A review of function blocks for process planning and control of manufacturing equipment

Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch sizes, requiring real-time monitoring for dynamic distributed decision making, and adaptive control capabilities that are able to handle, in a responsive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality. In many manufacturing systems, traditional methods, based on offline processing performed in advance, are used. These methods are not up to the standard of handling uncertainty, in the dynamically changing environment of these manufacturing systems. Using real-time manufacturing intelligence and information to perform at a maximum level, with a minimum of unscheduled downtime, would be a more effective approach to handling the negative performance impacts of uncertainty. The objective of our research is to develop methodologies for distributed, adaptive and dynamic process planning as well as machine monitoring and control for machining and assembly operations, using event-driven function blocks. The implementation of this technology is expected to increase productivity, as well as flexibility and responsiveness in a job-shop environment. This paper, in particular, presents the current status in this field and a comprehensive overview of our research work on function block-enabled process planning and execution control of manufacturing equipment.     

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.     

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

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.     

An adaptive process planning approach of rapid prototyping and manufacturing

This paper presents an adaptive approach to improve the process planning of Rapid Prototyping/Manufacturing (RP/M) for complex product models such as biomedical models. Non-Uniform Rational B-Spline (NURBS)-based curves were introduced to represent the boundary contours of the sliced layers in RP/M to maintain the geometrical accuracy of the original models. A mixed tool-path generation algorithm was then developed to generate contour tool-paths along the boundary and offset curves of each sliced layer to preserve geometrical accuracy, and zigzag tool-paths for the internal area of the layer to simplify computing processes and speed up fabrication. In addition, based on the developed build time and geometrical accuracy analysis models, adaptive algorithms were designed to generate an adaptive speed of the RP/M nozzle/print head for the contour tool-paths to address the geometrical characteristics of each layer, and to identify the best slope degree of the zigzag tool-paths towards achieving the minimum build time. Five case studies of complex biomedical models were used to verify and demonstrate the improved performance of the approach in terms of processing effectiveness and geometrical accuracy.     

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.     

A machining process planning activity model for systems integration

A key issue of integrating process planning systems with design systems and production planning systems is how to overcome barriers in data exchange and sharing amongst software systems. A machining process planning activity model was developed to address some of the barriers. This model represents functional components and data requirements in process planning systems. The purpose of the model is to create the context in which data requirements and data flow for numerically controlled machining process planning are defined. Furthermore, the model was developed as a unification of many previously developed process planning activity models.     

A hybrid approach for dynamic routing planning in an automated assembly shop

Highly turbulent environment of dynamic job-shop operations affects shop floor layout as well as manufacturing operations. Due to the dynamic nature of layout changes, essential requirements such as adaptability and responsiveness to the changes need to be considered in addition to the cost issues of material handling and machine relocation when reconfiguring a shop floor’s layout. Here, based on the source of uncertainty, the shop floor layout problem is split into two sub-problems and dealt with by two modules: re-layout and find-route. GA is used where changes cause the entire shop re-layout, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. This paper reports the latest development to the authors’ previous work.     

PROCASE: a case-based process planning system for machining of rotational parts

This paper introduces a case-based process planning system PROCASE which generates new process routines through learning from existing process routines. In contrast to traditional rule-based systems, the process planning knowledge of the PROCASE is represented in terms of cases instead of production rules. The planning basically comprises case retrieving and case adaptation rather than chaining applicable rules together to form process plans. The advantages are, first, the system is cheaper to build as it saves the expense of knowledge acquisition. Second, the system is able to advance its knowledge automatically through planning practice. Third, it is robust, because the reasoning is not based on pattern matching but similarity comparison. PROCASE has three modules: the retriever, the adapter and the simulator. It is supported by a feature-based representation scheme which naturally serves as the case indices for case retrieving and adaptation. The retriever uses a similarity metric to retrieve an old case which is the most similar case, among all old ones, to the new case. The adapter is then activated to adapt the process plan of the retrieved case to fit the needs for the new case. The simulator is used to verify the feasibility of the adapted plan. PROCASE is implemented on a Silicon Graphics IRIS workstation using C++ . An example is given to demonstrate how the process routine is generated by the system proposed by the authors.     

