Planning Of Machining Operations Research Articles

A Synthesis-based Tool Path Planning Approach for Machining Operations

The planning of machining operations for components using CAM requires careful consideration by highly trained personnel, with rising complexity in part requirements. This work proposes a novel domain-specific planning technique using Combinatory Logic Synthesis to generate a multitude of tool paths. By analyzing current state CNC path planning algorithms, motion primitives are identified and implemented in a modular representation using software components. Thus, a large variety of path planning strategies can be generated, while maintaining a multitude of different boundary constraints and productivity metrics. These solutions are subjected to geometric physically-based simulations in order to validate their ranking and compliance.

Fuzzy logic and sub-clustering approaches to predict main cutting force in high-pressure jet assisted turning

Due to the complexity of the high-pressure jet assisted turning, knowledge, and prediction of the cutting forces are essential for the planning of machining operations for maximum productivity and quality. However, it is well known that during processing using this procedure there are difficulties in collecting data. It is required to establish an adequate model that would make it possible to predict the cutting force based on the input parameters. During machining to avoid difficulties in acquisition data, two models have developed based on fuzzy logic that will allow indirect monitoring of the cutting force. This research uses the improved fuzzy logic methods for modeling, whereby it can make predictions of the main cutting force according to the different input parameters. The contribution of this work reflected through the application of two innovative methods based on reducing the number of rules, which leads to better interpretability of models. First is the Mamdani with rule reduction method, and second is the Sugeno sub-clustering method based on the identification of the model structure, it comes down to finding the required number of rules by forming specific clusters. Both approaches differ by reducing the number of rules without affecting the accuracy of the models. The ability to predict the model determined by applying different statistical parameters. It concluded that Mamdani and Sugeno models give an approximate quality of the prediction. The resulting models also have an acceptable error to predict data that did not participate in their creation. Furthermore, obtained models can be used at the generalization stage where the cutting force information is required and where direct measurement is not possible.

Cloud-DPP for distributed process planning of mill-turn machining operations

Abstract Today, the dynamic market requires manufacturing firms to possess a high degree of adaptability to deal with shop-floor uncertainties. Specifically targeting SMEs active in the metal cutting sector who normally deal with intensive process planning problems, researchers have tried to address the subject. Among proposed solutions, Cloud-DPP elaborates a two-layer distributed adaptive process planning based on function-block technology and cloud concept. One of the challenges of companies is to machine as many part features as possible in a single setup on a single machine. Nowadays, multi-tasking machines are widely used due to their various advantages such as reducing setup times and increasing part accuracy. However, they also possess programming challenges because of their complex configuration and multiple machining functions. This paper reports the latest state of design and implementation of Cloud-DPP methodology to support parts with a combination of milling and turning features, and process planning for multi-tasking machining centers with special functionalities to minimize the number of setups. The contributions of this work are: representation of machining states and part transfer functionality, support of multi-tasking machines in adaptive setup merging, development of special function blocks to handle sub-setups and transitions, and finally generation of function-block network for the merged setups. The developed prototype is validated through a case study.

加工事例を参照する作業設計における切削条件の適応的修正法

A new methodology to revise cutting conditions for machining operation planning has been developed in this study. In order to automate machining operation planning, reuse of cutting conditions in the past machining operations was proposed in our previous study. However, assessment and revision of cutting conditions are required to apply for the objective machining operation from the reference machining operation. For the assessment of cutting conditions, an acceptable area to select cutting conditions (AACC) is calculated. The AACC can be calculated under several limitations such as spindle power and maximum feed speed of machine tools, torsional and bending strength of cutting tools and so on. In the case that the reference cutting condition stays in the AACC, the reference cutting condition can be applied for the objective machining operation without any revision. In the case that the reference cutting condition stays out the AACC, the reference cutting condition should be revised to adapt for the objective machining operation. In our study, a methodology, which revises cutting conditions under keeping productivity, surface roughness (cusp height) and tool life, was proposed. In this report, an expanded methodology to revise cutting conditions by qualified relaxation of machining requirements is proposed to achieve wide range adaptation of the reference cutting conditions to the objective machining operation.

