Machine Cycle Time Research Articles

ANALYTICAL AND EXPERIMENTAL STUDY OF FEED RATE IN HIGH-SPEED MILLING

In the context of high-speed milling (HSM), during the machining process dynamic machine response has to be identified. To achieve this, we have to calculate the feed rate evolution in linear and circular interpolation according to dynamic performance of machine. In addition to that, actual trajectory for transition passages between two interpolations must be estimated with take into account of specific machining tolerances. This article proposes a model of machine tool behavior for a tool path with linear and circular interpolations and machining cycle time prediction. The method involves subdividing the trajectories into elementary geometries according to the type of interpolation (circular or linear). At points where different trajectories meet, there is often a discontinuity in curvature or in tangency, which decreases the feed rate. At the points of discontinuity in tangency, a fillet radius is inserted. In this article, the influence of the geometry for elementary trajectories was determined. Then, the value of the fillet radius between linear and circular contours in different combinations was modeled. An industrial application was carried out in order to validate models and to determine the influence of feed rate evolution on the machining cycle time.

A High Performance Computing Cloud Computing Environment for Machining Simulations

Machining is a pervasive manufacturing process used in industries such as automotive, aerospace, medical implants and oil and gas. Analysis of processes via physics-based modeling enables new and innovative designs for cutting tools, provides confidence of machined workpiece quality characteristics and allows reduction in machining cycle times and tooling costs. Progressively sophisticated analyses of machining processes have evolved with the inclusion of effects of full three-dimensional analysis of cutting tools and complex tool/workpiece kinematics. Detailed-level analysis of machined workpiece surfaces based on finite element method (FEM) allows prediction of residual stresses, work hardened layer depths and heat flow. However, with the increase in model sophistication has come with computational burden. This paper details a high performance computing (HPC) environment for finite element models used for machining analysis. First, the FEM model is reviewed and its extension to high core-count shared memory environments is described. Scaled performance improvements for up to forty cores are demonstrated and performance improvements documented. Next, an HPC cluster is designed and a specialized batch queuing software is implemented that allows submission, monitoring and management of large scale machining simulations. Finally, an infrastructure for delivering the HPC capability to customers through Software as a Service (SaaS) is introduced.

Energy-Efficient Production Systems Through Schedule-Based Operations

Control of production operations is considered as one of the most economical methods to improve energy efficiency in manufacturing systems. This paper investigates energy consumption reduction in production systems through effective scheduling of machine startup and shutdown. Specifically, we consider serial production lines with finite buffers and machines having Bernoulli reliability model. This machine reliability model is applicable in production situations, where the downtime is relatively short and comparable to machine cycle time (e.g., automotive paint shops and general assembly). In this paper, using transient analysis of the systems at hand, an analytical performance evaluation technique is developed for Bernoulli serial lines with time-dependent machine efficiencies. In addition, tradeoff between productivity and energy-efficiency in production systems is discussed and the energy-efficient production problem is formulated as a constrained optimization problem. The effects and practical implications of operations schedule are demonstrated using a numerical study on automotive paint shop operations. Note to Practitioners - This paper develops an effective analytical tool to evaluate the performance of production systems with time-varying parameters of machine reliability. Using this tool, production engineers and managers can predict the performance of the production systems in real-time with high accuracy. In addition, based on this tool, production operators can determine the machine startup and shutdown schedule based on the current status of the line and production requirement. Numerical experiments show that significant energy savings can be obtained by applying effective machine operations schedule.

To Automate AMF Pinspotter Machine

This paper gives instructions for making complete and efficient control system of AMF bowling machine, it helps decreasing machine cycle time, make the machine energy efficient, reduce error in scoring, perform automatic resetting of machine in the event of power failure, calculating and displaying accurate speed and giving more options for user interface.

