Generation Of Process Plans Research Articles

Generative Process Planning with Reasoning Based on Geometrical Product Specification

The focus of this paper is on CAPP for parts manufacture in systems of definite process capabilities, involving the use of multi-axis machining centers for parts shaping and grinding machines for finishing. It presents in particular a decision making scheme for setup determination as a part of generative process planning. The planning procedure consists of two stages. The first stage is associated with generic setup determination applying tool accessibility analysis to machined features and reasoning based on geometrical product specification (GPS). The second stage involves machine specific setup planning considering the availability and capability of machines on a shop floor through setup merging. The impact of dimensional and geometric tolerance as well as the surface roughness of relatively complex mechanical element on the generated process plan with several setups was presented. The relevant reasoning mechanism within a decision making scheme on generated process alternatives is shown based on a numeric case.

The development of an integrated haptic VR machining environment for the automatic generation of process plans

In modern manufacturing it is crucial to be able to produce a product right first time, as efficiently as possible. Virtual prototyping can play a key part in fulfilling this requirement, allowing the simulation of production in advance without the need for unnecessary downtime, material waste or increased lead times. This makes virtual reality (VR) potentially an ideal planning tool since, with each simulation, a better understanding of requirements can be reached and a more effective solution found.This application of haptic virtual reality is particularly relevant in the field of machining where both material and resource costs are high. Planning for the machining of a product requires an intricate knowledge of materials, processes and machining methods. Intuitive virtual reality interfaces can provide new, engineer-friendly means of generating such manufacturing sequences.Current research in haptic virtual reality focuses on specific individual aspects of machining such as path planning or simulation for training; however, in order to simulate multi-operation machining with a view to generating practical and usable process plans, in which there are several processes involved in bringing a product to fruition; an approach is required which allows this type of functionality.This paper presents such a haptic virtual process planning system that allows an operator to load and set up a billet for machining, set up, sequence and tear down any combination of milling, drilling or turning operations then produce a time-estimated process plan. All of the human expertise captured during this process is logged and parsed to generate the final instructions including operation times, tool lists, operation lists and route sheets in easy-to-read format.Via three planning examples, the generation of process plans is demonstrated; with these subsequently validated through their use by a shop floor machinist who utilised them to produce the parts, and a comparison to previously existing plans for the same parts.

Landscape generator: Method to generate landscape configurations for spatial plan-making

Current spatial planning tools focus mainly on land use evaluation and not on spatial plan design. Automated generation of spatial design intends to bridge the gap between graphical design and geographical information systems. We propose a new method that generates spatial plans with a high level of detail and realism. A simple set of rules is derived by the modeler from a landscape type that serves as a reference. These rules are implemented by spatial functions with a landscape type specific objective. In a multi-objective optimization algorithm a landscape plan is generated for a specific lot that meets the objectives. The modeler controls the plan generation process through an objective task list which determines the priority of the objectives. Examples show that the landscape generator is capable of generating plausible spatial plans in reasonable computing time. More specifically, they show that the landscape generator performs best with low configurationally structured reference projects and performs less with high configurationally structured reference projects. The manual construction of the objective task list could be further improved by pre-structured objectives dependent on landscape designer’s preference.

Process Plan Generation in Reconfigurable Manufacturing Systems using AMOSA and TOPSIS

Reconfigurable manufacturing system (RMS) is a recent manufacturing paradigm that responses to market changes cost-effectively. RMS consists mainly of reconfigurable machines, each of which has multiple configurations. In this paper we address the problem of process plan generation in RMS taking into account the multi-configuration nature of machines, in a multi-objective aspect. More specifically, we adapt AMOSA (Archived Multi-Objective Simulated Annealing) metaheuristic, to generate a set of non-dominated solutions, based on three criteria: completion time, total cost and reconfiguration effort. After that, TOPSIS method is used to rank these solutions in a preferential order.