加工事例を再利用してNCプログラムを作成する機械加工用作業設計システム

A new methodology to automate machining operation planning is proposed. A new machining operation plan is reconfigured from past case data or past machining operation data stored in the database. Machining information, e.g. cutting tool, cutting conditions, tool path pattern, is associated with a machining feature. A machining feature is recognized from the 3D CAD model of the finished shape. Sets of machining information are stored as the past case data with their 3D CAD models. In order to generate a new machining operation plan of a new product, machining features are recognized based on topological relationship of the 3D CAD model first. Then, each of machining feature is compared with machining features contained in 3D CAD models stored as the past case data quantitatively in terms of shape, size and material. Finally, the most similar machining feature is selected as the reference one. The machining information associated with the reference machining feature can be applied for each machining feature recognized in the new product. Finally, the NC program to perform a new machining operation is generated automatically. The usability and effectiveness of this proposed system to save time and effort of human operator was verified through a case study.

An Implementation of Web-based Machining Operation Planning

This paper introduces a machining operation planning system for practical use in small and medium sized manufacturers. The system passes through two stages: manual input of required information and semi-automated generation of an operation plan. At the first stage, removal volumes out of a workpiece and their reference details on the part drawing are translated into the specific machining features manually. The operations to be performed for each feature are then selected from the operation list related to the feature. At the second stage, the cutting tool for each operation is determined with its proper cutting conditions by using a rule-based algorithm and retrieving a machining database gradually updated. The web interface makes it possible for the manufacturers to keep a record of their machining practice in the database and obtain the favorable data from the various sources when needed. An example is shown to demonstrate the usefulness of the system.

S1310205 加工事例を参照する作業設計における切削条件の適応的修正法

A new methodology to revise cutting conditions for machining operation planning has been developed in this study. In order to automate machining operation planning, reuse of cutting conditions in the past previous machining operations was proposed in our previous study. However, assessment and revision of cutting conditions are required to apply the referred cutting conditions for the objective machining operation. For the assessment of cutting conditions, an acceptable area of cutting conditions (AACC) is calculated. The AACC is restricted by several constraints such as spindle power capacity and maximum feed speed of machine tools, torsional and bending strength of cutting tools and so on. In the case that the referred cutting conditions stay in the AACC, the referred cutting condition can be applied for the objective machining operation without any revision. In the case that the referred cutting conditions stay out the AACC, the referred cutting conditions should be revised for the objective machining operation. In this study, the referred cutting conditions are revised to keep machining time, surface roughness (cusp height) and tool life as much as possible. In this paper, the restriction by the spindle power capacity of machine tools is confirmed in cutting experiments. Also, the revision of the referred cutting conditions is performed for the objective machining operation.

D02 加工事例再利用による機械加工用NCデータ自動作成システムの開発(OS4 生産システムとCAD・CAM(1))

A new methodology to automate machining operation planning is proposed. A new machining operation plan is reconfigured from past case data or past machining operations. Machining information, e.g. cutting tool, cutting conditions, tool path pattern, is associated with a machining feature. A machining feature to be cut is related to 3D CAD model of workpiece. Sets of machining information and a 3D CAD model is stored as the past case data. In order to generate a new process plan of a new workpiece, machining features are recognized first. Then, each of machining feature is compared with machining features contained in 3D CAD models of workpieces stored as the past case data, and the most similar machining feature is selected. The machining information associated with the selected machining feature can be applied for each of machining feature consisted in the new workpiece.

CAM-Based Planning, Programming and Execution of Large-Scale Machining Operations by a Robot-Mounted Gantry System

CAM-Based Planning, Programming and Execution of Large-Scale Machining Operations by a Robot-Mounted Gantry System

Integrated assembly and machining planning for electronic products using a genetic algorithm approach

To produce an electronic product, both assembly operations and machining operations are required in the process plan. In most cases, the assembly operations and machining operations need to be combined in a continued order with an integrated sequence. This is different from the traditional process planning approaches in which machining operations and assembly operations are separated as two independent tasks with no interactions. For an electronic product, the two types of operations and the associated costs may affect each other in an interactive way. Therefore, the sequence planning of assembly operations and machining operations must be analyzed with an integrated model. In this research, a graph-based model is presented to represent the assembly and machining operations in an integrated model. The related operation cost functions are developed to evaluate the costs for the integrated assembly and machining sequences. The integrated sequence planning problem is solved using a genetic algorithm approach with an objective of lowest operation costs. As a result, the assembly operations and machining operations can be planned in an integrated sequence suitable for producing electronic products. The result shows that the developed method using the genetic algorithm approach is efficient for solving the integrated sequence planning problem. Example products are demonstrated and discussed.

