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The dynamical evolution of electrical discharge machining (EDM) has drawn immense research interest. Previous research on mechanism analysis has discussed the deterministic nonlinearity of gap states at pulse-on discharging duration, while describing the pulse-off deionization process separately as a stochastic evolutionary process. In this case, the precise model describing a complete machining process, as well as the optimum performance parameters of EDM, can hardly be determined. The main purpose of this paper is to clarify whether the EDM system can maintain consistency in dynamic characteristics within a discharge interval. A nonlinear self-maintained equivalent model is first established, and two threshold conditions are obtained by the Shilnikov theory. The theoretical results prove that the EDM system could lead to chaos without external excitation. The time series of the deionization process recorded in the EDM experiments are then analyzed to further validate this theoretical conclusion. Qualitative chaotic analyses verify that the autonomous EDM process has chaotic characteristics. Quantitative methods are used to estimate the chaotic feature of the autonomous EDM process. By comparing the quantitative results of the autonomous EDM process with the non-autonomous EDM process, a deduction is further made that the EDM system will evolve towards steady chaos under an autonomous state. [ABSTRACT FROM AUTHOR]

Aircraft structural parts are important and high-value parts used to constitute the frame of the aircraft, and are usually produced by NC machining, where the machining parameters are significant for the machining quality, efficiency, and cost. In the process planning, there are hundreds or even thousands of machining operations that require separate machining parameters, which is a huge task for the existing optimization-based methods that rely on iterative optimizations. Due to the complex structures and high requirements, the existing expert system-based methods require plenty of additional modifications. Recently, with the development of artificial intelligence, data-driven methods are used in machining parameter planning, which mines the knowledge and rules hidden in the historical data. However, the existing data-driven models require a large amount of training data and lack interpretability. To address this issue, this paper proposes an informed machine learning method for machining parameter planning, which introduces multiple prior constraints into the data-driven model. First, the part model is represented as an attribute graph, and the cutting area of each machining operation is correlated to a subgraph, which is used to obtain the vectorized representation of machining operation that covers cutting area and process information. Then, by fitting the mapping between the vectorized machining operation and the machining parameters, the knowledge and rules are learned. Next, to introduce prior constraints into the data-driven model, the constraint loss is designed and incorporated into the original loss function. The proposed method can generate machining parameters for all the machining operations in batch, thereby greatly reducing the human interactions. In the case study, the historical processing files of aircraft structural parts are used to train the proposed model for planning cutting width, cutting depth, spindle speed, and machining feedrate. The results show that the demand for training data is reduced and the prediction accuracy is improved with prior constraints. [ABSTRACT FROM AUTHOR]

Small volume and high product mix contribute greatly to the complexity of job shop operations. In addition, shop floor uncertainty or fluctuation is another issue regularly challenging manufacturing companies, including job delay, urgent job insertion, fixture shortage, missing tools, and even machine breakdown. Targeting the uncertainty, we propose a function block-based approach to generating adaptive process plans. Enabled by the function blocks, a so-generated process plan is responsive and tolerant to an unpredictable change. This paper presents in detail how a function block is designed and what it can do during process plan execution. It is expected that this new approach can largely enhance the dynamism of fluctuating job shop operations. [ABSTRACT FROM AUTHOR]

Setup planning of a part for more than one available machine is a typical combinatorial optimisation problem under certain constraints. It has significant impact not only on the whole process planning but also on scheduling, as well as on the integration of process planning and scheduling. Targeting the potential adaptability of process plans associated with setups, a cross-machine setup planning approach using genetic algorithms (GA) for machines with different configurations is presented in this paper. First, based on tool accessibility analysis of different machine configurations, partially sequenced machining features can be grouped into certain setups; then by responding to the requirements from a scheduling system, optimal or near-optimal setup plans are selected for certain criteria, such as cost, makespan and/or machine utilisation. GA is adopted for the combinatorial optimisation, which includes gene pool generation based on tool accessibility examination, setup plan encoding and fitness evaluation, and optimal setup plan selection through GA operations. The proposed approach is implemented in a GA toolbox, and tested using a sample part. The results demonstrate that the proposed approach is applicable to machines with varying configurations, and adaptive to different setup requirements from a scheduling system due to machine availability changes. It is expected that this approach can contribute to process planning and scheduling integration when a process plan is combined with setups for alternative machines during adaptive setup planning. [ABSTRACT FROM AUTHOR]

For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper. [ABSTRACT FROM AUTHOR]

Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm. [ABSTRACT FROM AUTHOR]

