Legacy Machines Research Articles

Digital Retrofitting of legacy machines: A holistic procedure model for industrial companies

Digital Transformation is a demanding evolutional process for industrial companies that touches all important business areas. A central role for the development towards a more flexible and efficient manufacture play production data. However, most machines in use lack a digital interface, such that they are not capable to connect in a network. Thus, data cannot be utilized. One solution from the technical perspective is Digital Retrofitting focusing on the implementation of additional sensors and edge devices to digitally connect the machine and transforming it into a cyber-physical system. This approach is also economically promising for companies since investing in new machines is far more expensive than the retrofitting of legacy systems. Nevertheless, industrial practice shows that Digital Retrofitting projects often do not lead to sustainable solutions because they lack integration into the digital transformation process. This paper thus introduces a holistic procedure model for Digital Retrofitting that is the result of an analysis and clustering of existing process models. The framework incorporates the perspectives on digital strategy, interdisciplinarity, change and lean management and, therefore, provides a guideline for companies leading to a more effective and efficient implementation process with sustainable impact. Furthermore, a description of a successful validation of the procedure model is given on the example of a condition monitoring use case in the Smart Production Lab of FH JOANNEUM.

In-Process Dimension Monitoring System for Integration of Legacy Machine Tools into the Industry 4.0 Framework

Abstract Manufacturers are implementing sensors, Internet of Things (IoT)-based automation, and communication devices on shop floors for connecting machine tools to a network-connected system for achieving “smart” functionality. The existing installation of legacy machines that offer either no or limited adaptability to these changes is a big obstacle to realizing the potential benefits of smart manufacturing. This research paper presents a sensor-based dimension monitoring system to capture and digitize component dimensions during machining operations. The capabilities are attained by developing an integrated framework consisting of sensors, data acquisition systems, feature extraction modules, and digital interfaces. The framework is implemented on legacy equipment such as lathes, milling, and drilling machines for component dimension monitoring while performing common operations. The proposed system functions at the edge level to improve man (operator)–machine–material interactions by displaying component dimensions and graphical visualization of the operations. The system also helps the operator recognize the resulting cutting forces and thereby achieve guided process control. The data generated at an edge level can be transmitted to the enterprise layer for performing tasks such as machine performance evaluation, man–machine utilization, process optimization, operator feedback, etc. The proposed framework provides a potential solution for integrating a vast base of the existing legacy machines into the Industry 4.0 framework.

Overall Equipment Utilisation (OEU) Monitoring and Remote Quality Check in Legacy Machine with Raspberry Pi

Overall Equipment Utilisation (OEU) plays an important role as a benchmark for manufacturing companies to determine each machine's efficiency. Currently, there is no proper OEU measurement system in legacy machines and only relies on human observation. This project aims to develop a measurement of OEU system by using Optical Character Recognition (OCR). An efficient Optical Character Recognition (OCR) algorithm is needed to have a high percentage of recognition rate. The outcome of this project will be a Graphical User Interface (GUI) that display real-time OEU monitoring and remote quality check for legacy machines. Pytesseract-OCR Version 4 classifier using the Recurrent Neural Network (RNN) method has been proposed in this paper. Furthermore, an error detection feature is designed from OCR output.

Upgrading a Legacy Manufacturing Cell to IoT

Many industries, such as aeronautics construction are still equipped with legacy machines and are not keen to change old, however fully functional, equipment to new ones. Hence, an upgrade must be found to cope the legacy and fully functional machines to IoT technologies. This paper is a contribution to embrace those challenges in a new IoT architecture able to support the creation of solutions for Smart Industries. Internet of Things is increasing acceptance and the infrastructure for them is becoming available. This leads to an insurgence on investments and development of new dedicated IoT infrastructures. Industries need to adapt quickly to this constant technological evolution, implementing measures and connecting machines and robots at critical points to the Internet, instrumenting them using the concept of IoT, with the major goal of implementing a flexible, secure, easy to maintain and capable to evolve infrastructure, when legacy equipment is involved. The availability of machines and other critical assets directly affects the effectiveness of manufacturing operations. The architecture design offers security, flexibility, simplicity of implementation and maintenance, and is resilient to failures or attacks and technologically independent. Field tests are reported to evaluate key aspects of the proposed architecture.