6 axis robot reduces machine cycle times by one-third

6 axis robot reduces machine cycle times by one-third

Application of Extracted Rules From a Multilayer Perceptron Network to Moulding Machine Cycle Time Improvement

Product delivery time is as vital as the yield rate for semiconductor manufacturing companies since the industry has become highly competitive and more dynamic nowadays. In the manufacturing process, appropriate parameter settings can shorten the machine cycle time and in turn the product delivery time. This paper presents the application of 'IF-THEN' rules extracted from a multilayer perceptron (MLP) artificial neural network to tune moulding machine parameters to improve the machine cycle time. We can extract the 'IF-THEN' rules by scrutinising the connecting weights inside the MLP network. Historical genuine operating data of a moulding machine input parameters and cycle time were collected from an integrated circuit packaging company in Taiwan. The data were fitted in with an MLP network, and the 'IF-THEN' rules were extracted afterwards. The 'IF-THEN' rules not only indicate which input parameters dominate the machine cycle time but also explain how to tune them. We have applied the extracted rules to tune the moulding machine parameters. Practical results show that the moulding machine cycle time agrees very well with the extracted rules, and also justify the feasibility of the proposed method.

Improvement of Tops Spinning Manufacturing with CNC Lathe

This paper presents an increasing demand of Top-Spinning (T-S) has resulted in the production of high quantity of T-S. Currently, the production of T-S is very low and not up to the required demand. This is due to the old machine used as well as an old method in the manufacturing of T-S, which lead to the longer time taken compared to the new technology found in this project. The cardinal purpose of this project is to identify the machining sequence with the selection of a suitable tool and reducing machining cycle time with the use of CNC Lathe for manufacturing T-S. In ensuring a successful and optimum result of the project, manufacturing T-S with CNC Lathe will be assisted by Mathematisch Technishe software Entwicklung (MTS) and Master CAM simulation to determine machining sequence, cutting tools, technology parameters and machining cycle time. The data extracted from MTS, and CAD/CAM simulation process will then be used for manufacturing T-S on CNC Lathe technology. This project emphasizes on the importance of determining machining sequence, cutting tools to manufacture T-S in any shape available and also resolving machining cycle time. The result from MTS and CAD/CAM with three analyses of machining sequence, cutting tool and technology parameters will be used to proceed on CNC Lathe. Finally, with the utilization of the data extracted from MTS software, and CAD/CAM simulation has successfully impacted the reduction on overall machining cycle time while producing T-S.

Fuzzy Logic-Based Torque Control System for Milling Process Optimization

This paper focuses on the design and implementation of a fuzzy-logic-based torque control system, embedded in an open-architecture computer numerical control (CNC), in order to provide an optimization function for the material removal rate. The control system adjusts the feed rate and spindle speed simultaneously as needed, to regulate the cutting torque using the CNC's own resources without requiring additional hardware overheads. The control system consists of two inputs (i.e., torque error and change of error), two outputs (i.e., the feed rate and spindle speed increment) fuzzy controller, and a self-tuning mechanism, all of which are embedded within the kernel of a standard open control. The self-tuning strategy is based on the measured peaks in the torque error signal of the closed-loop system response. The self-tuning fuzzy controller is applied to the milling process in a production environment in order to demonstrate the improvements in performance and effectiveness. Two approaches are tested, and their performance is assessed using several performance measurements. These approaches are the two-input/two-output for the fuzzy controller and a single-output fuzzy controller (i.e., only feed-rate modification), with and without the self-tuning mechanism. The results demonstrate that the proposed control strategy provides better transient performance, accuracy, and machining cycle time than the others, thus, increasing the metal removal rate.

Vacuum Coating Technology For Optical Data Storage Media

The manufacture of optical data storage media like CD and DVD has grown into a highly specialized large-scale industrial production. The following is a retrospective of developments in coating and system technology over the past 20 years. System reliability, stable processes and fast machine cycle times are key elements for efficient replication. Vacuum coating technology in particular contributes to a product-optimized process. Multilayer systems and the shift to blue laser recording in emerging formats serve to further increase disc storage capacity.

Fuzzy Control of Spindle Torque in High-Speed Milling Processes

This paper presents the design and implementation of a two-input/two-output fuzzy logic-based torque control system embedded in an open architecture computer numerical control (CNC) for optimizing the material removal rate in high-speed milling processes. The control system adjusts the feed rate and spindle speed simultaneously as needed to regulate the cutting torque using the CNC’s own resources. The control system consists of a two-input (i.e., torque error and change of error), two-output (i.e., feed rate and spindle speed increment) fuzzy controller, which is embedded within the kernel of a standard open control. Two approaches are tested, and their performance is assessed using several performance measurements. These approaches are a two-input/two-output fuzzy controller and a single-output (i.e., feed rate modification only) fuzzy controller. The results demonstrate that the proposed control strategy provides better accuracy and machining cycle time than other strategies, thus increasing the metal removal rate.

