The aim of this paper is to introduce a novel methodology, based on a finite element (FE) computation engine for validating of real-time identification schemes applied in machining. FE modelling of the milling process has the purpose of being accountable for a thorough validation of the parametric identification approach, and of providing a good physical insight into the phenomena investigated. The system considered here has a lower number of degree-of-freedoms which permits a thorough analysis. However, when taking into account the system’s nonlinear and time-varying nature, it is clear that the results are far from being trivial. Therefore, the analysis of the milling process, taking into account nonlinearities restricting the growth of response amplitudes in the case of chatter-type instability, provides some intrinsic information of the basic features on the system that might be of both fundamental interest and practical use.

The aim of this paper is to introduce a novel methodology, based on a finite element (FE) computation engine for simulation of process machine interaction occurring in machining systems. FE modelling of the milling process has the purpose of being accountable for a thorough validation of the parametric identification approach, and of providing a good physical insight into the phenomena investigated. The system considered here has a lower number of degree-of-freedoms which permits a thorough analysis. However, when taking into account the system’s nonlinear and time-varying nature, it is apparent that the results are far from being trivial. Therefore, the analysis of the milling process, taking into account nonlinearities restricting the growth of response amplitudes in the case of chatter-type instability, provides some intrinsic information of the basic features on the system that might be of both fundamental interest and practical use.

This paper focuses on a digital twin of a cutting tool as a digital replica of a physical tool, its data format and structure, information flows and data management, as well as possibilities for further applications and analysis of productivity. Data are collected throughout the production lifecycle in an accurate way, using the international standard ISO 13399 and messaging based on the previously developed event-driven line information system architecture (LISA) with IoT functionality. The digital twin is tweeted to be stored, refined and propagated to the process planning for an optimized machining solution.

Machining vibrations is a critical phenomenon in the industry as they negatively affect the quality and tool-life. One common avoidance strategy for machining vibrations is the fine-tuning of process parameters, thus leading to longer production time. Our research addresses this challenge and uses different streams of data to classify problematic processes. Data streams of machining parameters, tool position, loads, vibration sensors, together with process plan data and cutting tool usage information, are visualized. Experiments are performed to derive classification criteria. These results are then used to observe vibrations in a five-axis machining center for further process adjustment.

Considerable effort is usually devoted by companies to ensure the competitiveness of their manufacturing systems. This makes continuous improvement a central topic in production management activities. Less attention is given to the methods that drive improvement and to the way actions are defined and selected. In the literature, analytical models and simulation are the most widely used tools for manufacturing systems' performance evaluation and improvement. In practice, simple approaches, mainly based on experience, seems to be the best compromise to face this problem. In a collaboration project between Politecnico di Milano ( Milano, Italy), Kungliga Tekniska hogskolan ( Stockholm, Sweden) and Scania CV AB (Sodertalje, Sweden) within the European Network of Excellence VRL-KCiP we proposed a new methodology, based on analytical methods, to support the company in manufacturing system productivity improvement through re-configuration. The application of this approach to the Scania six-cylinder engine-block machining line enabled a remarkable increment in throughput by selecting analytically the most suitable improvement actions.

Manufacturing competitiveness frequently relies on company ability to rapidly reconfigure their assembly systems. This paper introduces assembly system proactivity, a concept based on interrelated levels of automation, human competence, and information handling. Increased and structured human involvement contributes to increased system ability to proactively address predicted and unpredicted events. Correct involvement of human operators will utilize the combined potential of human and technical capabilities, providing cost-efficient assembly system solutions. The ProAct project is developing proactive assembly system models and evaluates proactive, feature-based solutions. Focus is on realising the potential of cost-efficient and semi-automated systems with relevant human involvement, i.e. highly skilled operators who add flexibility and functionality.

Manufacturing competitiveness is highly dependant on the companies’ability to rapidly reconfigure their assembly systems. Ongoing efforts to approachself-reconfiguring and “emerging” assembly systems are promising but can besuccessfully complemented by integration of highly flexible human operators intosuch system models. This paper introduces the concept of assembly systemproactivity. The approach is based on interrelated levels of human involvement,automation, and information handling within the assembly system. Presently,assembly system developers react to the companies’ demands, thus developingsolutions, which are direct responses to existing problems, i.e. a highlyreactive approach. Assembly systems need to be more dynamic and evolvable tokeep up with the reduced product life cycles and still be cost efficient. Consequently,assembly systems with the ability to proactively meet emergent and long-termfluctuations concerning product design and volume capacity are required. Suchsystems consist of technical components efficiently integrated with human operatorsto constitute reliable resources in the production system. That way, disturbances canbe minimized and the availability of the entire production system increased.

