Automated tool sequence selection for 3-axis machining of free-form pockets

This paper describes an efficient method to find the lowest cost tool sequence for rough machining free-form pockets on a 3-axis milling machine. The free-form pocket is approximated to within a predefined tolerance of the desired surface using series of 2.5-D layers of varying thicknesses that can be efficiently removed with flat-end milling cutters. A graph-based method finds an optimal sequence of tools for rough machining the approximated pocket. The algorithm used here can be tuned to suit any available tool set and preferred cost models. The tool sequence that is obtained is near optimal, and may take into account tool wear, as well as various overhead costs of the machine shop.     

Virtual machining considering dimensional,geometrical and tool deflection errors in three-axis CNC milling machines

Virtual manufacturing systems can provide useful means for products to be manufactured without the need of physical testing on the shop floor. As a result, the time and cost of part production can be decreased. There are different error sources in machine tools such as tool deflection, geometrical deviations of moving axis and thermal distortions of machine tool structures. Some of these errors can be decreased by controlling the machining process and environmental parameters. However other errors like tool deflection and geometrical errors which have a big portion of the total error, need more attention. This paper presents a virtual machining system in order to enforce dimensional, geometrical and tool deflection errors in three-axis milling operations. The system receives 21 dimensional and geometrical errors of a machine tool and machining codes of a specific part as input. The output of the system is the modified codes which will produce actual machined part in the virtual environment.     

ISO 14649-based nonlinear process planning implementation for complex machining

Increasing attention is being paid to complete machining, i.e., machining of the whole part in a single machine tool, in the metal working industry. For this purpose, complex machine tools equipped with machining components, such as multiple spindles and turrets have been developed by leading machine tool builders. The efficiency of complex machine tools is largely dependent on how the machining components are utilized. The main thrust of this paper is twofold: (1) Proposition of a nonlinear process planning based on the STEP-NC (STEP-compliant data interface for numerical controls) paradigm whose data model is formalized as ISO 14649, and (2) Development of an optimal solution algorithm for process planning for complex machining. The developed algorithm is based on the branch-and-bound approach and heuristics derived from engineering insights. The developed process planning method and optimization algorithm were implemented and tested via the TurnSTEP system developed by our research team. Through the experiments, we are convinced that the new process planning and algorithm can be used as a fundamental means for implementing the third type of STEP-NC [Suh S. TurnSTEP: Tools to create CNC turning programs. In: White paper presented on STEP Implementers’ Forum ISO TC184/SC4 Meeting. 2004], i.e., an Intelligent and Autonomous STEP-NC system for the CAD-CAM-CNC chain supporting e-Manufacturing.     

数控回转工作台的几何误差测量和建模

作为五轴数控机床的关键零部件,数控回转工作台能够实现C轴和A轴的回转进给与定位,其精度在很大程度上决定了数控机床的加工精度。以此为研究背景,建立了回转工作台的空间误差模型,对回转工作台的几何误差进行测量,并建立了误差补偿的数学模型,为最终进行机床的误差补偿提供依据,从而实现数控加工中心加工精度的提高。     

An exact solution to the TLP problem in an NC machine

This paper considers a job sequencing problem for a single numerical controlled machining center. It is assumed that all the considered jobs must be processed on a single machine provided with a tool magazine with C positions, that no job requires more than C tools to be completely machined and that the tools may be loaded and unloaded from the tool magazine only when the machining operations for each job are completed. The decisional problem is referred to as the tool loading problem (TLP) and it determines the jobs machining sequence as well as the tools to load in the machine tool magazine before the machining operations on each job may start. In industrial cases where the tool switching time is both significant relative to job processing time and proportional to the number of tool switches, the performance criterion is the minimization of the number of tool switches. This paper demonstrates that the TLP is a symmetric sequencing problem. The authors enrich a branch-and-bound algorithm proposed in literature for the TLP with the new symmetric formulation. Computational experiments show the significant improvement obtained by the novel symmetric formulation of the TLP.     