Iowa Liquor Sales Data Predictive Analysis Using Spark

The paper aims to analyze and predict sales of liquor in the state of Iowa by applying machine learning algorithms to models built for prediction. We have taken recourse of Azure ML and Spark ML for our predictive analysis, which is legacy machine learning (ML) systems and Big Data ML, respectively. We have worked on the Iowa liquor sales dataset comprising of records from 2012 to 2019 in 24 columns and approximately 1.8 million rows. We have concluded by comparing the models with different algorithms applied and their accuracy in predicting the sales using both Azure ML and Spark ML. We find that the Linear Regression model has the highest precision and Decision Forest Regression has the fastest computing time with the sample data set using the legacy Azure ML systems. Decision Tree Regression model in Spark ML has the highest accuracy with the quickest computing time for the entire data set using the Big Data Spark systems.

VIRTUAL DESKTOP INFRASTRUCTURE IN HIGHER EDUCATION INSTITUTION: AN APPLICATION OF HOME AND MOBILE COMPUTING ENVIRONMENT

Currently, universities have rising demands to apply the incredible recent developments in computer technology that support students to achieve skills necessary for developing applied critical thinking in the contexts of online society. Medical and engineering subjects’ practical learning and education scenarios are crucial to attain a set of competencies and applied skills. These recent developments allow sharing and resource allocation, which brings savings and maximize use, and therefore offer centralized management, increased security, and scalability. This paper describes the implantation of Virtual Desktop Infrastructure (VDI) to access the virtual laboratories that bring efficient use of resources as one of Al Balqa Applied University’s (BAU) Private Cloud services. The concept of desktop virtualization implements the sharing of capabilities utilizing legacy machines, which reduces the cost of infrastructure and introduces increased security, mobility, scalability, agility, and high availability. Al Balqa Applied University uses the service extensively to facilitate in/off-campus learning, teaching, and administrative activities and continue performing their work and education functions remotely to cope with the COVID-19 pandemic.

A Blockchain Tokenizer for Industrial IOT trustless applications

The Blockchain is a novel technology with a wide range of potential industrial applications. Despite a vast range of tests, prototypes, and proof of concepts implemented in the last years, the industrial use of Blockchain technology is still in the early stages. Enabling the interaction of industrial Internet of Things (IOT) platforms with Blockchain might be challenging because standards are still missing in both these technologies. Moreover, integrating productive assets with distributed data exchange and storage technologies is a kind of activity that needs to take into account various aspects, in particular: interoperability, portability, scalability, and security that need to be guaranteed by design.This paper describes the implementation of a portable, platform-agnostic and secure Blockchain Tokenizer for Industrial IOT trustless applications. The Industrial Blockchain Tokenizer (IBT) is based on an industrial data acquisition unit able to gather data from both modern and legacy machines while also interfacing directly with sensors. Acquired data can be processed locally enabling an edge filtering paradigm and then sent to any Blockchain platform. The system has been designed, implemented and then tested on two supply chain scenarios. Tests demonstrated the system capability to act as a bridge between industrial assets and Blockchain platforms enabling the generation of immutable and trust-less “digital twins” for industrial IOT applications.

A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs).

In the new era of manufacturing with the Fourth Industrial Revolution, the smart factory is getting much attention as a solution for the factory of the future. Despite challenges in small and medium-sized enterprises (SMEs), such as short-term strategies and labor-intensive with limited resources, they have to improve productivity and stay competitive by adopting smart factory technologies. This study presents a novel monitoring approach for SMEs, KEM (keep an eye on your machine), and using a low-cost vision, such as a webcam and open-source technologies. Mainly, this idea focuses on collecting and processing operational data using cheaper and easy-to-use components. A prototype was tested with the typical 3-axis computer numerical control (CNC) milling machine. From the evaluation, availability of using a low-cost webcam and open-source technologies for monitoring of machine tools was confirmed. The results revealed that the proposed system is easy to integrate and can be conveniently applied to legacy machine tools on the shop floor without a significant change of equipment and cost barrier, which is less than $500 USD. These benefits could lead to a change of monitoring operations to reduce time in operation, energy consumption, and environmental impact for the sustainable production of SMEs.

