Implementing the Transformation of Discrete Part Manufacturing Systems Into Smart Manufacturing Platforms

2018 ◽  
Author(s):  
Bhaskar Botcha ◽  
Zimo Wang ◽  
Sudarshan Rajan ◽  
Natarajan Gautam ◽  
Satish T. S. Bukkapatnam ◽  
...  
Keyword(s):  
Real Time ◽  
High Performance ◽  
Manufacturing Systems ◽  
Energy Savings ◽  
Advanced Materials ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Workflow System ◽  
Manufacturing Technologies ◽  
Discrete Part

Prior R&D efforts point to substantial performance enhancements and energy savings from adopting the Smart Manufacturing (SM) paradigm for process optimization and real-time quality assurance. Significant barriers and risks disincentivize the industry from investing in the adoption and training of SM component suites for discrete manufacturing applications. A diverse discrete part manufacturing enterprises, SM tools and platform vendors are yearning for a testbed reconfigurable to achieve three objectives of performance benchmarking, demonstration, and workforce training for a spectrum of their industrial scenarios and workflows. This paper presents the key ingredients towards the successful transformation of present machine tool and manufacturing environments into SM platform-integrated environments. The present implementation focuses on demonstration of the use of the Smart Manufacturing (SM) platform towards qualification of advanced materials and manufacturing technologies to meet an industry-specified functionality. This initial implementation uses Kepler workflow system residing as part of an Amazon Web Services environment to allow flexible workflows on multiple machines, each of which is integrated with an innovative sensor wrapper that integrates Commercial Off The Shelf (COTS) components from National Instruments (NI) to connect a legacy equipment to the SM platform. Here, an advanced analytics engine with modules customizable for both high-performance computing and shop floor environments was integrated into the commercial web service (from Amazon) to provide real-time monitoring and anomaly detection capability. This implementation indicates the potential of SM platform to achieve drastic reductions in the time and effort taken towards qualification of advanced materials and manufacturing technologies.

A Digital Twin Concept for Manufacturing Systems

2018 ◽  
Author(s):  
Wesley Ellgass ◽  
Nathan Holt ◽  
Hector Saldana-Lemus ◽  
Julian Richmond ◽  
Ali Vatankhah Barenji ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Virtual Space ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Digital Twin ◽  
Physical Resources ◽  
Virtual Resources

With the developments and applications of the advanced information technologies such as cloud computing, internet of thing, artificial intelligence and virtual reality, industry 4.0 and smart manufacturing era are coming. In this respect, one of the specific challenges is to achieve a connection of physical resources on the shop floor with virtual resources, for real-time response, real time process optimization, and simulation, which is merged by big data problem. In this respect, Digital Twins (DT) concept is introduced as a key technology, which includes physical resources, virtual resources, service system, and digital twin data. DT considers current condition of physical resource and prediction of future events to make a responsive decision. However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little applications have been developed with this purpose, especially in the industrial manufacturing area. Therefore, the types of data and technology required to build the DT for a manufacturing system are presented in this work, trying to develop a framework of DT based manufacturing system, which is supported by the virtual reality for virtualization of physical resources.

Shop floor management system in the context of smart manufacturing: a case study

2019 ◽  
Vol 11 (5) ◽  
pp. 837-862 ◽  
Author(s):  
Diamantino Torres ◽  
Carina Pimentel ◽  
Susana Duarte
Keyword(s):  
Cyber Security ◽  
Performance Indicator ◽  
Theoretical Background ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Security Technology ◽  
Content Type ◽  
Smart Technologies ◽  
Manufacturing Technologies

Purpose The purpose of this study intends to make a characterization of a shop floor management (SFM) system in the context of smart manufacturing, through smart technologies and digital shop floor (DSF) features. Design/methodology/approach To attain the paper objective, a mixed method methodology was used. In the first stage, a theoretical background was carried out, to provide a comprehensive understanding on SFM system in a smart manufacturing perspective. Next, a case study within a survey was developed. The case study was introduced to characterize a SFM system, while the survey was made to understand the level of influence of smart manufacturing technologies and of DSF features on SFM. In total, 17 experts responded to the survey. Findings Data analytics is the smart manufacturing technology that influences more the SFM system and its components and the cyber security technology does not influence it at all. The problem solving (PS) is the SFM component more influenced by the smart manufacturing technologies. Also, the use of real-time digital visualization tools is considered the most influential DSF feature for the SFM components and the data security protocols is the least influential one. The four SFM components more influenced by the DSF features are key performance indicator tracking, PS, work standardization and continuous improvement. Research limitations/implications The study was applied in one multinational company from the automotive sector. Originality/value To the best of the authors’ knowledge, this work is one of the first to try to characterize the SFM system on smart manufacturing considering smart technologies and DSF features.

scholarly journals Blockchain Reference System Architecture Description for the ISA95 Compliant Traditional and Smart Manufacturing Systems