Manufacturing competitiveness relies on the companies' ability to rapidly reconfigure their assembly systems. This paper introduces assembly system proactivity, a concept based on interrelated levels of human involvement, automation, and information handling. Increased and structured human involvement contributes to increased system ability to proactively address predicted and unpredicted events. Correct involvement of human operators will utilize the fully combined potential of human and technical capabilities, providing cost-efficient assembly system solutions. The ProAct project is developing proactive assembly system models and evaluating proactive, feature-based solutions. Focus is on realizing the potential of cost-efficient and semi-automated systems with relevant human involvement, i.e. highly skilled operators that add flexibility and functionality.

Competitive assembly systems must cope with frequent demand changes, requiring drastically shortened resetting and ramp-up times. Characteristics of assembly systems capable of rapid change are e.g. Flexibility; Robustness, Agility, and ability to handle frequent changes and disturbances. This paper proposes proactivity as a vital factor of semi-automated assembly systems to increase speed of change. Proactive systems utilize the full potential of human operators and technical systems. Such systems have ability to dynamically change system automation levels, resulting in decrease of time consumed for assembly tasks. Proactivity criteria for assembly systems are reviewed based on theory and industrial case studies

Manufacturing competitiveness is highly dependant on companies' ability to rapidly reconfigure their manufacturing and assembly systems. Efforts to approach emerging and self-reconfigurable systems could be successfully complemented by efficient integration of highly flexible human operators into the system. The concept of system proactivity is introduced which is based on the interrelated levels of automation, information, and competence in the assembly system. An ongoing project to develop proactive assembly systems, ProAct, is described.

Increasing customisation of products results in decreasing production batch sizes, especially in the final assembly. Industry must therefore increase their capability to handle smaller batches as well as radically decrease set up time between different product groups and new products. This paper suggests the need for further development, primarily addressing time parameters in dynamically changing assembly systems. We propose proactivity as a vital characteristic of semi-automated assembly systems, to increase fulfilment of customer demands and decrease non value-adding tasks. In proactive assembly systems, the full and complementary potential of human operators and technical systems is utilised. Criteria for proactivity in assembly systems are reviewed from automation, information, and competence perspectives.

Increasing customisation of products results in decreasing production batch sizes, especially in the final assembly. Industry must therefore increase their capability to handle smaller batches as well as radically decrease set up time between different product groups and new products. This paper suggests the need for further development, primarily addressing time parameters in dynamically changing assembly systems. We propose proactivity as a vital characteristic of semi-automated assembly systems, to increase fulfilment of customer demands and decrease non value-adding tasks. In proactive assembly systems, the full and complementary potential of human operators and technical systems is utilised. Criteria for proactivity in assembly systems are reviewed from automation, information, and competence perspectives.

Machining description for a CNC machining process has the purpose to give information about the machining process and relations between, product, process and resources. Currently used information model requires time consuming and expensive procedures during the manufacturing preparation process to create this documentation. This paper presents how machining descriptions can be generated more efficient. We have studied how ISO 10303 standard for product data representation and exchange, known as STEP, can be utilized in the manufacturing preparation process. We present how information requirements for machining descriptions containing a relationship between a machined face, its tolerances and used cutting tool can be met. To demonstrate our research result we have developed a prototype system where STEP data models of the machiningprocess, workpiece with tolerances, fixture and cutting tools are used and integrated. We have proven that information separation of data, to create machining descriptions for CNC machining, is currently done unnecessarily early in the manufacturing preparation process.

Today’s CAD/CAM systems make it possible to manage features, dimensions and tolerances from design through process and operations planning in their internal proprietary data model. For manufacturing purposes information is then split into different documents for setup and machining operation besides NC code. A demonstrator has been developed based on a case study and the ISO 10303 standard as a coherent data model for representing relationships between manufacturing features, machined faces, tolerances and used cutting tools. When the information model is fully exploited, the fragmentation of data as well as manufacturing preparation time and administration cost are significantly reduced.