A Compressive Sensing Approach to Detect the Proximity Between Smartphones and BLE Beacons

Bluetooth low energy (BLE) beacons have been widely deployed to deliver proximity-based services (PBSs) to user’s smartphones when users are in the proximity of a beacon. Conventional proximity detection simply uses the received signal strength (RSS) to infer the proximity, and then retrieves the PBS by mapping the beacon ID with the corresponding service in the cloud database. Such an approach suffers two major issues: 1) the severe RSS fluctuation might confuse the smartphone during the detection and 2) a malicious PBS can be delivered by manipulating the same beacon ID. This paper proposes RF fingerprinting to label a beacon with an $N$ -dimensional fingerprint vector, which consists of $N$ RSS values from $N$ deployed beacons. The contribution of our proposed method is twofold: 1) we infer the proximity based on the fingerprint vector instead of relying solely on the single RSS value and 2) we retrieve the PBS by mapping the fingerprint vector instead of the hard-coded beacon ID. The challenge with our proposed approach is the incomplete fingerprint observation during real-time detection, resulting in an underdetermined proximity detection problem. To this end, we exploit the compressive sensing (CS) approach based on the differential evolutional algorithm to address such an underdetermined problem. Extensive simulations with real-world datasets show that our proposed approach outperforms the legacy machine learning techniques with substantial performance gains.

Implementation of the MIALinx User Interface for Future Manufacturing Environments

The flexible and easy-to-use integration of production equipment and IT systems on the shop floor becomes more and more a success factor for manufacturing to adapt rapidly to changing situations. The approach of the Manufacturing Integration Assistant (MIALinx) is to simplify this challenge. The integration steps range from integrating sensors over collecting and rule-based processing of sensor information to the execution of required actions. This paper presents the implementation of MIALinx to retrofit legacy machines for Industry 4.0 in a manufacturing environment and focus on the concept and implementation of the easy-to-use user interface as a key element.

Implementation of the MIALinx integration concept for future manufacturing environments to enable retrofitting of machines

Manufacturing has to adapt to changing situations in order to stay competitive. It demands a flexible and easy-to-use integration of production equipment and ICT systems. The contribution of this paper is the presentation of the implementation of the Manufacturing Integration Assistant (MIALinx). The integration steps range from integrating sensors over collecting and rule-based processing of sensor information to the execution of required actions. Furthermore, we describe the implementation of MIALinx by commissioning it in a manufacturing environment to retrofit legacy machines for Industrie 4.0. Finally, we validate the suitability of our approach by applying our solution in a medium-size company.

Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system

Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part families with uncertainty. A systematic virtual model mirroring the real world of manufacturing system is essential to bridge the gap between its design and operation. This paper presents a digital twin-driven methodology for rapid individualised designing of the automated flow-shop manufacturing system. The digital twin merges physics-based system modelling and distributed semi-physical simulation to provide engineering solution analysis capabilities and generates an authoritative digital design of the system at pre-production phase. An effective feedbacking of collected decision-support information from the intelligent multi-objective optimisation of the dynamic execution is presented to boost the applicability of the digital twin vision in the designing of AFMS. Finally, a bi-level iterative coordination mechanism is proposed to achieve optimal design performance for required functions of AFMS. A case study is conducted to prove the feasibility and effectiveness of the proposed methodology.

Online monitoring of a micro-EDM machine: Machining diagnosis on the cloud based on discharge currents and voltages

Currently, legacy machines (not designed to be connected to internet) are part of the manufacturing industry. This work presents a framework to incorporate legacy machines into cyber physical systems. A micro electrical discharge machine (µEDM) was equipped with sensors to monitor the machining energy. The data was uploaded to the internet. The data in the cloud determined if the machining was considered good, arcing or short circuit. This work contributes a cyber physical system that puts a µEDM online incorporating the real-time diagnosis of the machining quality, open architectures like MTConnect and compatibility with the goal of Industry 4.0.

