Intelligent Process Management Research Articles

Metal forming defect detection method based on recurrence quantification analysis of time-series load signal measured by real-time monitoring system with bolt-type piezoelectric sensor

In recent years, research related to smart manufacturing processes, which aim to advance production technology through productivity improvement and manufacturing technology innovation, has been actively pursued. In the field of sheet metal press forming, intelligent process management is required to improve productivity and reduce costs. In this field, a method of indirectly monitoring signals generated in the forming process without the need to modify existing tools is advantageous, as it can be easily applied to the mass-production process. In this study, a monitoring system for a laboratory-scale cup drawing process was established that uses a bolt-type piezoelectric sensor. First, a numerical analysis was conducted to select the location where the sensor was to be installed in the tool, and the feasibility of load monitoring was confirmed. Then, the forming load of the cup drawing process using a servo press was measured according to process variables such as holding force, specimen size, and initial specimen position. Forming defects such as wrinkles and fractures occurred according to process variables, and it was confirmed that the measured load had a specific pattern. To quantitatively analyze the time series data of the measured load, recurrence quantification analysis was used, and a system for real-time monitoring of formability was developed based on the structural shapes formed by the resulting recurrence plots.

Automation of a garment sewing department assessment by smart simulation

The textile-garment industry needs great production flexibility to adapt, in terms of sustainability, to the fickle fashion market. This study proposes a new simulation model of manufacturing and production processes, which can be used as design support, performance evaluation and plant management of highly flexible production systems such as those operating in the fashion sector. The proposed simulation model aims to support industries in increasing their reactive flexibility by adjusting the production set-up. It is a distributed discrete event simulator based on the easy re-composition of resources in the production layout and on the availability of intelligent process management and control modules. The simulator has been applied to optimise the production of children's garments. The development was done together with the production manager of an important European industry; the manager enjoyed the intuitive use of the software and the significance and reliability of the information obtained.

Improving data communication on construction sites via LoRaWAN

Easily-accessible and reliable data communication in construction processes ensures high building quality, efficient workflow and secure working environments. The setup of network infrastructure on construction sites provides the necessary condition for timely and effective data communication. This paper researches a solution for on-site networking by implementing an IoT network on a reference construction site in Germany. In contrast to high-cost and high-bandwidth network infrastructure, a Long Range Wide Area Network–LoRaWAN with low cost and low bandwidth was deployed on the site. With additional IoT servers and LoRa-enabled devices, the reference construction site is able to communicate remotely with a robotic lab. In order to validate this concept of LoRaWAN on construction sites, an intra-site logistics and task scheduling system was developed to test the network performance. This paper conducts a preliminary study on the application of the IoT network technology–LoRaWAN in the logistics automation in construction. The test results can be used as references for other automation applications, such as internet of robot, intelligent process management, decision making system, etc.

Collaboration Robots as Digital Doubles of Person for Communication in Public Life and Space

Trans-sectoral digital research of man, nature, society and industrial communication allows to create digital twins of social services. Digital duplicates associated with the service sector are created for intelligent process management. Digital twins of man provide services in the social sphere and in space. Training of digital twins in professional competences is carried out on the basis of communicative associative logic of technological thinking by cognitive methods. The study of the effectiveness of machine learning techniques, allowed the use of an approach that allows the combination of artificial intelligence and cognitive psychology. This approach provided pre-preparation of neural networks from accumulated data using existing behaviors. The approach combines existing scientific theories of human behavior with the flexibility of neural networks to make better decisions made by humans in space and in extreme situations. From a practical point of view, this makes it possible to more accurately determine the behavior of the human digital twin in space and in extreme situations. There are a number of socio-economic issues related to human-machine interaction. Complex technologies are not credible on the part of citizens. The coming years will take to improve safety and standardize the creation, application of digital twins and behavior of robots.

The practical exploitation of tacit machine tool intelligence

Manufacturing equipment embraces an increasing measure of tacit intelligence, in both capacity and value. However, this intelligence is yet to be exploited effectively. This is due to both the costs and limitations of developed approaches and a deficient understanding of data value and data origin. This work investigates the principal limitations of typical machine tool data and encourages consideration of such inherent limitations in order to improve potential monitoring strategies. This work presents a novel approach to the acquisition and processing of machine tool cutting data. The approach considers the condition of the monitored system and the deterioration of cutting tool performance. The management of the cutting process by the machine tool controller forms the basis of the approach, and hence, makes use of the tacit intelligence that is deployed in such a task. By using available machine tool controller signals, the impact on day-to-day machining operations is minimised while avoiding the need to retrofit equipment or sensors. The potential of the approach in the contexts of the emerging internet of things and intelligent process management and monitoring is considered. The efficacy of the approach is evaluated by correlating the actively derived measure of process variation with an offline measurement of product form. The potential is then underlined through a series of experiments for which the derived variation is assessed as a direct measure of the cutting tool health. The proposed system is identified as both a viable alternative and synergistic addition to current approaches that mainly consider the form and features of the manufactured component.