Cutting tools, considered as a basic prerequisite machining resource, are generally selected according to the selected machining methods, which cannot fit in the current manufacturing environment where small- and medium-sized enterprises (SMEs) are the major manufacturers. For the survival of SMEs, it is critical to develop methods for selecting proper cutting tools and reducing machining cost according to product data. Therefore, this study proposes an enriched machining feature (MF)-based approach towards adaptive cutting tool and machining method selection, in which both machinability and machining cost of MF are considered. It includes a two-step workflow: filtering and optimisation. In the filtering process, cutting tools are filtered according to workpiece materials, geometries of MFs and cutting tool inventory, respectively. Here, MF geometries depend on Machining Limit Value decided by sizes and interference relationships of MFs. Also, the client is suggested to choose proper new cutting tools. In the optimisation process, the filtered cutting tools are considered for all the MFs, and machining costs are calculated for each option, in order to select the cheapest one. In particular, if similar cutting tools are required for different MFs, the cutting tool selection for these MFs should be performed altogether. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic feature based adaptive process planning approach that can optimise machining cost, machining time and energy consumption simultaneously. The material removal volume of a dynamic feature is refined into non-overlapping volumes removed respectively by a single machining operation in which unified cutting mode is performed. Benefitting from this refinement, energy consumption is estimated analytically based on instantaneous cutting force as a function of real cutting parameters. Moreover, the cutting parameters assigned to each machining operation are optimised effectively in the unified cutting mode. This novel approach enhances the energy efficiency of NC machining through process planning. [ABSTRACT FROM AUTHOR]

Feature-based technologies are widely researched for manufacturing automation. However, in current feature models, features once defined remain constant throughout the whole manufacturing lifecycle. This static feature model is inflexible to support adaptive machining when facing frequent changes to manufacturing resources. This paper presents a new machining feature concept that facilitates responsive changes to the dynamics of machining features in 2.5/3D machining. Basic geometry information for feature construction of complex parts with various intersecting features is represented as a set of meta machining features (MMF). Optimum feature definition is generated adaptively by choosing optimum merging strategies of MMFs according to the capabilities of the selected machine tool, cutter, and cutting parameters. A composite function block for dynamic machining feature modelling is designed with Basic Machining Feature Function Block, Meta Machining Feature Extraction Function Block and Feature Interpreter Function Block. Once changes of the selected machining resources occur, they are informed as input events and machining features are then updated automatically and adaptively based on the event-driven model of function blocks. An example is provided to demonstrate the feasibility and benefits of the developed methodology. [ABSTRACT FROM AUTHOR]

Abstract: Cloud manufacturing as a trend of future manufacturing would provide cost-effective, flexible and scalable solutions to companies by sharing manufacturing resources as services with lower support and maintenance costs. Targeting the Cloud manufacturing, the objective of this research is to develop an Internet- and Web-based service-oriented system for machine availability monitoring and process planning. Particularly, this paper proposes a tiered system architecture and introduces IEC 61499 function blocks for prototype implementation. By connecting to a Wise-ShopFloor framework, it enables real-time machine availability and execution status monitoring during metal-cutting operations, both locally or remotely. The closed-loop information flow makes process planning and monitoring feasible services for the Cloud manufacturing. [Copyright &y& Elsevier]

This paper proposes a spatial three degrees of freedom (DOF) parallel kinematic machine enhanced by a passive leg and a web-based remote control system. First, the geometric model of the parallel kinematic machine is addressed. In the mechanism, a fourth kinematic link-a passive link connecting the base center to the moving platform center-is introduced. Each of the three parallel limbs is actuated by one prismatic joint, respectively. The additional link has three passive DOF, namely two rotations around x and y axes and one translation along z axis. With the existence of this link, the unwanted motion of the tool (located in the moving platform) is constrained. The fourth link also enhances the global stiffness of the structure and distributes the torque from machining. With the kinematic model, a web-based remote control approach is applied. The concept of the web-based remote manipulation approach is introduced and the principles behind the method are explored in detail. Finally, a remote manipulation is demonstrated to the proposed structure using web-based remote control concept. [ABSTRACT FROM AUTHOR]