Embedded smart box for legacy machines to approach to I 4.0 in smart manufacturing

This paper introduces the design of a Linux-based embedded controller which includes machine state detection application for legacy machines and manufacturing line. For Industrie 4.0 (I4.0), it is important to acquire, manipulate, and transmit machine operating states or physical data to form useful information. However, many existing legacy machines lack of controller or sensor(s) to response to their operating status. Some machine controllers cannot be connected to provide internal parameter(s) by means of communication. Gathering machine operating state should be the first priority to approach to I4.0. This paper adopts widely used Raspberry PI as the core platform to build Embedded Smart Box (ESB). It uses external sensors to detect the machine operating status to compute the machine's availability (one of Overall Equipment Efficiency factors) and measures current to calculate the power consumption. In this research, the combination of embedded system and sensors can be a smart box for legacy machines. Such cost-effective design would help users to take the useful data from the machines and construct the base of I4.0 system even without the existence of the controller. This embedded-based design methodology has great potential implications that might fundamentally change the legacy factories into I4.0 smart one.

An I2C based architecture for monitoring legacy manufacturing equipment

The Industrial Internet of Things (IIoT) has the potential to improve manufacturing through increased productivity and decreased costs by collecting, processing, and acting upon process data. Recent manufacturing equipment can provide the necessary data needed by IIoT, but most legacy machines cannot. This severely limits IIoT implementation since most production machines are legacy equipment. To overcome this limitation, this paper describes a low-cost architecture based upon the Inter-Integrated Circuit (I2C) communication protocol that can support IIoT systems by collecting data from sensors placed on legacy equipment. The proposed system is designed, fabricated, and validated on a horizontal band saw.

Holistic soldier fitness: A concept for healthier tomorrow

The flexible and easy-to-use integration of production equipment and IT systems on the shop floor becomes more and more a success factor for manufacturing to adapt rapidly to changing situations. The approach of the Manufacturing Integration Assistant (MIALinx) is to simplify this challenge. The integration steps range from integrating sensors over collecting and rule-based processing of sensor information to the execution of required actions. This paper presents the implementation of MIALinx to retrofit legacy machines for Industry 4.0 in a manufacturing environment and focus on the concept and implementation of the easy-to-use user interface as a key element.

Method for Automatically Recognizing Various Operation Statuses of Legacy Machines

Monitoring the operation status of machine tools using IoT is widely carried out in order to improve productivity. However, at manufacturing sites, a lot of legacy machines, which are old and lack the capability of sending data on their operation status to networks, are still in use because the average useful life of machine tools is more than 20 years. Therefore, we developed method for recognizing the operation status of machine tools using a spindle motor current acquired by a current sensor. Because this current is in proportion to the spindle torque, a conventional method recognises the operation status when the current amplitude is above a threshold value. However, in a high-mix low-volume factory, the threshold value must be reset frequently because a drill and the cutting process change per order so the spindle torque varies. In contrast, we propose an automatic recognition method that can learn the variance in spindle motor current by unsupervised learning and with labelling scheme based on prior knowledge. We applied the proposed method to eight kinds of machine tools in a real factory, and the accuracy rate of the operation status estimation was more than 80% for all the machines. This result shows that all machine tools could be monitored by using this method.

Efficacy of Cruise Control in controlling postocclusion surge with Legacy and Millennium venturi phacoemulsification machines

To determine the efficacy of the Cruise Control surge-limiting device (Staar Surgical) with phacoemulsification machines known to have high levels of surge. John A. Moran Eye Center Clinical Laboratories. In an in vitro study, postocclusion anterior chamber depth changes were measured in fresh phakic human eye-bank eyes using the Alcon Legacy and Bausch & Lomb Millennium venturi machines in conjunction with the Staar Cruise Control device. Both machines were tested with 19-gauge non-Aspiration Bypass System tips at high-surge settings (500 mm Hg vacuum pressure, 75 cm bottle height, 40 mL/min flow rate for the Legacy) and low-surge settings (400 mm Hg vacuum pressure, 125 cm bottle height, 40 mL/min flow rate for the Legacy). Adjusted parameters of flow, vacuum, and irrigation were used based on previous studies to create identical conditions for each device tested. The effect of the Cruise Control device on aspiration rates was also tested with both machines at the low-surge settings. At the high setting with the addition of Cruise Control, surge decreased significantly with the Legacy but was too large to measure with the Millennium venturi. At the low setting with the addition of Cruise Control, surge decreased significantly with both machines. Surge with the Millennium decreased from more than 1.0 mm to a mean of 0.21 mm +/- 0.02 (SD) (P<.0001). Surge with the Legacy decreased from a mean of 0.09 +/- 0.02 mm to 0.05 +/- 0 mm, a 42.9% decrease (P<.0001). The Millennium had the highest surge and aspiration rate before Cruise Control and the greatest percentage decrease in the surge and aspiration rates as a result of the addition of Cruise Control. In the Legacy machine, the Cruise Control device had a statistically and clinically significant effect. Cruise Control had a large effect on fluidics as well as surge amplitude with the Millennium machine. The greater the flow or greater the initial surge, the greater the impact of the Cruise Control device.