Feature-based generation of machining process plans for optimised parts manufacture

Efficacious integration of such CAx technologies as CAD, CAM and CAPP still remains a problem in engineering practice which constantly attracts research attention. Design by feature model is assumed as a main factor in the integration effort in various engineering and manufacturing domains. It refers principally to feature clustering and consequently operation sequencing in elaborated process plan designs. The focus of this paper is on CAPP for parts manufacture in systems of definite processing capabilities, involving multi-axis machining centres. A methodical approach is proposed to optimally solve for process planning problems, which consists in the identification of process alternatives and sequencing adequate working steps. The approach involves the use of the branch and bound concept from the field of artificial intelligence. A conceptual scheme for generation of alternative process plans in the form of a network is developed. It is based on part design data modelling in terms of machining features. A relevant algorithm is proposed for creating such a network and searching for the optimal process plan solution from the viewpoint of its operational performance, under formulated process constraints. The feasibility of the approach and the algorithm are illustrated by a numerical case with regard to a real application and diverse machine tools with relevant tooling. Generated process alternatives for complex machining with given systems, are studied using models programmed in the environment of Matlab® software.

A Generative CAPP System for Tube Hydro Forming

Tube hydro forming (THF) is a relatively complex manufacturing process. The performance of the process depends on many factors such as part geometry and design, material and process and the boundary condition of forming. Studying the behaviour of the process is by Finite Element-Computer Aided Engineering process has now become very popular and therefore, X- and T-branch shaped tubes have been taken to study the forming behaviour under THF coupled thermal and updated forming formulation through explicit solver as DEFORM using Computer Aided Engineering procedure. The simulation results have been compared with the experimental results and it agrees well. It is noted that the developed branch height and the wall thickness distribution along different planes of X and T are in good agreement with the experimental results. Based on several parametric studies and the simulation result, a Generative Process Planning system for THF has been proposed and validated with examples.

Simulated verification of NC part programs from 2D images

This paper describes a new approach to extract information from the 2D-drawing image files to determine turning features existing on rotational parts to be machined on turning centres; and later utilise this information to generate process plans and numerical control (NC) part programs. The software package is developed using ‘Java’ language incorporating the advanced features of Java Advanced Image (JAI) package. With the developed software package, one can achieve full automation of all aspects of the process plan, including sequence of operations, selection of tools, machining parameter selection and NC part program generation, etc. Thus, it becomes possible to go from image to NC part program in a fully automated fashion. This package is menu-driven and needs no programming skills to prepare NC part program for machining the parts. The paper includes an illustration of the procedure for extracting turned features, generating process plans and NC part programs from the image. The results are verified through CNC train simulation package and are satisfactory.

Automated generation of NC part programs for turned parts based on 2-D drawing image files

This paper describes a novel method for automated generation of process plans and numerical control (NC) part programs from 2D drawing image files. Four algorithms are introduced in this paper. First algorithm deals with extraction of geometrical data, second algorithm used to recognise turned features from the extracted geometrical data base. Recognised features are then passed to third and fourth algorithms for generation of process plans and NC part programs respectively. Using proposed system, it is possible to achieve fully automation of recognition of turned features, generation of process plans and NC part programs. Thus, it becomes possible to go from scanned image to finished product in a fully automated fashion. These algorithms have been implemented on mini-computer to process image data, and display recognised turned features, process plans and creates output files containing NC part programs. These NC part programs are verified through CNC train simulation package. The results are satisfactory.

SU‐C‐BRC‐05: RT Analytics: Prior‐Knowledge Treatment Planning Using Image‐ Based Database Mining

Purpose: The purpose of this work was to create prospective treatment plans based on anatomical feature matching to previously treated cases from the institution database. This tool recommends ideal treatment plans to clinicians and provides quality control (QC) of the plan generation process to ensure best practices based on past planning experiences. Methods: An image‐based and feature‐selection search engine (RT‐Analytics) was implemented that explores previously treated cases of similar anatomy, returning a list of suggested plan configurations and corresponding DVHs. The search is based on a multi‐modality image registration to match isocenters and with the dose matrix used as mask to focus the registration on the tumor and surrounding tissue only. Plans ordered by anatomical similarity, are presented to the clinician and are exported to the treatment planning system to be used as a starting point in the treatment planning process.Results: RT‐Analytics is demonstrated on ten clinical cases of breast carcinomas. Using an institutional database of 317 previously treated cases, the search procedure finds anatomical matches at a value of 0.9 or higher as measured by a mutual information metric computed over 30 histogram bins. Without re‐optimizing the treatment plan, dose homogeneity in the breast was within 12%. Minor changes needed to further decrease this inhomogeneity, and the previous optimization constraints and attained DVH can be used as a starting solution in the optimization process. Suggested solutions are also used for quality assurance purposes, to investigate if the attained DVH on new cases meet the quality of previous cases using a population‐based plan assessment Conclusions: RT‐Analytics represents a unique approach to plan generation by using prior planning solutions, and it allows quality control of the plan development process.