For the past three decades, computer-aided process planning (CAPP) has attracted a large amount of research interest. A huge volume of literature has been published on this subject. Today, CAPP research faces new challenges owing to the dynamic markets and business globalisation. Thus, there is an urgent need to ascertain the current status and identify future trends of CAPP. Covering articles published on the subjects of CAPP in the past 10 years or so, this article aims to provide an up-to-date review of the CAPP research works, a critical analysis of journals that publish CAPP research works, and an understanding of the future direction in the field. First, general information is provided on CAPP. The past reviews are summarised. Discussions about the recent CAPP research are presented in a number of categories, i.e. feature-based technologies, knowledge-based systems, artificial neural networks, genetic algorithms, fuzzy set theory and fuzzy logic, Petri nets, agent-based technology, Internet-based technology, STEP-compliant CAPP and other emerging technologies. Research on some specific aspects of CAPP is also provided. Discussions and analysis of the methods are then presented based on the data gathered from the Elsevier's Scopus abstract and citation database. The concepts of 'Subject Strength' of a journal and 'technology impact factor' are introduced and used for discussions based on the publication data. The former is used to gauge the level of focus of a journal on a particular research subject/domain, whereas the latter is used to assess the level of impact of a particular technology, in terms of citation counts. Finally, a discussion on the future development is presented. [ABSTRACT FROM AUTHOR]

This paper presents methodologies of web-based decision making for collaborative manufacturing, including web-based knowledge sharing, distributed process planning, dynamic scheduling, real-time monitoring and remote control, targeting distributed yet collaborative manufacturing environments. The web-based decision making is enabled by a framework that allows users to plan and control manufacturing operations based on information either gathered via the Web or collected from manufacturing devices. The objective of this research is to develop an integrated system for web-based collaborative planning and control, supported by real-time monitoring for dynamic scheduling. Details on the principle of the framework, system architecture, and a proof-of-concept prototype are reported in this paper. An example of remote machining is chosen as a case study to demonstrate the effectiveness of this framework toward web-based collaborative manufacturing. [ABSTRACT FROM AUTHOR]

The objective of this research is to develop a framework named Wise-ShopFloor and the enabling technologies for collaborative manufacturing in a decentralized environment. Particularly, this paper presents our latest development on Web-based and sensor-driven remote machining. Once a product design is given, its process plan and NC codes are generated by using a distributed process planning (DPP) system. The NC codes are then used for remote machining via a standard Web browser and a Java GUI interface running inside the browser. In this paper, the focus is given to the concept, architecture and a prototype implementation of the enabling technology. A case study of a test part machining on a 5-axis milling machine is also completed for testing and validation. It is expected that the developed technology can be applied to design verification via remote machining as well as real part production in a distributed manufacturing environment. [ABSTRACT FROM AUTHOR]

Accurate evaluation of the empirical coefficients of a mechanistic cutting force model is critical to the reliability of the predicted cutting forces. This paper presents a simplified and efficient method to determine the cutting force coefficients of a ball-end milling model. The unique feature of this new method is that only a single half-slot cut is to be performed to calibrate the empirical force coefficients that are valid over a wide range of cutting conditions. The instantaneous cutting forces are used with the established helical cutting edge profile on the ball-end mill. The half-slot calibration cut enables successive determination of the lumped discrete values of the varying cutting mechanics parameters along the cutter axis whereas the size effect parameters are determined from the known variation of undeformed chip thickness with cutter rotation. The effectiveness of the present method in determining the cutting force coefficients has been demonstrated experimentally with a series of verification test cuts. [Copyright &y& Elsevier]

Tool condition monitoring (TCM) in machining operations is crucial to maximise the useful tool life while reducing the risks associated with tool breakage. Unlike progressive tool wear, tool breakage occurs randomly, with more severe implications for workpiece quality, machining system stiffness, and even operator safety. Existing literature reviews on TCM focus on tool wear monitoring, including wear state recognition and remaining useful life prediction. However, a comprehensive review of tool breakage monitoring (TBM) techniques is lacking. Generic signal processing and intelligent decision-making methods cannot fully satisfy the practical requirements of the TBM. In addition, developing and evaluating TBM models using imbalanced data is more challenging. Herein, we present the first systematic review on TBM to bridge these limitations, and provide adequate guidance for avoiding catastrophic tool failures during cutting processes. Signal acquisition, feature extraction, and decision-making methodologies for the TBM are outlined and compared with related techniques for tool wear monitoring. The effects of data imbalance on TBM models are considered, and feasible solutions are provided at the data and algorithm levels. Finally, the challenges faced by the TBM are discussed, and potential research directions are suggested. The research and application of TBM techniques will certainly better empower various machining operations in response to intelligent manufacturing demands. [Display omitted] • Tool breakage monitoring techniques in machining operations are systematically reviewed. • The difference between tool wear and breakage monitoring are compared. • TBM data imbalance problems are discussed at the data and algorithm levels. • The performance metrics commonly used to evaluate TBM models are highlighted. • The specific research route of tool life cycle management is proposed. [ABSTRACT FROM AUTHOR]