基于机床超低待机状态的流水车间能耗调度

为降低流水车间能源消耗,引入一种数控机床的超低待机状态,相比于将数控机床待机状态切换为停机状态的节能研究,可在不停机情况下降低数控机床加工间隔状态的功率,避免数控机床频繁启停.针对流水车间加工状态、待机状态及超低待机状态三元调度问题,提出基于工序平移的混合遗传算法,分别定义了不同的工序邻域移动操作,实现数控机床待机状态向超低待机状态和停机状态的转化,形成主动节能调度策略,提升遗传算法求解考虑超低待机状态的流水车间调度问题的优化能力.实验研究表明,启用超低待机状态能够降低流水车间10%以上的能耗,且基于工序平移的混合遗传算法求解考虑超低待机状态的流水车间调度问题性能优于遗传算法.     

A performance evaluation methodology for robotic machine tools used in large volume manufacturing

The manufacture of large components in various industries can create health, safety and economic challenges as a result of machining operations. A potential solution is the replacement of conventional large machine tools with low-cost and portable robotic machine tools, although these lack accuracy and precision in comparison. Effort is therefore required to develop robotic machining technology to a state where it can be implemented in high tolerance applications using a variety of materials. A barrier to implementation is that there is not a standardised procedure available to robustly assess current robotic machining performance, which makes it challenging to assess the impact of technological developments. This paper develops a methodology to determine robotic machining performance based upon reviews of standards available that currently specify such guidelines for robotics and machine tool technologies used independently. It is found that useful elements from each theme can be combined and applied to robotic machine tool performance evaluation. These are presented here with the aim of forming a conceptual foundation on which the technology can be developed for large volume manufacturing.     

In-process regressions and adaptive multicriteria neural networks for monitoring and supervising machining operations

The authors develop a monitoring and supervising system for machining operations using in-process regressions (for monitoring) and adaptive feedforward artificial neural networks (for supervising). The system is designed for: (1) in-process tool life measurement and prediction; (2) supervision of machining operations in terms of the best machining setup; and (3) catastrophic tool failure monitoring. The monitoring system predicts tool life by using different sensors for gathering information based on a regression model that allows for the variations between tools and different machine setups. The regression model makes its prediction by using the history of other tools and combining it with the information obtained about the tool under consideration. The supervision system identifies the best parameters for the machine setup problem within the framework of multiple criteria decision making. The decision maker (operator) considers several criteria, such as cutting quality, production rate and tool life. To make the optimal decision with several criteria, an adaptive feedforward artificial neural network is used to assess the decision maker's preferences. The authors' neural network approach learns from the decision maker's complex behavior and hence, in automatic mode, can make decisions for the decision maker. The approach is not computationally demanding, and experiments demonstrate that its predictions are accurate.     

Optimization of multi-task turning operations under minimal tool waste consideration

Most of the literatures on machining economics problems tend to focus on single cutting operations. However, in reality most parts that need to be machined require more than one operation. In addition, machining technology has been developed to the point that a single computer numerical control (CNC) machine is capable of performing multiple operations, even simultaneously, employing multiple spindles and cutting tools. When several operations are performed on a CNC turning machine, various tools are required for the cutting operations. Determining the life of these cutting tools under different machining conditions is an arduous task for the operators. They usually replace the tools based on their experience or according to the specific cutting tool handbook. Frequent tool replacements may result in wasted tools and tool utilization, while infrequent tool replacements may result in poorly machined parts. In this study we propose a mathematical model in which several different turning operations (turning, drilling, and parting) with proper constraints are performed. The issue of tool replacement is taken into account in the proposed cutting model. In addition, an evolutionary strategy (ES)-based optimization approach is developed to optimize the cutting conditions of the multiple turning-related operations while taking into account the minimizing unit cost criteria under the economical tool replacement strategy.     

Object-oriented design support system for machine tools

This paper deals with an object-oriented intelligent design support system which is intended to assist in the basic design of machine tools, in particular machining centres. The machine tools design process is analysed through interviews with experienced designers, and an object-oriented model is established to represent the design process. Software modules named design objects are proposed, which are basic components for the implementation of an intelligent design support system for machine tools. A prototype of the design support system for machining centres is developed based on the design objects, and some case studies are carried out to verify the effectiveness of the methods proposed.     

Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation

Although machine tool can meet the specifications while it is new, after a long period of cutting operations, the abrasion of contact surfaces and deformation of structures will degrade the accuracy of machine tool due to the increase of the geometric errors in six freedoms. Therefore, how to maintain its accuracy for quality control of products is of crucial importance to machine tool. In this paper, machining accuracy reliability is defined as the ability to perform its specified machining accuracy under the stated conditions for a given period of time, and a new method to analyze the sensitivity of geometric errors to the machining accuracy reliability is proposed. By applying Multi-body system theory, a comprehensive volumetric model explains how individual geometric errors affect the machining accuracy (the coupling relationship) was established. Based on Monte Carlo mathematic simulation method, the models of the machining accuracy reliability and sensitivity analysis of machine tools were developed. By taking the machining accuracy reliability as a measure of the ability of machine tool and reliability sensitivity as a reference of optimizing the basic parameters of machine tools, an illustrative example of a three-axis machine tool was selected to demonstrate the effectiveness of the proposed method.     

Solving the combined part sequencing and tool replacement problem for an automated machining center: A tabu search approach

This paper addresses a joint part sequencing and tool replacement problem on an automated machining center. The objective is to minimize the expected production cost subject to available spare tools. In the literature, it has been shown that the sequencing problems with sequence-dependent setups are equivalent to the “travelling salesman problem” (TSP) and thus are NP-complete. The problem under consideration is further complicated by the position-dependent cost components associated with tool replacement decisions. To provide an efficient planning tool for shop floor decision making, a tabu search approach is proposed. The application of the proposed approach is demonstrated using an example problem. Our computational experience shows that good solutions can be found within a relatively short search time. The impact of tool spare level on the performance of the machining center is also examined. Finally, the effects of tabu-list size and path diversification are discussed.     

Computerised scheduling technique for productivity improvement

The objective of the presented paper is to develop a model for job - shop scheduling that could be applied to manufacturing workshops as a tool for productivity improvement. The model is based on n jobs, m machines and considers the machining times and the machine ordering of each of the jobs on each machine. The model tries to minimise the total make-span of the product as a measure of performance. The model uses the Longest Preceding Path Time (LPPT) scheduling method.

The model was applied to a production workshop that manufactures pontoon sections which are used for constructing the folding pontoon bridges and ferries, besides, some other products. The pontoon section consists of 30 main jobs and uses 7 machines.     

Optimal tool selection for pocket machining in process planning

In process planning for pocket machining, selection of tool size, tool path, cutting width at each tool path, and calculation of machining time are very important factors for optimal process planning. The tool size is the most important factor because the other factors depend on tool size. Therefore, the optimal selection of tool size is the most essential task in pocket machining process planning. This paper presents a method for selecting optimal tools for pocket machining for the components of injection mold. The branch and bound method is applied to select the optimal tools which minimize the machining time by using the range of feasible tools and the breadth-first search.     

Drilling reconfigurable machine tool selection and process parameters optimization as a function of product demand

Special purpose machines (SPMs) are customized machine tools that perform specific machining operations in a variety of production contexts, including drilling-related operations. This research investigates the effect of optimal process parameters and SPM configuration on the machine tool selection problem versus product demand changes. A review of previous studies suggests that the application of optimization in the feasibility analysis stage of machine tool selection has received less attention by researchers. In this study, a simulated model using genetic algorithm is proposed to find the optimal process parameters and machine tool configuration. During the decision-making phase of machine tool selection, unit profit is targeted as high as possible and is given by the value of the following variables: SPM configuration selection, machining unit assignment to each operation group, and feed and cutting speed of all operations. The newly developed model generates any random chromosome characterized by feasible values for process parameters. Having shown how the problem is formulated, the research presents a case study which exemplifies the operation of the proposed model. The results show that the optimization results can provide critical information for making logical, accurate, and reliable decisions when selecting SPMs.     

Estimation of NC machining time using NC block distribution for sculptured surface machining

The estimation of NC machining time is of importance because it provides manufacturing engineers with information to accurately predict the productivity of an NC machine, as well as its production schedule. NC programs contain various machining information, such as tool positions, feed and speed rates, and other machine instructions. Nominal NC machining time can easily be obtained based on the NC program data. Actual machining time, however, cannot simply be found due to the dynamic characteristics of a NC machine controller, such as acceleration and deceleration effect. Hence, this study presents an NC machine time estimation model for machining sculptured surfaces, considering such dynamic characteristics of the machine. The proposed estimation model uses several factors, such as the distribution of NC blocks, angle between the blocks, federates, acceleration and deceleration constants, classifying tool feed rate patterns into four types based on the acceleration and deceleration profile, NC block length, and minimum feed rate. However, there exists an error for the actual machining time due to the lack of the measurement equipment or tools to gauge an exact minimum feed rate. Thus, this paper proposes a machining time estimation model using NC block distributions, lowering down the error caused by the inaccurate minimum feed rate. The proposed machining time estimator performs at around 10% of mean error.     

