Development of a supervisory internet of things (IoT) system for factories of the future

Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing. In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread. The limitations of DCM have been analysed, and further research directions have also been discussed

Monitoring and Control of Unstructured Manufacturing Big Data

Unstructured manufacturing big data silos are challenging for enabling various data-driven applications such as digital threads and digital twins in manufacturing. The management of big data silos requires to address the issues of large volume, data inconsistency, data redundancy, information silos and data security. This research developed a systematic approach to managing data silos using the state of art big data software. Applying this approach in the product life cycle can control data silos, data consistency, redundancy, timely update and enable the automatic workflow of each system

Manufacturing big data ecosystem: A systematic literature review

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity

Large scale MTConnect data collection

Data collection is the first stage of data lifecycle to drive manufacturing activities such as monitoring, prediction and optimization. Data collection at shop floor is more complex since it is affected by both the physical and cyber worlds. Two common issues are: difficult to collect data of various machines which support different interfaces and communication protocols, and hard to analyze inconsistent data. MTConnect provides a standard solution to collect consistent data from various machines and devices. The current MTConnect applications are based on a single computer which is not suitable to collect large-scale MTConnect data. Cloud computing technology can handle this issue easily such as Apache NiFi. In this paper, a NiFi based solution is proposed to address issues of large-scale MTConnect data collection, processing, and long-term storage. The proposed approach also provides a fault tolerant infrastructure for data provenance to ensure historical data integrity for the requirement of enterprise compliance

Ultraprecision Diameter Measurement of Small Holes with Large Depth-To-Diameter Ratios Based on Spherical Scattering Electrical-Field Probing

In order to solve the difficulty of precision measurement of small hole diameters with large depth-to-diameter ratios, a new measurement method based on spherical scattering electrical-field probing (SSEP) was developed. A spherical scattering electrical field with identical sensing characteristics in arbitrary spatial directions was formed to convert the micro gap between the probing-ball and the part being measured into an electrical signal. 3D non-contact probing, nanometer resolution, and approximate point probing—which are key properties for high measurement precision and large measurable depth-to-diameter ratios—were achieved. A specially designed hole diameter measuring machine (HDMM) was developed, and key techniques, including laser interferometry for macro displacement measurement of the probe, multi-degree-of-freedom adjustment of hole attitude, and measurement process planning, are described. Experiments were carried out using the HDMM and a probing sensor with a ϕ3-mm probing ball and a 150-mm-long stylus to verify the performance of the probing sensor and the measuring machine. The experimental results indicate that the resolution of the probing sensor was as small as 1 nm, and the expanded uncertainty of measurement result was 0.2 μm (k = 2) when a ϕ20-mm ring gauge standard was measured

Quantitative Investigation of Surface Charge Distribution and Point Probing Characteristics of Spherical Scattering Electrical Field Probe for Precision Measurement of Miniature Internal Structures with High Aspect Ratios

For precision measurement of miniature internal structures with high aspect ratios, a spherical scattering electrical field probe (SSEP) is proposed based on charge signal detection. The characteristics and laws governing surface charge distribution on the probing ball of the SSEP are analyzed, with the spherical scattering electrical field modeled using a 3D seven-point finite difference method. The model is validated with finite element simulation by comparing with the analysis results of typical situations, in which probing balls of different diameters are used to probe a grounded plane with a probing gap of 0.3 μm. Results obtained with the proposed model and finite element method (FEM) simulation indicate that 31% of the total surface charge on a ϕ1 mm probing ball concentrates in an area that occupies 1% of the total probing ball surface. Moreover, this surface charge concentration remains unchanged when the surface being measured varies in geometry, or when the probing gap varies in sensing range. Based on this, the SSEP has realized approximate point probing capability with a virtual “needle” of electrical effect. Together with its non-contact sensing characteristics and 3D isotropy, it can, therefore, be concluded that the SSEP has great potential to be an ideal solution for precision measurement of miniature internal structures with high aspect ratios

OTUB1 promotes osteoblastic bone formation through stabilizing FGFR2

Abstract Bone homeostasis is maintained by the balance between osteoblastic bone formation and osteoclastic bone resorption. Dysregulation of this process leads to multiple diseases, including osteoporosis. However, the underlying molecular mechanisms are not fully understood. Here, we show that the global and conditional osteoblast knockout of a deubiquitinase Otub1 result in low bone mass and poor bone strength due to defects in osteogenic differentiation and mineralization. Mechanistically, the stability of FGFR2, a crucial regulator of osteogenesis, is maintained by OTUB1. OTUB1 attenuates the E3 ligase SMURF1-mediated FGFR2 ubiquitination by inhibiting SMURF1’s E2 binding. In the absence of OTUB1, FGFR2 is ubiquitinated excessively by SMURF1, followed by lysosomal degradation. Consistently, adeno-associated virus serotype 9 (AAV9)-delivered FGFR2 in knee joints rescued the bone mass loss in osteoblast-specific Otub1-deleted mice. Moreover, Otub1 mRNA level was significantly downregulated in bones from osteoporotic mice, and restoring OTUB1 levels through an AAV9-delivered system in ovariectomy-induced osteoporotic mice attenuated osteopenia. Taken together, our results suggest that OTUB1 positively regulates osteogenic differentiation and mineralization in bone homeostasis by controlling FGFR2 stability, which provides an optical therapeutic strategy to alleviate osteoporosis

CLSTN3β enforces adipocyte multilocularity to facilitate lipid utilization.

Multilocular adipocytes are a hallmark of thermogenic adipose tissue1,2, but the factors that enforce this cellular phenotype are largely unknown. Here, we show that an adipocyte-selective product of the Clstn3 locus (CLSTN3β) present in only placental mammals facilitates the efficient use of stored triglyceride by limiting lipid droplet (LD) expansion. CLSTN3β is an integral endoplasmic reticulum (ER) membrane protein that localizes to ER-LD contact sites through a conserved hairpin-like domain. Mice lacking CLSTN3β have abnormal LD morphology and altered substrate use in brown adipose tissue, and are more susceptible to cold-induced hypothermia despite having no defect in adrenergic signalling. Conversely, forced expression of CLSTN3β is sufficient to enforce a multilocular LD phenotype in cultured cells and adipose tissue. CLSTN3β associates with cell death-inducing DFFA-like effector proteins and impairs their ability to transfer lipid between LDs, thereby restricting LD fusion and expansion. Functionally, increased LD surface area in CLSTN3β-expressing adipocytes promotes engagement of the lipolytic machinery and facilitates fatty acid oxidation. In human fat, CLSTN3B is a selective marker of multilocular adipocytes. These findings define a molecular mechanism that regulates LD form and function to facilitate lipid utilization in thermogenic adipocytes