Simulation of flank milling processes

The paper presents prediction of cutting forces when flank milling ruled surfaces with tapered, helical, ball end mills. The geometric model of the workpiece is imported from standard CAD systems, and the tapered helical ball end mill is modeled as the combination of sphere and cone primitives in ACIS © solid modeling environment. The intersection of cutter and part with a ruled surface is evaluated, and the cutter entry into and exit angles from the work material are modeled, and stored as a function of tool center coordinates along the path. The cutter entry and exit angles, the immersion angles, are used as boundary conditions in predicting the cutting forces along the path. The methodology allows prediction of cutting load distribution on the tool and part, as well optimization of machining cycle times by scheduling the feedrate in such a way that torque, power and static deflections can be maintained at safe levels.

A computational intelligent fuzzy model approach for excavator cycle time simulation

The tracked hydraulic excavator is one of the most versatile and widely utilised piece of earthmoving equipment. In many instances, the ‘excavator’ represents the first choice of earthmoving plant for both construction managers and estimators, since when properly employed (i.e. with a competent operator and in an appropriate working environment), it offers high production rates at economical cost. Nonetheless, predicting machine production performance is difficult; given the typical multiple operational parameters (e.g. machine weight, machine configuration, ground conditions, operator ability) that can apply. Consequently, determination of accurate cost estimates and predicted contract durations are subject to considerable inaccuracy, especially where a significant amount of site work is needed. To address this inadequacy, this paper presents a computational intelligent ‘fuzzy’ model with the ability to forecast excavator cycle time. In this context, a cycle is defined as one complete revolution, from ‘place empty bucket in dig material’ through ‘fill bucket’, ‘move charged bucket to target’, ‘empty charged bucket’ and ‘return bucket to dig material’. The developed model is based upon 70 separate cycle time observations obtained from four plant manufacturers. These data provide a representative spread of machine cycle times since they include a range on a continuum from optimum to adverse operational parameters. Tests on the derived model identified that its accuracy was acceptable; but the accuracy could be improved using larger samples and a more comprehensive and exhaustive range of variables to predict machine cycle time.

Predicting downtime costs of tracked hydraulic excavators operating in the UK opencast mining industry

This paper describes the development of a model to predict the hourly cost of downtime (using regression equations) for tracked hydraulic excavators operating in the UK opencast mining industry. A three-stage process was utilized for the model's development. The first stage predicted machine cycle times, the second predicted hire costs per hour and the third used the outputs of the first two to forecast the cost of breakdown. Both cycle time and hire cost models were revealed to be good predictors, as exhibited by the ‘high’ R2 values of 0.86 and 0.95, respectively. A plant expert employed within the Defence Logistics Organisation, UK Ministry of Defence, validated these regression models and the process by which downtime costs were predicted. Future research work will aim to enhance the predictive ability of the models developed, expand the research to cover other machine types, and reproduce the findings in graphical and tabular format to improve the interpretation of information generated.

Influence of tool path strategy on the cycle time of high-speed milling

This work at first look discusses the influence of the tool path strategy on the cycle time of high-speed milling operations. Experiments and predictions were focused on pocketing operations with a zig-zag tool path, quantifying the significant discrepancy between the programmed feed rate and the actual average feed rate. A mechanistic approach for cycle time evaluation is proposed. The mechanistic model construction is based on the experimental measurement of the machine tool acceleration and specific geometric assumptions regarding tool motion. For high feed rates, the proposed approach is capable of capturing the influence of the zig-zag tool path orientation on the machining cycle time.

Artificial intelligence approach to calculation of hydraulic excavator cycle time and output

Accurate prediction of the cycle time and output of tracked hydraulic excavators is notoriously difficult, not least because such data are freely available to the mining practitioner from only a limited number of plant manufacturers. Previous research attempted to rectify this problem through the development of ESTIVATE. ESTIVATE utilized a multiple regression (MR) equation to predict machine cycle time and subsequently, on the basis of this, to estimate machine output and excavation costs. However, with a coefficient of determination (R2) of 0.88 and a mean absolute percentage error (MAPE) of 20%, the MR equation failed to provide an adequately robust predictor of machine cycle time. Improvement to ESTIVATE's predictive capacity was sought through the use of a feed-forward artificial neural network with back-propagation training. With a sum square error of 0.194 and a MAPE of 7% (that is, a 14% reduction on the equivalent MR equation) the feed-forward network provides a significant improvement over the MR equation. An aim in future work will be to expand the capability of the model to include long-reach machines.