This thesis deals with the organization of an adaptive control system for turning and, furthermore, the development of an integration framework for subsystems in the adaptive control system. The thesis is organized in three main parts.

The first part of the thesis is devoted to a general discussion on adaptive control of machine tools and a presentation of the adaptive control plan that we are working on at our department. An opening discussion on economical and human aspects on automation leads to the conclusion that the need for optimizing machining costs becomes more obvious, as the time portion for machining increases due to reductions in non-productive time.

Optimization can be performed at various levels of a production system and an attempt to identify different levels is made. However, in this dissertation, I have focused my attention on the machine tool level and optimization by means of adaptive control. Since adaptive control can assume different shapes, one chapter is devoted to adaptive control definitions.

The development trends are highlighted in a state-of-the-art discussion. The market supply of adaptive control systems is limited to monitoring systems without any possibilities of feedback control except from emergency stop.

The most ambitious IMT/KTH adaptive control plan ends the first part.

The five subsystems of the IMT/KTH system are presented in the middle part .

Different monitoring, adaptive control constraint and adaptive control optimization functions are assigned to the different subsystems. Monitoring and control of machining operations is based on measurements of machining characteristics and the subsystems include a number of advanced sensor systems, which are described.

In the final part the integration framework is presented. The stochastic nature of the machining process calls for real-time control of machining conditions. The subsystems are designed to operate in a relatively independent manner. Consequently, computer technologies for real-time and distributed control have been employed to realize a flexible modular system architecture for the adaptive control system. The layout of the system as well as the adaptive control strategy are presented. It is stated that the presented adaptive control system with its present adaptive control strategy constitutes a development platform, well-prepared for extensions based on future research results.

The future capital intensive CIM and FMS systems will demand adaptive controlled (AC) machine tools. At the Department of Production Engineering of the Royal Institute of Technology in Stockholm (IMT/KTH) we are continuing the development of an advanced AC turning center. Our approach is to design and use sophisticated sensor systems to measure several features both on-line and off-line in order to obtain sufficient information on the cutting process and make adaptive feedback feasible. The AC system operates at three different levels: advanced process monitoring adaptive control constraint (ACC) adaptive control optimization (ACO). In this paper we give an overview of practical progress and improvements that have been achieved since our contribution to MSTF '87 in Cambridge.1 This includes: a new flexible sensor installation for optical tool wear measurements on-line tool wear estimation based upon a dynamic force sensor applied time series analysis for on-line chatter control real-time control of maching conditions with respect to cutting forces distributed real-time computer system solution.

Resource efficiency has become a topic of greater importance due to dramatic increase of world-population and globalization. A company that is not able to efficiently utilize its resources is less sustainable and competitive. This paper introduces an assessment approach as a tracking and analyzing system to facilitate improvement of resource efficiency particularly for SMEs. The approach supports common economic KPIs of producing companies using resource-based measures. Two main elements are used. One for tracking resource efficiency measures (REC) and one for analyzing efficiency losses (REF). A tool has been designed to support the application and interpretation of the system elements.

Adaptive Control (AC) of machine tools requires many kinds of measured input data. The more information about the complex metal cutting process that can be obtained, the better the process can be controlled.

The paper describes an Adaptive Control Optimization (ACO) system for turning operations. The system continuously chooses Optimal Cutting Data (OCD), taking into account both economical criteria and technical limitations.

The system operates at three different levels:

• • Advanced Process Monitoring

• • Adaptive Control Constraint (ACC)

• • Adaptive Control Optimization (ACO).

Two commercial monitoring systems perform process monitoring. In addition, five independent measurement systems have been developed.

A dedicated vision system has been installed in the lathe to measure the tool flank wear between cuts. The flank wear data are utilized to predict the tool life. Based upon these predictions economical optimum cutting data can be calculated at the ACO level.

To obtain in-process real-time control of the metal cutting process the cutting forces are measured during machining. The forces are measured with conventional piezoelectric force transducers which are located between the turret housing and the cross-slide. The measured force signals are processed by a dedicated microcontroller at the ACC level and cutting data adjustments are fed back to the machine control.