Dimensional metrology interoperability and standardization in manufacturing systems

Dimensional metrology is an important part of any manufacturing system. It consists of distinct components and requires a large, diverse, and interconnected knowledge base. How to pass information seamlessly with minimal cost and minimal data loss between different components of a dimensional metrology system is a major issue that concerns software and hardware vendors, standards developers, and customers. This paper focuses on the four main elements of a dimensional metrology system: product definition, measurement process plan definition, measurement process execution, and analysis and reporting of quality data. The activities and software modules that are involved in these elements are discussed. Key issues that cause interoperability problems are identified. These issues are discussed as they relate to the current situation in dimensional metrology standards development. The STEP (ISO 10303) standards are the product of an international effort to achieve interoperability for manufacturing systems. Extending STEP is an appropriate way to solve the interoperability problem within dimensional metrology systems. Further development of STEP standards is proposed so that Geometric Dimensioning and Tolerancing (GD&T) information already available in STEP can be linked with manufacturing feature information, measurement technology, and measurement results. The proposed STEP data model is an attempt to provide a standard that will support automatic measurement process plan generation for in-process on-machine measurement. Some case studies are under way to test the model.

Experience-Based Quality Control of Clinical Intensity-Modulated Radiotherapy Planning

To incorporate a quality control tool, according to previous planning experience and patient-specific anatomic information, into the intensity-modulated radiotherapy (IMRT) plan generation process and to determine whether the tool improved treatment plan quality. A retrospective study of 42 IMRT plans demonstrated a correlation between the fraction of organs at risk (OARs) overlapping the planning target volume and the mean dose. This yielded a model, predicted dose = prescription dose (0.2 + 0.8 [1 - exp(-3 overlapping planning target volume/volume of OAR)]), that predicted the achievable mean doses according to the planning target volume overlap/volume of OAR and the prescription dose. The model was incorporated into the planning process by way of a user-executable script that reported the predicted dose for any OAR. The script was introduced to clinicians engaged in IMRT planning and deployed thereafter. The script's effect was evaluated by tracking δ = (mean dose-predicted dose)/predicted dose, the fraction by which the mean dose exceeded the model. All OARs under investigation (rectum and bladder in prostate cancer; parotid glands, esophagus, and larynx in head-and-neck cancer) exhibited both smaller δ and reduced variability after script implementation. These effects were substantial for the parotid glands, for which the previous δ = 0.28 ± 0.24 was reduced to δ = 0.13 ± 0.10. The clinical relevance was most evident in the subset of cases in which the parotid glands were potentially salvageable (predicted dose <30 Gy). Before script implementation, an average of 30.1 Gy was delivered to the salvageable cases, with an average predicted dose of 20.3 Gy. After implementation, an average of 18.7 Gy was delivered to salvageable cases, with an average predicted dose of 17.2 Gy. In the prostate cases, the rectum model excess was reduced from δ = 0.28 ± 0.20 to δ = 0.07 ± 0.15. On surveying dosimetrists at the end of the study, most reported that the script both improved their IMRT planning (8 of 10) and increased their efficiency (6 of 10). This tool proved successful in increasing normal tissue sparing and reducing interclinician variability, providing effective quality control of the IMRT plan development process.