Collaboration Robots as Digital Doubles of Person for Communication in Public Life and Space

Trans-sectoral digital research of man, nature, society and industrial communication allows to create digital twins of social services. Digital duplicates associated with the service sector are created for intelligent process management. Digital twins of man provide services in the social sphere and in space. Training of digital twins in professional competences is carried out on the basis of communicative associative logic of technological thinking by cognitive methods. The study of the effectiveness of machine learning techniques, allowed the use of an approach that allows the combination of artificial intelligence and cognitive psychology. This approach provided pre-preparation of neural networks from accumulated data using existing behaviors. The approach combines existing scientific theories of human behavior with the flexibility of neural networks to make better decisions made by humans in space and in extreme situations. From a practical point of view, this makes it possible to more accurately determine the behavior of the human digital twin in space and in extreme situations. There are a number of socio-economic issues related to human-machine interaction. Complex technologies are not credible on the part of citizens. The coming years will take to improve safety and standardize the creation, application of digital twins and behavior of robots.

The HOSHIN KANRI TREE. Cross-plant Lean Shopfloor Management

Shopfloor Management (SM) empowerment methodologies have traditionally focused on two aspects: goal achievement following rigid structures, such as SQDCME, or evolutional aspects of empowerment factors away from strategic goal achievement. Furthermore, SM Methodologies have been organized almost solely around the hierarchical structure of the organization, failing systematically to cope with the challenges that Industry 4.0 is facing. The latter include the growing complexity of value-stream networks, sustainable empowerment of the workforce (Learning Factory), an autonomous and intelligent process management (Smart Factory), the need to cope with the increasing complexity of value-stream networks (VSN) and the leadership paradigm shift to strategic alignment. This paper presents a novel Lean SM Method (LSM) called “HOSHIN KANRI Tree” (HKT), which is based on standardization of the communication patterns among process owners (POs) by PDCA. The standardization of communication patterns by HKT technology should bring enormous benefits in value stream (VS) performance, speed of standardization and learning rates to the Industry 4.0 generation of organizations. These potential advantages of HKT are being tested at present in worldwide research.

A first step towards unmanned intelligent process management: A procedure for the diagnostics and prognostics of energy conversion plants

This paper describes the conceptual development and the prototype implementation of an Expert System that deals with the prognosis and diagnosis of a significant subset of the operative faults of a cogeneration power plant. The expert system receives both raw and organised real-time information on the “instantaneous” (actually, averaged over 60-seconds intervals) plant operating conditions from a non-intelligent “plant interface” consisting of a standard plant data acquisition system and of a specific plant simulation software. This solution was adopted in view of possible future applications to industrial plants, where a low number of intrusive sensors is desirable: in this case, the simulator provides the missing data. The Inference Engine operates on the basis of a set of pre-defined rules that seek possible “faults chains” expressed as combinations of a pre-assigned number of continuously calculated performance indicators, like the air filter output pressure drop, the relative compressor, combustion and turbine efficiency decay with respect to their respective nameplate values, the compressor enthalpy gain and surge conditions, the pollutants concentration in flue gases, the relative pressure losses in both the primary and secondary water circuit, the TTD of the Heat Recovery Boiler, etc.: the complete list includes 29 indices. The rules establish whether a component’s behaviour is degraded by examining both the individual indicators and all of their relevant combinations. A graphic window displays a series of icons, one for each indicator, with a refresh rate of one real-time minute. The Expert System enables the user to determine in detail the instantaneous performance conditions. If performance deterioration is detected, it sends a message to the operator and provides some decision support via a customised graphic interface. The structure of the code is Object Oriented, and each component as well as each flux are represented as the instance of a class. Both the reasoning and the controlling actions are taken in the same O-O environment. The present paper presents the organization of an ES whose prototype version has been nicknamed PROMISE, from the Italian acronym for PROgnostic and Intelligent Monitoring Expert System, defines its goals and discusses both the results of the tests conducted so far and the implications for future applied research in this field.

Modular™ Systems At Work

Modular™ Systems At Work

Modelling and control of fed-batch fermentation processes: towards improved quality and productivity

This paper describes research into modelling, estimation and control of fed batch fermentation and an application to a large fermentation plant producing secondary metabolites. The problems are examined in a general sense from the viewpoints of both biological science and control engineering. Very extensive modelling and estimation work has been carried out. Only the most significant outcomes are reported here but references to the detailed research are given. Some of the research has resulted in the development of a novel advanced supervisory system- the Intelligent Process Management System (IPMS). Experience of applying the IPMS to a large-scale production process is described. The software has been designed on a modular basis and although implemented initially at one production plant, the approach is generic and fundamentally plant-independent so that further application to a range of other plants is envisaged.