Effect of incisional friction and ophthalmic viscosurgical devices on the heat generation of ultrasound during cataract surgery

To determine the thermal features of the Legacy (Alcon) and Sovereign (Advanced Medical Optics) phacoemulsification machines in a cadaver eye and with 7 ophthalmic viscosurgical devices (OVDs). In situ and in vitro study. Temperature without occlusion was recorded at the sleeve placed in the wound of a cadaver eye, and temperature over baseline was determined after 60 seconds. The result was then compared with the results in a previous study that used balanced salt solution (BSS) in artificial chambers. In the second portion of the experiment, with irrigation and aspiration lines occluded, temperature was recorded at the sleeve placed in an artificial chamber filled with sodium hyaluronate 2.3% (Healon5), sodium hyaluronate 1.4% (Healon GV), sodium hyaluronate 1.0% (Healon), sodium hyaluronate 1.6% (Amvisc Plus), sodium hyaluronate 1.0% (Provisc), sodium hyaluronate 3.0%-chondroitin sulfate 4.0% (Viscoat), or hyaluronate 3.0% (Vitrax). Temperature over baseline was also determined after 60 seconds. These results were compared with each set of OVD data and with the results in the prior BSS study. In the eye-bank model, the Legacy machine had a 62% temperature increase from incisional friction and the Sovereign machine had a decrease of 8.6% over results in an artificial anterior chamber. The OVD temperature increases were greater for the Sovereign (P<.001) and followed the same general trend for the Legacy. The least temperature increase was with Amvisc Plus, Healon, and Healon GV; the intermediate increases were with Provisc and Vitrax; and the greatest increases were with Viscoat and Healon5. The OVD findings did not correlate with viscosity or pseudoplasticity. Incisional friction alone increased heat generation in the Legacy, a stroke-length driven instrument, more than in the Sovereign, a power-driven instrument. Ophthalmic viscosurgical devices are not only a concern due to outflow occlusion but can also add up to 6 times the heat in comparison with BSS. The need to aspirate the OVD before using ultrasound is thus verified.

Avaliação do desempenho da tecnologia NeoSoniX TM em cirurgias de catarata

The techniques and results of cataract surgery have improved dramatically with technological advancements. One of the reasons for this improvement would be the reduced use of ultrasound with the new phacoemulsification devices. The objective of this study is to confirm the theoretical advantage of the NeoSoniX handpiece. The authors compared the results of the parameters recorded by the Legacy phacoemulsification system during 300 cataract surgeries performed with the use of the conventional ultrasound handpiece and 100 performed with the innovative Neosonix. All surgeries were performed by the same senior surgeon, using the same surgical technique. The choice of handpiece was random. The handpieces were compared considering cataract grade of hardness, in relation to parameters of ultrasound time, amount of balanced saline solution, and percentage of average power used during surgery. The analysis performed retrospectively on a chronological series of cases showed that the time of ultrasound used with the NeoSoniX handpiece was inferior to that with the conventional handpiece, for any cataract grade of hardness (p<0.000001). The volume of balanced saline solution used during surgical procedures was smaller, favoring the NeoSoniX technology (p<0.000001). In relation to the percentage of ultrasound energy used with the different technologies, an inferior value was found in the group who underwent surgery with the use of the NeoSoniX handpiece (p<0.000001). Based on the information provided by these surgeries, we may claim that there was a significant superiority when using the NeoSoniX handpiece in comparison to the conventional Legacy machine handpiece in cataract surgeries, regardless of cataract grade.