Enhanced product design for generative process planning

AbstractProduct design and process planning are important stages of the product lifecycle because they mainly determine the success or failure of a new product. Process planning, which is linking design and production phase, causes most of the resulting production costs. Gashed application landscapes, heterogenic systems and consequently different inconsistent product data models are preventing an efficient product development especially across different stages of the product lifecycle (Sharma and Gao (2006)). This results in increased error-proneness, higher development effort, misinterpretations and miscalculations. For generative process planning e.g. for an exact quotation generation and continuously initializing the production after procurement, we utilize a completely computer-interpretable design model for products based on machining parts. Therefore, the design model provides semantics about the geometric information which is necessary for reasoning required manufacturing operations for automatic process planning. Furthermore, the design model includes complete product information which can be reused and extended during later product lifecycle stages. For continuous system integration and consistent product data representation we are utilizing the ISO standard 10303. Based on these aspects, we present our approach for generating process plans directly from a given product description by retrieving and evaluating various process plans while considering the entire manufacturing environment.

An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system

With burgeoning global markets and increasing customer demand, it is imperative for companies to respond quickly and cost effectively to be present and to take the lead among the competitors. Overall, this requires a changeable structure of the organization to cater to a wide product variety. It can be attained through adoption of the concept of reconfigurable manufacturing system (RMS) that comprises of reconfigurable machines, controllers and software support systems. In this paper, we propose a new approach to generate the dynamic process plan for reconfigurable manufacturing system. Initially, the requirements of the parts/products are assessed which are then compared with the functionality offered by machines comprising manufacturing system. If the production is feasible an optimal process plan is generated, otherwise the system shows an error message showing lack of functionality. Using an adapted NSGA-2 algorithm, a multi-objective scenario is considered with the aim of reducing the manufacturing cost and time. With the help of a numerical example, the efficacy of the proposed approach is demonstrated.

Design of a STEP compliant system for turning operations

The changing economic climate has made global manufacturing a growing necessity over the last decade, forcing companies from East and West to collaborate beyond geographic boundaries in the design, manufacture and assembly of products. The ISO 10303 and ISO 14649 Standards (STEP and STEP-NC) have been developed to introduce interoperability into manufacturing enterprises so as to meet the challenges of responding to production on demand. This paper describes and illustrates a STEP compliant CAD/CAPP/CAM System for the manufacture of rotational parts on CNC turning centers. The information models to support the proposed system together with the data models defined in the ISO 14649 standard used to create the NC programs are also described. A structured view of a STEP compliant CAD/CAPP/CAM system framework supporting the next generation of intelligent CNC controllers for turn/mill component manufacture is provided. Finally a proposed computational environment for a STEP-NC compliant system for turning operations (SCSTO) is described. SCSTO is the experimental aspect of the research and is supported by information models that and have been constructed using a structured methodology and object-oriented methods. SCSTO was developed to generate a Part 21 file based on machining features to support the interactive generation of process plans utilizing feature extraction. A case study component has been developed to prove the concept of using the milling and turning parts of ISO 14649 to provide a turn-mill CAD/CAPP/CAM environment.

SU‐GG‐T‐202: Automation of Patient Specific IMRT QA Plan Generation

Purpose: An effort to automate the planning process for measurement based patient specific intensity modulated radiation therapy (IMRT) quality assurance (QA). Method: In our clinic before an IMRT plan is delivered, we perform measurement based QA using ionization chambers, MapCheck diode array and MatriXX chamber array. The patient plan is copied onto the phantom, dose is computed and ionization chamber points are picked or the planar dose planes are exported. This process is resource intensive and would require a physicist to spend time on generating the QA plan. To automate this process of QA plan generation we have implemented a set of software tools on the Pinnacle treatment planning system (TPS). The tools are implemented using Pinnacle scripting combined with other programming languages including Perl and C/C++. The scripts automatically operate the Pinnacle TPS to create the QA plans, export phantom dose data and generate all necessary documents and reports. Results and Conclusion: The developed scripts for QA plan generation have been used in clinic for months. They have aided in speeding up the plan generation process and reducing measurement failure/repetition rate. Overall, these tools are useful to make the daily QA plan generation on Pinnacle automatic, efficient and less repetitive.

