Advanced CNC system with in-process feed-rate optimisation
Highlights
► Implementation of STEP-NC enabled Machine Condition Monitoring (MCM). ► optiSTEP-NC performs initial feed-rate optimisation based on STEP-NC data. ► AECopt acts as a connector between the process planner and machining environment. ► KBE based-MTConnect is responsible for obtaining machining know-how. ► Optimisation is performed before, during or after machining operations.
Introduction
Over the years, computer numerical control (CNC) machine tools have been developed, with the ability to machine high-precision products. One of the technologies applied in support of CNC development is by incorporating machine condition monitoring (MCM). In doing so, machine tools are supervised by means of sensing elements, signal conditioning devices, signal processing algorithms and signal interpretation. For real-time supervision of a CNC machine, various intelligent functions such as adaptive control, re-generation of optimised data sets and advanced optimisation models have been developed and implemented. In this way, various machining process anomalies can be detected at an early stage, assuring a safer machining environment.
Utilisation of MCM for machine tools reduces the need for human intervention during machining and allowed automatic supervision of the machine tool. However, challenges still exist in coping with frequent design revisions, stringent demand on product quality and shorter times to market. Moreover, machining activities have been customer-centric rather than manufacturer-driven. To enable active quality control during machining, machining parameters are best monitored and controlled, so that machine tool behaviour is analysed in time and appropriate actions taken in due course. The main concern of monitoring and control of on-going processes is to record relevant sensory data so that machine tool characteristics can be understood and fed back for a real-time reaction. For example, under the machining domain, maintaining optimal machining parameters to avoid over-loading of a spindle, excessive cutting force, chatter, tool wear and other constraints needs the proper combination of appropriate machining parameters and on-going performance of machine tools. Understanding these characteristics demands a trade-off between precise empirical models and systematic control of machine tools.
To cope with this, MCM can be automated by integrating monitoring technology with decision-making procedures. The aim is to produce self-adjusting intelligent systems that are capable of adapting to the ever-changing machining environment [1]. In addition, as the technology grows, there are demands and new prospects to empower current CNC with more advanced features such as adaptability, agility, reconfigurability and interoperability [2]. In realising an agile and autonomous manufacturing environment, open CNC architecture is also envisaged [3]. There are multiple impediments in realising this vision. First, in spite of great technological achievements, contemporary CNC programmes are still being executed based on a sequential set of NC programming language, aka G-codes. These codes were developed more than 50 years ago with little, if any, intelligence. The initial design of the codes was to hold a set of low-level data that are mostly step-by-step instructions to drive the earliest models of machine tools. Outdated yet still widely used, G-codes only hold a subset of information, which becomes an obstacle to achieving a complete, intelligent and optimised machining environment. For instance, although various work has been devoted to enhancing optimisation models, a range of features cannot be utilised and incorporated within the codes.
Second, only limited control of the programme execution is allowed during machining, which makes it difficult to change the programme on the shop-floor. Last minute changes are not permitted. The machining operations are fully dominated by the predetermined NC codes and in most cases, the machine tools are not able to change any cutting conditions and machining sequences during machining operations. In addition, since it only supports one-way information flow from design to manufacturing, any drastic changes to the manufacturing process cannot be readily preserved and directly fed back to the designer [4].
Finally, information flow in G-codes was designed unidirectionally, i.e., from CAD to the shop-floor, and does not enable feedback of know-how from the shop-floor to the designer [5]. As a result, this conventional way of NC programming is considered a bottleneck for achieving an intelligent machining environment.
The ISO TC 184/SC 1/WG7 envisions a gradual evolution from ISO 6983 to portable feature-based programming formally known as ISO 14649. TC 184 is the technical committee for “Industrial automation systems and integration” [6]. Its scope is standardisation in the field of industrial automation and integration concerning discrete part manufacturing and encompassing the application of multiple technologies, that is information systems, machines and equipment, and telecommunications. ISO 14649, also known as STEP-NC (Standard for the Exchange of Product data for Numerical Control), provides an opportunity to overcome the abovementioned obstacles especially in realising intelligent machining operations. The main characteristic of STEP-NC is its high-level and object-oriented data structure. Unlike G-code where a part program is written to describe simple tool movements and functions, the STEP-NC interface is able to work with rich information such as manufacturing features, multiple operations such as finishing and roughing processes, machine tool capability, motor drive power, mechanical efficiency, machining strategy, cutting tool information and workpiece properties. Since STEP-NC data model describes rich information, quality knowledge and data can be utilised on the shop-floor, which enables advanced optimisation analysis to be conducted. Modifications on the shop-floor are possible and machining know-how can be preserved for designers and process planners, thus improving the communication link between design and manufacturing departments. By providing a complete and structured data model, no information is lost. Post-processors for machine-specific adaptations of NC programs are no longer needed. In addition, this rich information content results in higher flexibility enabling last-minute changes or the correction of technological values within the part program. This research has a focus on adaptive control for intelligent machining in a universal and interoperable manner. Adaptive execution of STEP-NC data and feed-rate optimisation has been realised.
Section snippets
STEP-NC enabled MCM framework
G-codes deprive machining processes of much needed information such as workpiece characteristics, tool properties and optimised cutting parameters that is often provided by experienced operators. The functional requirements of the developed STEP-NC enabled MCM system include (i) an offline optimisation module, (ii) a data model in support of process optimisation, and (iii) process monitoring and control. These functional requirements are explained below.
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Offline optimisation—preliminary
Development of the system
The system architecture has been developed to support an intelligent, interoperable, informative and innovative manufacturing platform. Under the machining domain, it is widely known that over-loading of a spindle, excessive cutting force, chatter, tool wear and other constraints may lead to major problems such as tool breakage, product quality deterioration and even worse machine breakdown. As such, continuous monitoring of machine behaviour, real-time optimisation and the systematic retention
Conclusions
Use of the STEP-NC data model provides a promising platform for various applications consolidated under the same data structure. It brings design data such as geometry, tolerances and materials into process control and monitoring of machining operations, allowing a robust control mechanism. Motivated by this benefit, the newly developed EXPRESS schema for optimisation purposes augments the existing STEP-NC data models. This is necessary for an integrated environment in which high-level machine
Acknowledgements
This research is supported by the Directorate General of Higher Education (DGHE) Department of National Education of Indonesia under scholarship contract No:1840-D4.4-2008. The authors wish to thank Johannes Nittinger for his contribution in programming and setting up MTConnect.
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