Off line simulation system of machining processes

The primary objective for modeling of machining processes is to develop a predictive capability of machining performance in order to facilitate effective planning of machining operations. This capability leads to faster implementation, higher performance, quality at a lower cost. This comes about due to improved selection of machining parameters, optimal fixture design and the avoidance of tool failure. The simulation system presented simultaneously considers the effect of cutter geometry, the cutter's initial position errors, workpiece geometry, machine tool dynamics, and workpiece/fixture system dynamics on the machining process. The integration of all of the above in one model provides an off-line tool to simulate and optimize the machining parameters and the fixture configuration cutting both lead and production time. The modular nature of the simulation system presented allows for the study of many different machining processes. The cutting forces in this system are modeled using a mechanistic approach. NURBS curves and surfaces are utilized for the geometric modeling and simulation of the machining process. While a finite element method is used to model and analyze the workpiece/fixture dynamics. Two case studies are presented to demonstrate the practical application of the presented simulation. The first case presents the optimization of the fixture configuration of a generic automotive component. While the second case presents the results of simulations performed on a novel mill/grind machining process. This process is a combination of face milling and grinding in one operation. Some simulated results are presented along with experimental validation.

A constraint-based operation sequencing for a knowledge-based process planning

Process planning is a decision-making process. Decisions on machining operations for a particular feature have to be made on various independent conditions such as which operation should be performed with which tools and under what cutting parameters. An integrated knowledge-based CAPP system called ProPlanner has been developed. The system has five modules namely information acquisition, feature recognition, machining operation planning and tool selection, set-up planning, and operation sequencing. Most process-planning systems do not produce alternative process plans. Usually, a fixed sequence created by a process plan is not necessarily the best possible sequence. Therefore, the aim should be to generate all possible operation sequences and use some optimality criteria to obtain the best sequence for the given operating environment. This paper presents an efficient heuristic algorithm, belongs to the system's operation sequencing module, for finding near-optimal operation sequences from all available process plans in a machining set-up. The costs of the various machining schemes are calculated and the machining scheme with the lowest cost is chosen. All feasible cutting tools are identified for each particular feature and the corresponding machining operations. This process is repeated for all the features in the machining set-up. All possible feature sequence combinations allowed by the current feature constraints are then generated. Appropriate cutting tools are identified and assigned to different operations. The feature sequence with the smallest number of tool changes is adopted.

A novel chatter stability criterion for the modelling and simulation of the dynamic milling process in the time domain

The modelling of the dynamic processes in milling and the determination of chatter-free cutting conditions are becoming increasingly important in order to facilitate the effective planning of machining operations. In this study, a new chatter stability criterion is proposed, which can be used for a time domain milling process simulation and a model-based milling process control. A predictive time domain model is presented for the simulation and analysis of the dynamic cutting process and chatter in milling. The instantaneous undeformed chip thickness is modelled to include the dynamic modulations caused by the tool vibrations so that the dynamic regeneration effect is taken into account. The cutting force is determined by using a predictive machining theory. A numerical method is employed to solve the differential equations governing the dynamics of the milling system. The work proposes that the ratio of the predicted maximum dynamic cutting force to the predicted maximum static cutting force can be used as a criterion for the chatter stability. Comparisons between the simulation and experimental results are given to verify the new model.

Reverse engineering of machining operation planning

Computer-aided process planning (CAPP) is becoming increasingly crucial to today's computer-integrated manufacturing (CIM) and rapid production. To automate the process planning, feature-based operation planning systems have been suggested and studied extensively. In such a system, given a machining feature, the operator requires practical machining operation data for the feature. In this research, a system of reverse engineering is proposed to extract machining features and their associated machining operation data. Furthermore, a machining know-how database containing the extracted data is created for future operation planning. Since successful NC programs contain the machining know-how of skilled workers, the system is aimed at extracting the machining know-how data from the NC programs through reverse engineering. The extraction of the machining features and feature topologies has been addressed previously. The present paper deals with the extraction of machining operation data, including operation sequence, cutting conditions, machining type and cutting mode. A prototype of the system is developed and a machining know-how database is generated. The extraction of machining features and their associated machining operations has been verified through a variety of NC programs.