A configuration independent error model of machine tools: Hyperpatch model and metrology pallet

A machine tool error model based on a three-dimensional hyperpatch error map was developed to describe the machine tool error field over a workspace. The error map was generated by a set of measurement points without using a detailed relationship among joints and linkages of a machine tool. The modeling is based on the motion that an ideal workspace is distorted to an actual workspace through a prescribed parametric representation. The concept was implemented on a three-axis CNC machining center. A metrology pallet was designed to serve as a reference coordinate frame of the workspace. A touch trigger probe was used to collect the required data. Experimental results show that this error model is capable of modelling the warm machine tool distortion behavior. The proposed model is based on the observed phenomena rather than the structural configuration of machine tools. Therefore, the advantage of this approach is that only a few measurements are required to construct the prediction model for a variety of machine tool configurations and for working environments.     

Augmented Reality-assisted Intelligent Window for Cyber-Physical Machine Tools

Aiming to advance current machine tools to a higher level of intelligence and autonomy, this paper presents a new generation of machine tools, i.e. Cyber-Physical Machine Tool (CPMT), inspired by the recent advances in Cyber-Physical Systems (CPS). CPMT refers to a CPS-enabled machine tool that integrates physical machine tool and machining processes with computation and networking capabilities. Augmented Reality (AR) is used to enable intuitive and efficient human-machine interactions between humans and CPMT. An AR-assisted Intelligent Window for CPMT is proposed. The Intelligent Window is essentially an advanced Human-Machine Interface (HMI) which provides users with intuitive interactions with CPMT. The proposed Intelligent Window consists of four main functional modules, Real-time Control, AR-enabled Process Monitoring, AR-enabled Machining Simulation, and Process Optimization. An AR-assisted Intelligent Window for an EMCO Concept 105 milling machine is developed making use of a touch-screen computer. The advantages and potentials of CPS and AR in manufacturing are discussed based on the experience gained from the experiments.     

Remote real-time CNC machining for web-based manufacturing

Today's machining shop floors, characterized by large variety of products in small batch sizes, require dynamic control and real-time monitoring capabilities that are responsive and adaptive to the rapid changes of production capability and functionality. It is especially true when the shop floors are combined with the e-manufacturing concept. However, a highly efficient infrastructure that can integrate the pieces of automated equipment together and link them to the e-manufacturing is still missing. The objective of this research is to develop an appropriate methodology with open architecture for real-time monitoring and remote control of networked CNC machines. A framework named Wise-ShopFloor (Web-based-integrated sensor-driven e-ShopFloor) is designed for this purpose. Within the context, this paper presents a new enabling technology to bring traditional CNC machine tools on-line with combined monitoring and control capability. Issues such as architecture design, methodology development, and prototype implementation are addressed through a milling machine case study. It is expected that the developed technology can be readily applied to real shop floor environments with increased productivity, flexibility, and responsiveness.     

考虑能耗与质量的机床构件生产线多目标柔性作业车间调度方法

针对机床构件的生产存在多品种、小批量、生产能耗大的特点,建立以完工时间、空闲时间、加工质量及机器能耗为目标的多目标柔性作业车间调度模型,提出一种基于直觉模糊集相似度的遗传算法(IFS_GA).该算法将直觉模糊集相似度值作为适应度值来引导算法进化;利用拥挤距离修剪外部档案,提高种群的多样性.此外,为提高初始种群的质量,设计一种基于权重的启发式规则.为提高算法的寻优能力,提出一种新的染色体交叉方法,通过直觉模糊集相似度值选出引导体以引导交叉.最后,在可行的Pareto最优解集中,选择直觉模糊集相似度值最大的解作为最满意解.通过算例测试、实例仿真和QUEST软件验证,结果表明,所提出算法是有效的,并且效果优于NSGAII算法.