mode model predictive/linear control method is presented, which extends the concept of dual-mode model predictive control (MPC) to trajectory tracking control of nonlinear dynamic systems described by discrete-time state-space models. The dual-mode controller comprises of a time-varying linear control law, implemented when the states lie within a sufficiently small neighborhood of the reference trajectory, and a model predictive control strategy driving the system toward that neighborhood. The boundary of this neighborhood is characterized so as to ensure stability of the closed-loop system and terminate the optimization procedure in a finite number of iterations, without jeopardizing the stability of the closed-loop system. The developed controller is applied to the central air handling unit (AHU) of a two-zone variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) system

Feature signature prediction of a boring process using neural network modeling with confidence bounds

Prediction of machine tool failure has been very important in modern metal cutting operations in order to meet the growing demand for product quality and cost reduction. This paper presents the study of building a neural network model for predicting the behavior of a boring process during its full life cycle. This prediction is achieved by the fusion of the predictions of three principal components extracted as features from the joint time–frequency distributions of energy of the spindle loads observed during the boring process. Furthermore, prediction uncertainty is assessed using nonlinear regression in order to quantify the errors associated with the prediction. The results show that the implemented Elman recurrent neural network is a viable method for the prediction of the feature behavior of the boring process, and that the constructed confidence bounds provide information crucial for subsequent maintenance decision making based on the predicted cutting tool degradation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45845/1/170_2005_Article_114.pd

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment

Model-predictive control and closed-loop stability considerations for nonlinear plants described by local ARX-type models

In this paper, a model-predictive control (MPC) method is detailed for the control of nonlinear systems with stability considerations. It will be assumed that the plant is described by a local input/output ARX-type model, with the control potentially included in the premise variables, which enables the control of systems that are nonlinear in both the state and control input. Additionally, for the case of set point regulation, a suboptimal controller is derived which has the dual purpose of ensuring stability and enabling finite-iteration termination of the iterative procedure used to solve the nonlinear optimization problem that is used to determine the control signal

Stream of variation (SOV) modeling of machining errors and its applications.

The research presented in this dissertation offers a new linear state space model of dimensional machining errors, where the role of the time index is played by the station index in the multi-station machining system. Following previously published works, the model is referred to as the Stream of Variation (SOV) model of dimensional machining errors. Due to its specific form, this analytical model can serve as an interface in employing the rich knowledge of control theory and multi-variate statistics in systematically solving problems in machining that are currently solved using substantial expert intervention. Several such problems are root cause identification, machining error compensation and measurement scheme analysis and synthesis. In this dissertation, the linear state space SOV model of dimensional machining errors was employed to quantitatively assess the amount of information measurement schemes in multi-station machining systems carry about the root causes of dimensional errors. Both the Bayesian and non-Bayesian statistical approaches to the problem of measurement scheme analysis are presented and discussed. Thus, measurement scheme analysis methods have been developed to exploit the a priori statistical knowledge about the machining system, should such knowledge exist, as well as to handle situation when the a priori statistical knowledge about the machining system is non-existent, or uncertain. The measurement scheme analysis methods introduced in this dissertation are used to devise formal and systematic procedures for synthesizing measurement schemes that under a given cost limit carry the most information about the root causes of dimensional machining errors. Measurement scheme synthesis procedures based on the successive removal of measurements and based on the genetic algorithm (GA) have been developed. It was observed that the GA based measurement synthesis procedure robustly outperformed those based on the successive removal of measurements by consistently offering more informative measurement schemes. Efficacy and applicability of the newly proposed SOV model, as well as its applications in the measurement scheme analysis and synthesis were illustrated and verified in machining of an automotive cylinder head in the machining testbed of the Engineering Research Center for Reconfigurable Machining Systems at the University of Michigan.Ph.D.Applied SciencesMechanical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/131744/2/3057938.pd

Recognition of patterns in reduced interference time-frequency distributions of the temporomandibular joint sounds

Disorders of the temporomandibular joint (TMJ) are a source of pain and discomfort for many people. Sounds evoked in the TMJ during jaw movements may indicate pathology. Auscultatory findings and qualitative verbal descriptions of TMJ sounds are inherently subjective and difficult to document for accurate comparison of diagnoses among different clinicians, or within the same patient over time. Time-frequency analysis of electronically recorded TMJ sounds makes possible a more objective and sophisticated analysis.Master of Engineering (MPE

Monitoring of complex systems of interacting dynamic systems

Increases in functionality, power and intelligence of modern engineered systems led to complex systems with a large number of interconnected dynamic subsystems. In such machines, faults in one subsystem can cascade and affect the behavior of numerous other subsystems. This complicates the traditional fault monitoring procedures because of the need to train models of the faults that the monitoring system needs to detect and recognize. Unavoidable design defects, quality variations and different usage patterns make it infeasible to foresee all possible faults, resulting in limited diagnostic coverage that can only deal with previously anticipated and modeled failures. This leads to missed detections and costly blind swapping of acceptable components because of one’s inability to accurately isolate the source of previously unseen anomalies. To circumvent these difficulties, a new paradigm for diagnostic systems is proposed and discussed in this paper. Its feasibility is demonstrated through application examples in automotive engine diagnostics

Stream of Variation Based Error Compensation Strategy

ABSTRACT Linear state space Stream of Variation (SoV) models of error flow in multistation assembly and machining systems have been well studied in the past decade. SoV models were utilized for identification of process-level root causes of manufacturing errors, quantitative characterization of measurements in multistation manufacturing systems, systematic selection of measurement points and features, as well as tolerance allocation and process design. Nevertheless, natural connection of the linear state space form of SoV models with traditional control theory has not been utilized to automatically compensate observed manufacturing errors and thus close the quality control loop. Recent advances in measurement technology and flexible fixtures make such operations possible and in this paper, we propose a method for strategic elimination of root causes of quality problems based on the SoV models of the flow of manufacturing errors. Furthermore, the concept of compensability that quantitatively depicts the capacity of error compensation in a specific system is proposed. Based on this concept analogous to the controllability in the traditional control theory, compensable and non-compensable subspaces of dimensional errors are identified and quantitatively described. Theoretical results have been demonstrated using the SoV model of a real industrial process used for machining of automotive cylinder heads