A vibration measurement system, which either can be connected to an accelerometer or use the dynamic force signal from the piezoelectric force transducer, is part of a vibration control module at the ACC level. An ultra-fast signal processor performs the signal analysis.

The remaining two measurement systems—a high frequency tool signal analysis system and a power spectra analysis system—are mentioned in the paper but not further discussed.

Finally, the paper deals with how the strategies at the three different levels will be combined, in order to form an AC system. The monitoring tasks will always reside in the background and be activated if any failure occurs. The ACO subsystem will act as a path-finder and suggest cutting data. The active control tasks will, however, be carried out at the ACC level.

Production companies of today face extreme challenge to meet the rapid changes and increased flexibility that mass customization require. More and more customers are requiring the product to suite specific needs such as design, function and sustainability. These requirements results in increasing demands for the developers of the product but also for the personnel who will assemble the products in the final assembling.This paper suggests the need for further development, primarily addressing time parameters in dynamically changing assembly systems. We propose proactivity as a vital characteristic of semi-automated assembly systems, to increase fulfilment of customer demands and decrease non value-adding tasks. In proactive assembly systems, the potential of human operators and technical systems is utilised. Criteria for proactivity are reviewed from automation, information, and competence perspectives. Empirical data have been collected from five production companies in Sweden.

Internet of things (IoT) in manufacturing can be defined as a future where every day physical objects in the shop floor, people and systems (things) are connected by the Internet to build services critical to the manufacturing. Smart factory is a way towards a factory-of-things, which is very much aligned with IoT. IoT not only deals with smart connections between physical objects but also with the interaction with different IT tools used within the digital factory. Data and information come from heterogeneous IT systems and from different domains, viewpoints, levels of granularity and life cycle phases causing potential inconsistencies in the data sharing, preventing interoperability. Hence, our aim is to investigate approaches and principles when integrating the digital factory, IT tools and IoT in manufacturing in a heterogeneous IT environment to ensure data consistency. In particular this paper suggests an approach to identify what, when and how information should be integrated. Secondly it suggests integration between IoT and PLM platforms using semantic web technologies and Open Services for Lifecycle Collaboration (OSLC) standard on tool interoperability.

Future manufacturing systems need to be more flexible, to embrace tougher and constantly changing market demands. They also need to make better use of plant data, ideally utilizing all data from the entire plant. Low-level data should be refined to real-time information for decision making, to facilitate competitiveness through informed and timely decisions. The Line.inf or mai on System, Architecture. LISA, is designed to enable flexible factory integration and data utilization. In LISA, international standards and established off-thevslielf technologies have been combined with the main objective to be industrially applicable. LISA is an event-driven architecture with a prototype-oriented information model and formalized transformation services. It features loose coupling, flexibility, and ease of retrofitting legacy devices. The architecture has been evaluated on both real industrial data and industrial demonstrators and is also being installed at a large automotive company.

Future manufacturing systems need to be more flexible, to embrace tougher and constantly changing market demands. They need to make better use of plant data, ideally utilising all data from the entire plant. Low-level data should be refined to real-time information for decision-making, to facilitate competitiveness through informed and timely decisions. The Line Information System Architecture (LISA), is presented in this paper. It is an event-driven architecture featuring loose coupling, a prototype-oriented information model and formalised transformation services. LISA is designed to enable flexible factory integration and data utilisation. The focus of LISA is on integration of devices and services on all levels, simplifying hardware changes and integration of new smart services as well as supporting continuous improvements on information visualisation and control. The architecture has been evaluated on both real industrial data and industrial demonstrators and it is also being installed at a large automotive company. This article is an extended and revised version of the paper presented at the 2015 IFAC Symposium on Information Control in Manufacturing (INCOM 2015). The paper has been restructured in regards to the order and title of the chapters, and additional information about the integration between devices and services aspects have been added. The introduction and the general structure of the paper now better highlight the contributions of the paper and the uniqueness of the framework.

This paper presents a model for describing machine tools based on the STEP standard AP239 as a neutral format for communicating information throughout production development and operation. The machine model is further defined at a reference data level, where concepts regarding machine tools are defined from four main viewpoints: process planning, investment process, factory planning and operation/improvement. The concept models extend the standard and define the specific machine tool information. The utilization of the machine model is exemplified in a use case.