An expert system of machining operation planning in Internet environment

A framework for machining operation planning systems is discussed, in which machining know-how, extracted and organized from electronic tool catalogs and machining instance databases available in the Internet environment plays a principal role. A system concept based on the use of reference machining instance data is proposed, which is derived from the investigation of tool catalogs, related international standards, reference textbooks, and handbooks. The format is used partly during user input and database inquiry to extract required data effectively from the databases. In developing system organization, WWW technology, including XML markup language and the Java programming language, is utilized. A prototype system to advise the engineer of cutting conditions including trouble shooting for side end milling is developed to demonstrate the concept. For a feasibility test, sample databases of tool catalogs and the machining instance data of heat resistant super alloys are implemented and used. From the case studies, the concept and the implementation method are evaluated, and found to be practical and effective at the time the information infrastructure is established.

Recognition of machining features and feature topologies from NC programs

The machining feature is a high-level product geometric representation and macro description of the product geometry. It is vital to machining operation planning and integration of CAD and CAM systems. In the feature-based operation planning, given a feature, the operator needs its associated machining operation data. Since successful NC programs contain machining know-how of skilled workers associated with the machining features, this paper studies the recognition of machining features in milling from the NC programs through reverse engineering, for reuse of the know-how in operation planning. First, the concepts of the machining element, machining feature and feature topology are presented, and the classification and representation of the features are performed from the machining point of view. Then algorithms for recognizing the machining features and feature topologies by analyzing NC programs are developed based on progressive Z-maps. Finally, a prototype is developed to extract the machining features and their associated operation data, and the machining know-how database is generated accordingly. The recognition of a variety of machining features and topologies, as well as their associated machining operations, is demonstrated through an example part from NIST parts repository. By using the generated machining know-how database, the machining operation planning will be automated by its counterpart system—the machining operation planner.

ANALYTICAL MODELING OF FORCE SYSTEM IN BALL-END MILLING

This paper establishes ah analytical expression for dynamic cutting forces involved in machining with multiflute ball-end cutters. The expression is given in explicit terms of cutting parameters, tool-workpiece geometry, and machining configuration. A model of this nature can assist in the planning of machining operations, the design of machine tool components, the optimization of cutting parameters, and the monitoring/control of process parameters. The concept involved in this model is the mathematical characterization of the cutter-workpiece interaction in terms of the chip width density function in an angular convolution form. This characterization allows the dynamic cutting forces to be algebraically represented in the frequency domain. The analytical development of the model is discussed and the experimental verification presented.

The planning of multi-volume rough machining operations

The authors show that results from previous research in the area of machining economics may be used to obtain good solutions to the problem of planning the production of parts which have machined volumes which intersect one another. The basic problem involves the determination of methods of subdividing the total machined volume so that cost is minimized; the method derived is based upon cost per unit volume, the major advantage being that a good solution may be obtained without conducting an exhaustive evaluation of the costs for each feasible volume combination.

A structured approach to the automatic planning of machining operations for rotational parts based on computer integration of standard design and process data

The traditional procedure for developing a manufacturing process plan involves a number of interconnected steps beginning with the initial design and culminating in the instructions for the machine that makes the part. When performed manually, the procedure is very tedious, time consuming, and often, inconsistent. This paper presents an alternative method, called the Automated Machining-Operations Process-Planning System (AMOPPS), to automatically generate the process plan and the numerical control (NC) instructions to machine a part that has been previously designed by a computer-aided design (CAD) system. AMOPPS combines preset planning logic and data from three databases (viz., workpiece material, machine tool, and cutting tool) with appropriate data from a CAD database and information from the user about the workpiece to calculate the optimal machining parameters, print a process plan, print the corresponding NC program listing, and provide an on-screen animation of the cutting passes. Although AMOPPS is a prototype, it can be extended by adding other modules, and it does provide a structure through which a fully integrated manufacturing system can be developed.

An Expert System Framework for Economic Evaluation of Machining Operation Planning

Recognising the importance of combining manufacturing and management systems for machining operation planning, this paper presents a new methodology for the evaluation of economic aspects in an operation plan. To ensure that the quality of machined parts satisfies the required specifications, the manufacturing system acts as an alternative generator that provides meaningful and practical plans. Through cost analysis, the variable, fixed, and total costs associated with the machining operation are quantitatively determined. The management system, which functions as an evaluation mechanism, then selects the optimal plan based on the defined goal. The proposed methodology has been applied in the framework design of an expert system. The program establishes a sequence of machining operation planning and searches for the optimal plan. This optimal plan integrates considerations from both managers and production engineers, and balances their needs for efficient machining of a quality product.