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6456 ◽  
Author(s):  
Erkan Yalcinkaya ◽  
Antonio Maffei ◽  
Mauro Onori
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Research Effort ◽  
Smart Manufacturing ◽  
Reference Architecture ◽  
Systematic Assessment ◽  
Blockchain Technology ◽  
Design Requirements ◽  
Manufacturing Technologies ◽  
Smart Manufacturing Systems

The next-generation technologies enabled by the industry 4.0 revolution put immense pressure on traditional ISA95 compliant manufacturing systems to evolve into smart manufacturing systems. Unfortunately, the transformation of old to new manufacturing technologies is a slow process. Therefore, the manufacturing industry is currently in a situation that the legacy and modern manufacturing systems share the same factory environment. This heterogeneous ecosystem leads to challenges in systems scalability, interoperability, information security, and data quality domains. Our former research effort concluded that blockchain technology has promising features to address these challenges. Moreover, our systematic assessment revealed that most of the ISA95 enterprise functions are suitable for applying blockchain technology. However, no blockchain reference architecture explicitly focuses on the ISA95 compliant traditional and smart manufacturing systems available in the literature. This research aims to fill the gap by first methodically specifying the design requirements and then meticulously elaborating on how the reference architecture components fulfill the design requirements.

A Data-Driven Simulation-Based Optimisation Approach for Adaptive Scheduling and Control of Dynamic Manufacturing Systems

2016 ◽  
Vol 1140 ◽  
pp. 449-456 ◽  
Author(s):  
Mirko Kück ◽  
Jens Ehm ◽  
Michael Freitag ◽  
Enzo M. Frazzon ◽  
Ricardo Pimentel
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Shop Floor ◽  
Time Data ◽  
Rush Order ◽  
Real Time Data ◽  
High Flexibility ◽  
And Control

The increasing customisation of products, which leads to higher numbers of product variants with smaller lot sizes, requires a high flexibility of manufacturing systems. These systems are subject to dynamic influences and need increasing effort for the generation of the production schedules and for the control of the processes. This paper presents an approach that addresses these challenges. First, scheduling is done by coupling an optimisation heuristic with a simulation model to handle complex and stochastic manufacturing systems. Second, the simulation model is continuously adapted by real-time data from the shop floor. If, e.g., a machine breakdown or a rush order appears, the simulation model and consequently the scheduling model is updated and the optimisation heuristic adjusts an existing schedule or generates a new one. This approach uses real-time data provided by future cyber-physical systems to integrate scheduling and control and to manage the dynamics of highly flexible manufacturing systems.

Special Issue on Process Simulation

2013 ◽  
Vol 7 (1) ◽  
pp. 5-5
Author(s):  
Takashi Matsumura
Keyword(s):  
Process Simulation ◽  
Machine Tools ◽  
High Performance ◽  
Advanced Materials ◽  
Manufacturing Processes ◽  
Special Issue ◽  
Process Simulations ◽  
Investment Returns ◽  
Manufacturing Technologies ◽  
Effective Use

High production rates and low costs in manufacturing process should be considered in the manufacturing design divisions. Process simulation, therefore, plays an important role in implementing high performance manufacturing. Simulation is expected to improve the manufacturing processes and the human activities without production faults and downtime of the manufacturing facilities. The production simulation has become diversified with requirements for the manufacturing processes. Then, the effective use of the simulation is also an important issue for the simulation users considering investment returns. Recently advanced materials have been applied to products with developments in material science. The machining systems have also become complicated with progress in the machine tools. Therefore, the process simulations should be developed in terms of materials and machine tools. This special issue includes 9 papers for providing innovative approaches to advanced modeling and simulations in manufacturing technologies and machine tool systems. The special issue also includes discussions in the simulation with the advanced materials for future manufacturing processes. I thank the authors for their generous cooperation and the editing staff for its many contributions.

A Sensor Model for Enhancement of Manufacturing Equipment Data Interoperability

2012 ◽  
Author(s):  
Kang B. Lee ◽  
Eugene Y. Song ◽  
Peter S. Gu
Keyword(s):  
Data Exchange ◽  
Manufacturing Systems ◽  
Sensor Data ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Sensors And Actuators ◽  
Data Interoperability ◽  
Manufacturing Equipment ◽  
Sensor Model ◽  
Smart Manufacturing Systems

Sensors can provide real-time production information to optimize manufacturing processes in a factory. Recently, more attention has been paid to the application of sensors in smart manufacturing systems. Sensor data exchange, sharing, and interoperability are challenges for manufacturing equipment monitoring in smart manufacturing. Standardized sensor data formats and communication protocols can help to solve these problems. MTConnect is an open, free, extensible protocol for the data exchange between monitoring applications and shop floor devices which include machine tools, sensors, and actuators. This paper introduces a sensor model for MTConnect to enhance manufacturing equipment data interoperability. The sensor model defines a Sensor and SensorChannel, as well as an interface to access the Sensor and its SensorChannels, which include sensing element, calibration, signal conditioning, and analog-to-digital conversion (ADC) information. The sensor model has been implemented in a virtual milling machine with a built-in sensor. Two case studies of MTConnect Probe and Sample requests for sensor information are provided to verify the sensor model.

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