A novel energy demand modelling approach for CNC machining based on function blocks

Energy efficiency remains one of the major issues in the machining domain. Today's machining systems are confronted with a number of new challenges, such as turbulent product demand and variations in production resources. Rapid and flexible energy modelling in a distributed and collaborative machining environment emerges as a new research area. Energy demand models in such an environment need to be practical, accurate, effective, scalable and reusable. Energy analysis and optimisation cannot be carried out once for all at the beginning. Instead, it is an on-going process. In this paper, the function block technique, i.e. IEC 61499, is used for the development of energy demand models as it brings advantages such as modularity, encapsulation, extensibility and reusability. A brief review on energy modelling and research on function blocks are given in the first part. A novel energy demand modelling approach based on function blocks is then proposed and elaborated. Three types of function blocks have been developed, i.e. machine tool dependent function blocks, state transition function blocks, and service interface function blocks. The first type, as the fundamental building blocks, is divided into two sub-types, machine component function block and machining state function block. Two case studies, based on a small 3-axis milling machine and an industrial production line respectively, are presented to demonstrate the possible applications using the function block-based model. Comprehensive discussions are given thereafter, including a pilot application of a distributed process planning system and a unique energy evaluation scheme. A confidence level associated energy rating system is proposed as the first step to turn energy consumption figures into useful indicators. The energy demand model based on function blocks developed here enhances the energy modelling and their practical implementations.     

A model for integrating process planning and production planning and control in machining processes

The goal of process planning is to propose the routing of a previously designed part and results in a sequence of operations and their parameters. It concerns and requires detailed information about the process. The goal of production planning, on the other hand, is to schedule, sequence and launch the orders introduced on the routing sheet into the job-shop according to the enterprise's strategic goal and the actual conditions of the production plant. The goals, information and decisions taken in process planning and production planning and control are often very different and, because of that, it is very difficult to integrate them.

The objective of this work is to develop a model that can be applied in the future to the development of an integrated process planning and scheduling tool using an integrated definition (IDEF) methodology to design an activity model, which integrates process and production planning in metal removal processes. An activity model will be used to develop a system that allows the user to plan the process and the production at the same time in collaborative engineering work. To design the activity model, a wide range of parts were evaluated and processed in an actual job-shop factory. Several activities were developed in detail to be tested in real cases, and an example of one of them is introduced in this article.     

Modelling for process planning: the links between process planning entities

In an integrated design framework, each actor of the design process must have his own view of the product to efficiently participate and co-design. The product model used by the process planner is presented here: it is in fact extracted from the global product model of the whole design system by filtering. The links of dependence among the different entities of the model – links of availability, accessibility and quality – are particularly emphasised in this text. Their efficiency has been tested in process planning and product designing.     

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.     

Modeling of process parameter selection with mathematical logic for process planning

Process planning is the systematic determination of detailed methods by which workpieces or parts can be manufactured economically and competitively from initial stages to finished stages. One of the key problems of computer-aided process planning (CAPP), however, is the complexity of process knowledge representation of process planning and the diversity of manufacturing background. Process knowledge representation and inference mechanism of process parameter selection is one of the most important issues in the research on CAPP. A proper methodology for modeling inference mechanism of process parameter selection, hence, is essential for selection of process parameters in process planning. The paper presents an atomic inference engine model of process parameter selection in process planning using mathematical logic. The methodology of modeling the inference mechanism of process parameter selection is proposed with backward chaining of mathematical logic that is a form of goal-directed reasoning. An illustrative case has been analyzed using the proposed approach to demonstrate its potential application in the real manufacturing environment, by combining with a practical application of a hole-making in a industrially relevant workpiece. The outcomes of this work provide a process reasoning mechanism for process parameter selection in process planning and thus alleviate automated process reasoning problems in process planning.     