ESTIVATE: a model for calculating excavator productivity and output costs

Hydraulic excavator cycle time and associated unit costs of excavation for given input estimating data, for machines operating in the UK construction industry, are predicted. Using multiple regression analysis, three variables are identified as accurate predictors of cycle time: machine weight, digging depth and machine swing angle. With a coefficient of determination (R2) of 0.88, a mean percentage error (MPE) of −5.49, and a mean absolute error (MAPE) of 3.67, the cycle time model is robust; this is further validated using chi‐square analysis and Pearson's correlation coefficient (on predicted and actual values of machine cycle time). An illustrative example of the model's application to determine machine productivity is given. The paper concludes with a spreadsheet model for calculating excavation costs (m3 and cost per h) which is able to deal with any combination of the three independent cycle time predictor variables and other estimator's input data.

ESTIVATE: a model for calculating excavator productivity and output costs

Hydraulic excavator cycle time and associated unit costs of excavation for given input estimating data, for machines operating in the UK construction industry, are predicted. Using multiple regression analysis, three variables are identified as accurate predictors of cycle time: machine weight, digging depth and machine swing angle. With a coefficient of determination (R2) of 0.88, a mean percentage error (MPE) of −5.49, and a mean absolute error (MAPE) of 3.67, the cycle time model is robust; this is further validated using chi‐square analysis and Pearson's correlation coefficient (on predicted and actual values of machine cycle time). An illustrative example of the model's application to determine machine productivity is given. The paper concludes with a spreadsheet model for calculating excavation costs (m3 and cost per h) which is able to deal with any combination of the three independent cycle time predictor variables and other estimator's input data.

An intelligent knowledge-based system for wire-electro-erosion dissolution in a concurrent engineering environment

An intelligent knowledge-based system for evaluating wire-electro-erosion dissolution (WEED) in a concurrent engineering (CE) environment and based on object-oriented techniques, is introduced. The design description is obtained through a feature-based approach. Nine different classes of design features are interactively acquired. The attributes of steel as a workpiece material, copper wire as a tool material, a single electrolyte solution, one type of WEED machine, and machining conditions such as current pulse on- and off-time, and nozzle distance, are stored in a database. For each design feature, information needed in manufacturing, such as the machining cycle time and cost, material removal rate, width of cut, maximum and working feed-rate, cutting area, and operation efficiency are estimated.

Combining loop transformations considering caches and scheduling

The performance of modern microprocessors is greatly affected by cache behavior, instruction scheduling, register allocation and loop overhead. High level loop transformations such as fission, fusion, tiling, interchanging and outer loop unrolling (e.g., unroll and jam) are well known to be capable of improving all these aspects of performance. Difficulties arise because these machine characteristics and these optimizations are highly interdependent. Interchanging two loops might, for example, improve cache behavior but make it impossible to allocate registers in the inner loop. Similarly, unrolling or interchanging a loop might individually hurt performance but doing both simultaneously might help performance. Little work has been published on how to combine these transformations into an efficient and effective compiler algorithm. In this paper we present a model that estimates total machine cycle time taking into account cache misses, software pipelining, register pressure and loop overhead. We then develop an algorithm to intelligently search through the various possible transformations, using our machine model to select the set of transformations leading to the best overall performance. We have implemented this algorithm as part of the MIPSPro commercial compiler system. We give experimental results showing that our approach is both effective and efficient in optimizing numerical programs.

Intelligent concurrent manufacturability evaluation of design for electrochemical machining

An intelligent knowledge-based system (IKBS) for evaluating electrochemical machining, in a concurrent engineering (CE) environment and based on object oriented techniques, is introduced. The design specification is acquired through a feature-based approach. Ten different classes of design features are interactively obtained. The attributes of 72 workpiece material types, eight tool-electrode metals, two electrolyte solutions, and seven different sizes and types of electrochemical machines are stored in a database. For each design feature, information needed in manufacturing, such as the machining cycle time and cost, penetration rate, efficiency and effectiveness, costs for electricity consumed, machine installation and depreciation are estimated. Finally, for the same design specification, machining times are compared and ordered, for alternative unconventional processes of electrochemical, electrodischarge and electrochemical are machining.