Machining Process Monitoring Research Articles

Industrial Applications of Online Machining Process Monitoring System

An online machining process monitoring system has been constructed by taking advantage of the new achievements in data acquisition, sensor technology, and signal processing. Firstly, the architecture of monitoring system with the capability of automatic online acquisition, presentation, and analysis sensory signals is designed. Secondly, wavelet transform is further explored to decompose sensory signals into static and dynamic components for the purpose of extracting distinctive features associated with different tool malfunctions. Thirdly, by conjunction with the wavelet transform, univariate and multivariate statistical process monitoring techniques are proposed to construct the thresholds of malfunction-free machining zone. Short time Fourier transform is further introduced to detect the onset of chattering in machining processes. Finally, the effectiveness of the developed techniques and monitoring system has been demonstrated by experimental results obtained from the extensive industrial machining trials.

Design and implementation of an intelligent grinding assistant system

In modern competitive manufacturing industry, machining processes are expected to deliver products with high accuracy and assured surface integrity, using shorter cycle times with reduced operator intervention and increased flexibility. To meet such demands, the trend towards increased use of machine intelligence in machining systems and operations is clear and unlikely to be revised. This paper describes the structure, content and relations employed in a fully integrated intelligent grinding system for adaptive controlled cycle optimisation, thermal damage avoidance, dressing interval optimisation and data retention. This system, termed: 'Intelligent Grinding Assistant' (IGA) is a unique, robust and versatile software system with embedded links and protocols for communication with the Computer Numerical Control (CNC) and instrumentation of a grinding machine. The IGA was evaluated and implemented on a commercially available production machine and had its international launch in September 2005. The CNC, machine tool instrumentation, process monitoring and data analysis systems, Profibus link, and control strategies all constituted the IGA system. Grinding performance was monitored and assessed in real time. An intelligent database was also developed to support the IGA in the provision of optimised and/or safe starting cycle data to the operator/adaptive system and in the selective retention of data. Importantly, the IGA has been designed and implemented in a way to ensure that an operator can readily use the system and achieve process improvement without recourse to extensive training.

Acoustic sensor for monitoring machining processes in machining tools

An acoustic sensor for monitoring machining processes in machining tools, e.g. grinding machines, includes a stationary unit, or stator (21) and a rotating unit, or rotor (23), the latter being coupled, for instance, to the grinding wheel and rotating with it. The rotor includes a support (45;45') and a piezoelectric transducer (33). The position of the transducer is defined and locked without using glues by means of mechanical elements (48, 53, 54; 63, 64; 74, 78) that may include elastic elements such as springs. The rotor also includes a printed circuit board (55), connected to the piezoelectric transducer, with a charge amplifier (37) for amplifying the output signal of the piezoelectric transducer. The rotor is electrically coupled to the stator by means of transformer type couplings (30, 31).

Monitoring of diamond disk wear in stone cutting by means of force or acceleration sensors

The application of sensor systems is becoming more commonplace in improving productivity, automation, and reliability. The sensors employed in such systems possess signal and information ability for enhancing the monitoring and control of machining processes. Although measuring force and acceleration signals have been commonly used for the monitoring of metal machining processes, their application to stone cutting has not been well developed, which is perhaps due to the complexity of the interaction between the stone and the diamond disk. In order to enhance knowledge in this area of applications, a multi-sensor system was developed and installed for the monitoring of stone cutting by diamond mill. The signals acquired and analysed by the system include force and acceleration under different machining conditions. The measured signal data was used to perform time-domain analysis. The results indicate the feasibility of using the RMS features of force or acceleration signals along z-axis for the monitoring of disk wear.

Development of an online machining process monitoring system: Application in hard turning

A PXI-based online machining process monitoring system has been developed in LabVIEW environment. The developed monitoring system is capable to acquire, present and analyse sensory signals automatically by using advanced queue and triggering technique. The wavelet transform is explored to decompose sensory signals into static and dynamic components and extract features of tool malfunctions in machining processes. Statistical process monitoring technique is further proposed to construct thresholds of free-malfunction machining zone. Experimental results show that the developed online process monitoring system is effective to detect tool malfunctions and construct thresholds for free-malfunction machining zone. Finally, a critical analysis in terms of sensitivity and repeatability of alternative sensors versus a dedicated platform dynamometer has been carried out. The possibility to employ alternative sensing techniques in industrial environment has been investigated by turning experimental trials.

A Web-based machining process monitoring system for E-manufacturing implementation

Recently, with the rapid growth of information technology, many studies have been performed to implement Web-based manufacturing system. Such technologies are expected to meet the need of many manufacturing industries who want to adopt E-manufacturing system for the construction of globalization, agility, and digitalization to cope with the rapid changing market requirements. In this research, a real-time Web-based machine tool and machining process monitoring system is developed as the first step for implementing E-manufacturing system. In this system, the current variations of the main spindle and feeding motors are measured using hall sensors. And the relationship between the cutting force and the spindle motor RMS (Root Mean Square) current at various spindle rotational speeds is obtained. Thermocouples are used to measure temperature variations of important heat sources of a machine tool. Also, a rule-based expert system is applied in order to decide the machining process and machine tool are in normal conditions. Finally, the effectiveness of the developed system is verified through a series of experiments.