Generation of Optimal Process Plan Alternatives for Manufacturing Mechanical Components

The focus of this paper is on computer-aided process planning (CAPP) for parts manufacture in systems of definite processing capabilities, involving multi-axis machining centers. A methodical approach is developed to optimally solve for process planning problems, which consists in the identification of process alternatives and sequencing working steps. The approach involves the use of the branch and bound (B&amp;B) concept from the field of artificial intelligence (AI). A conceptual scheme for generation of alternative process plans in the form of a network is developed, based on part design data modeling in terms of machining features. A relevant algorithm is proposed for creating such a network and searching for the optimal process plan solution from the viewpoint of its operational performance, under formulated process constraints. The use of the approach is studied numerically with regard to a real life case study and diverse machine tools with relevant tooling are considered. Generated process alternatives for complex machining with given systems are studied using models programmed in the Matlab environment.

TU‐C‐BRA‐02: Radiotherapy Plan Integrity Verification with Dynamic Pinnacle Scripts

Purposes: The purpose of this study was to automate elements of the quality control (QC) necessary for IMRT plan generation process. Physicists often review all dosimetrist‐generated IMRT plans prior to physician approval with the aim of detecting sub‐optimal or technically undeliverable plans. We report here an automated suite of QC tools designed to carry out a majority of these laborious physics verification tasks efficiently and accurately within the Pinnacle treatment planning system (TPS). Method: The IMRT plan integrity verification tool is implemented as a combination of Pinnacle scripting routines and Perl programs which collect/derive the plan data, compare the data against pre‐defined physical and dosimetric rules, and report the results of the logical comparisons to the user. Important technical details include (a) a unique Perl program library which has been created to allow easier processing of Pinnacle data and (b) a dynamic script generation method which was developed to allow features and flexibilities of Perl programming for plan data processing inside Pinnacle. This QC tool is able to check many rules in the categories of patient setup, contouring, beams, dose calculation, IMRT optimization and dose distribution. Results and conclusion: This tool has been in routine clinical use for several months, and initial results have shown that it improves both the efficiency and accuracy of physicists and dosimetrists, virtually eliminating some errors commonly encountered prior to the tools introduction. While this technique cannot be extended to all elements of the IMRT QC process, it has been adapted to include elements such as IMRT objective conflict analysis and IMRT cost function ranking which would be time‐consuming, if not impossible, for a human to manually verify. Further, the dynamic Pinnacle script method which facilitated the IMRT QC could also be expanded to other radiotherapy QC including 2D, 3D conformal, and brachytherapy planning.

DETERMINATION OF MINIMUM LIMITING AXIAL DEPTH OF CUT FOR 2½D POCKET MACHINING BASED ON RECEPTANCE COUPLING

A minimum limiting axial depth of cut guarantees stable machining for any cutting speed and is selected as the height of the z-level of cut used for 2D pocket machining. It is determined based on the frequency response function (FRF) and receptance coupling substructure analysis (RCSA) to minimize experimentation. For process planning considerations, long cutters are more preferable for deep pockets to reduce the number of z-levels of cut. However, the higher cutter flexibility results in bouncing effect during impact testing. To overcome this problem, a string test is proposed in which the cutter tip is deflected by a string and receptance measured for free vibration. In this work, the FRF at the tip of cutters with different geometries was measured with both the tests to study the effect of geometry on receptance. The prediction of experimentally calibrated RCSA models shows good agreement with the measured receptance. The results also suggest the suitability of string test for flexible cutters. Finally, a reference plot of the variation of limiting axial depth with cutting geometry and conditions is generated based on the predicted receptance. The plot provides a priori determination of the stable limiting axial depth of cut which facilitates the automatic generation of process plans for 2D pocket machining.

Designing function blocks for distributed process planning and adaptive control

The objective of this research is to develop methodologies and a framework for distributed process planning and adaptive control using function blocks. Facilitated by a real-time monitoring system, the proposed methodologies can be applied to integrate with functions of dynamic scheduling in a distributed environment. A function block-enabled process planning approach is proposed to handle dynamic changes during process plan generation and execution. This paper focuses mainly on distributed process planning, particularly on the development of a function block designer that can encapsulate generic process plans into function blocks for runtime execution. As function blocks can sense environmental changes on a shop floor, it is expected that a so-generated process plan can adapt itself to the shop floor environment with dynamically optimized solutions for plan execution and process monitoring.

Recognition of Machining Features and Optimum Process Plan Generation using Genetic Algorithms

Recognition of Machining Features and Optimum Process Plan Generation using Genetic Algorithms