An active learning genetic algorithm for integrated process planning and scheduling

In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.     

Efficient feature-based process planning for sculptured pocket machining

Feature is recently known as the core concept necessary to realize a fully integrated CAD/CAM system. The information contents in a feature can be easily conveyed from one application to another in the manufacturing domain. However the feature generated in one application may not be suitable for another without being modified with more information. The objective of the paper is to present the methodology of decomposing bulky features of the sculptured shape of pocket to be removed into compact features to be efficiently machined. It is possible to reactively and efficiently machine the sculptured shape of pocket by segmenting horizontally and vertically a bulky feature and by applying variable cutting condition to each feature.     

Serial 3D model reconstruction for machining evolution of rotational parts by merging semantic and graphic process planning information

The manufacturing of a mechanical part is a dynamic evolution process from a raw workpiece to the final part, in which the generation of serial 3D models reflecting the changes on geometric shapes is especially critical to digital manufacturing. In this paper, an approach driven by the process planning course, the machining semantics and the machining geometry to reconstruct incrementally the serial 3D models for rotational part’s dynamic evolution is proposed. The two major techniques involved are: (1) extraction of machining semantics based on process planning language understanding; (2) 3D reconstruction from 2D procedure working drawings guided by machining semantics and visualization for the reconstructed series of 3D models. Compared with the conventional 3D reconstruction methods, this approach introduced the process planning course and relevant information to implement a dynamic, incremental and knowledge-based reconstruction which can greatly reduce the efforts in reconstruction and extend the collection of geometric shapes to be reconstructed.     

A Web-based process planning optimization system for distributed design

In this paper, a process planning module, which can optimize the selection of machining resources, determination of set-up plans and sequencing of machining operations to achieve optimized process plans, has been wrapped as services and deployed in the Internet to support distributed design and manufacturing analysis. The module includes four intelligent approaches, and a Tabu search-based approach is explained in this paper to illustrate the optimization process. A Web-based prototype system has been setup for users to carry out visualization-based manipulations and process planning of design models by invoking the services remotely. The Web-based system has been integrated with a distributed feature-based design system, and the latter can generate design models and re-represent them in an XML representation based on VRML and attributes of features to provide the input of the former. Through effective utilization of the Web and Java technologies, this system is independent of the operating system, scalable and service-oriented, and can be used by a geographically distributed design team to organize concurrent engineering design activities effectively.     

Electromagnetism-like algorithms for optimized tool path planning in 5-axis flank machining

Optimization of tool path planning using metaheuristic algorithms such as ant colony systems (ACS) and particle swarm optimization (PSO) provides a feasible approach to reduce geometrical machining errors in 5-axis flank machining of ruled surfaces. The optimal solutions of these algorithms exhibit an unsatisfactory quality in a high-dimensional search space. In this study, various algorithms derived from the electromagnetism-like mechanism (EM) were applied. The test results of representative surfaces showed that all EM-based methods yield more effective optimal solutions than does PSO, despite a longer search time. A new EM-MSS (electromagnetism-like mechanism with move solution screening) algorithm produces the most favorable results by ensuring the continuous improvement of new searches. Incorporating an SPSA (simultaneous perturbation stochastic approximation) technique further improves the search results with effective initial solutions. This work enhances the practical values of tool path planning by providing a satisfactory machining quality.     

Integration of process planning and scheduling functions

Automated process planning (APP) has received a considerable amount of attention in recent years. This has mainly been due to APP allowing for the bridging of the gap between design and manufacturing, and for its automation of the related tedious human task. While there have been several efforts in developing interfaces between computer-aided design (CAD) and APP, little attention has been given to the integration of APP and scheduling, which is likely to be of even higher importance. This paper addresses the basic issues involved in the integration of the two functions, demonstrates a methodology and the potential benefits of the integrated approach by an example, and presents directions for future research.