Embedding machining features in function blocks for distributed process planning

IEC 61499 function blocks, being emerging industrial process measurement and control standards, are chosen as data processing and execution control elements within the context of a distributed process planning (DPP) system. A two-layer structure, consisting of supervisory planning (SP) and operation planning (OP), is identified to separate the generic process data from those machine-specific ones. Within DPP, function blocks are used for data encapsulation, machining parameter optimization, process monitoring, scheduling integration, and CNC control. From SP to OP, a function block evolves from meta function block, through object function block, to execution function block. The detailed design of a meta function block is depicted in this paper. A function block designer has been developed to help process planners define basic function block types and generate composite function block networks. It is expected that the function block-enabled DPP approach and the associated algorithms can largely improve the dynamism of process plans as well as the flexibility and productivity of job shop operations in a changing environment.

Development of an online machining process monitoring system: a case study of the broaching process

This paper presents a new online machining process monitoring system based on the PXI hardware platform and the LabVIEW software platform. The whole system is composed of the following interconnected packages: sensing, triggering, data acquisition, characterisation, condition monitoring and feature extraction packages. Several signal processing methods, namely, cross-correlation, resample, short-time Fourier transform (STFT) and statistical process control, are developed to extract the features of tool malfunctions and construct the thresholds of malfunction-free zones. Experimental results show that the developed online process monitoring system is efficient for acquiring, analysing and presenting sensory signals simultaneously, while the developed signal processing techniques are effective for detecting tool wear and constructing thresholds for tool-malfunction-free zones. Additionally, a sensitivity analysis of the signals acquired from alternative sensors versus those collected from a dedicated platform dynamometer has been carried out. This enables the evaluation of the possibility to employ alternative sensing techniques in an industrial environment.

Multi-Scale Statistical Process Monitoring in Machining

Most practical industrial process data contain contributions at multiple scales in time and frequency. Unfortunately, conventional statistical process control approaches often detect events at only one scale. This paper addresses a new method, called multiscale statistical process monitoring, for tool condition monitoring in a machining process, which integrates discrete wavelet transform (WT) and statistical process control. Firstly, discrete WT is applied to decompose the collected data from the manufacturing system into uncorrelated components. Next, the detection limits are formed for each decomposed component by using Shewhart control charts. A case study, i.e., tool condition monitoring in turning using an acoustic emission signal, demonstrates that the new method is able to detect abnormal events (serious tool wear or breakage) in the machining process.

Research on Spindle and Machining Process Monitoring for Intelligent Machine Tools(Advanced machine tool)

Research on Spindle and Machining Process Monitoring for Intelligent Machine Tools(Advanced machine tool)

Networked sensing for high-speed machining processes based on CORBA

The goal of this paper is to show preliminary results concerning the development of an open software platform supported by portable, low-cost technologies accepted worldwide (i.e., real-time CORBA), aiming at networked monitoring of a complex electromechanical process. A high-speed machining process was selected as a case study. Preliminary results in the networked real-time monitoring of a high-speed machining process were encouraging in spite of constraints dictated by computerized numerical control. The results also corroborated the viability of the platform for monitoring on the basis of current communications and computation technologies using open architectures.

A Bayesian network approach to root cause diagnosis of process variations

This paper addresses the challenges inherent in root cause diagnosis of process variations in a production machining environment. We develop and present a process monitoring and diagnosis approach based on a Bayesian belief network for incorporating multiple process metrics from multiple sensor sources in sequential machining operations to identify the root cause of process variations and provide a probabilistic confidence level of the diagnosis. The vast majority of previous work in machining process monitoring has focused on single-operation tool wear monitoring. The focus of the present work is to develop a methodology for diagnosing the root cause of process variations that are often confounded in process monitoring systems, namely workpiece hardness, stock size, and tool wear variations. To achieve this goal, multiple sensor metrics have been identified with statistical correlations to the process faults of interest. Data from multiple sensors on sequential machining operations are then combined through a causal belief network framework to provide a probabilistic diagnosis of the root cause of the process variation.

An investigation of tool-wear monitoring in a high-speed machining process

The monitoring of high-speed machining processes is a key issue for ensuring better use of new machine-tool capabilities. An investigation of tool-wear monitoring in a high-speed machining process on the basis of the analysis of different signals’ signatures in the time and frequency domains is presented in this paper. Sensorial information from relevant sensors (i.e., dynamometer, accelerometer and acoustic-emission (AE) sensor) is compared and analyzed, assessing the deviation in representative variables in the time and frequency domains. The time- and frequency-domain analysis confirms the relevance of cutting-force and vibration signals’ signatures for tool-wear monitoring in high-speed machining (HSM) processes. Likewise, the spectrum analysis of AE signals corroborates that AE sensors are very sensitive to changes of tool condition, with increasing amplitudes of up to 160 kHz for worn tools.

Development of in situ system to monitor the machining process using a piezo load cell

The measurement of cutting force is one of the most frequently used techniques for monitoring machining processes. Its widespread application ranges from tool condition identification, feedback control and cutting system design to process optimization.

Machining Process Monitoring and Control: The State-of-the-Art

Research in automating the process level of machining operations has been conducted, in both academia and industry, over the past few decades. This work is motivated by a strong belief that research in this area will provide increased productivity, improved part quality, reduced costs, and relaxed machine design constraints. The basis for this belief is two-fold. First, machining process automation can be applied to both large batch production environments and small batch jobs. Second, process automation can autonomously tune machine parameters (feed, speed, depth of cut, etc.) on-line and off-line to substantially increase the machine tool’s performance in terms of part tolerances and surface finish, operation cycle time, etc. Process automation holds the promise of bridging the gap between product design and process planning, while reaching beyond the capability of a human operator. The success of manufacturing process automation hinges primarily on the effectiveness of the process monitoring and control systems. This paper discusses the evolution of machining process monitoring and control technologies and conducts an in-depth review of the state-of-the-art of these technologies over the past decade. The research in each area is highlighted with experimental and simulation examples. Open architecture software platforms that provide the means to implement process monitoring and control systems are also reviewed. The impact, industrial realization, and future trends of machining process monitoring and control technologies are also discussed.

Study of machining process monitoring of FMS based on time series analysis

Study of machining process monitoring of FMS based on time series analysis

Monitoring of Ultraprecision Machining Processes

New demands are being placed on monitoring systems in the manufacturing environment because of recent developments in machining technology and machine tool design. In-process sensors are used to generate control signals to improve both the control and productivity of manufacturing systems. Numerous different sensors are available for monitoring and controlling the machining environment including force, power, and acoustic emission (AE) sensors. This paper first discusses the requirements for sensor technology for precision manufacturing process monitoring in general. Details are also given about AE and its application for process characterisation and monitoring in ultraprecision machining.

A brief review: acoustic emission method for tool wear monitoring during turning

Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process monitoring. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing processes. This paper reviews briefly the research on AE sensing of tool wear condition in turning. The main contents included are: 1. The AE generation in metal cutting processes, AE signal classification, and AE signal correction. 2. AE signal processing with various methodologies, including time series analysis, FFT, wavelet transform, etc. 3. Estimation of tool wear condition, including pattern classification, GMDH methodology, fuzzy classifier, neural network, and sensor and data fusion. A review of AE-based tool wear monitoring in turning is an important step for improving and developing new tool wear monitoring methodology.

Open system architecture modular tool kit for motion and machining process control

A user-friendly reconfigurable and modular monitoring and control system for computerized numerical control (CNC) machine tool control and machining process monitoring is described. The main part of the system is a fully integrated combination of a real-time preemptive operating system (ORTS-DSP) for machine-level soft real-time tasks running on digital signal processor (DSP) boards and an enhanced Windows-NT-based environment (ORTS-PC/NT), running on a PC, for applications such as high-level operator-machine interfaces and high-level communications with the ORTS-DSP tasks on the DSP boards. A transparent communication protocol allows connection between software modules on the same or different DSP boards and the host Windows NT computers. The communication connections can be set up at runtime via a scripting language. ORTS allows modular integration of new functions developed in the C language, and can be reconfigured to control robots, machine tools, or other processes, using simple script commands. The system can be used as an architecture modular operating system for the progressive development of real-time signal processing, motion, and process control applications. Sample applications for machine tool control and sensor-assisted machining applications are presented. An adjunct component of the above system is a module called the intelligent machining module (IMM) which is intended to provide an easy-to-use subsystem for sensor-assisted machining. IMM is a fast, synchronous, runtime configurable signal processing network that can be reconfigured by means of plug-in software modules (PIMs) to form a sensor-based control or signal processing system for applications that can take advantage of synchronous processing. Examples of existing PIMs are peak and average detectors, fast Fourier transforms, and control algorithms. IMM can be used as an integral part of the above system, or as a stand-alone subsystem, or integrated with semi-open commercial CNC machines.

Analysis of Acoustic Emission Signals in Machining

Acoustic emission (AE) signals are emerging as promising means for monitoring machining processes, but understanding their generation is presently a topic of active research; hence techniques to analyze them are not completely developed. In this paper, we present a novel methodology based on chaos theory, wavelets and neural networks, for analyzing AE signals. Our methodology involves a thorough signal characterization, followed by signal representation using wavelet packets, and state estimation using multilayer neural networks. Our methodology has yielded a compact signal representation, facilitating the extraction of a tight set of features for flank wear estimation.