Smart Shop Research Articles

Internet of Things (IoT) Based Smart Shop (S-SHOP) System with RFID Technique

Billing system technology in the market nowadays is less efficient in parallel with revolution of technology. Conventional method widely uses a barcode scanner that has disadvantages as it is a manual system and requires man power. Besides that, the conventional method does not store the information such as warranty of the product and the information of a buyer. Using the smart shop billing system using Radio Frequency Identification (RFID) reader and RFID tag, the system will become more efficient and automated. The system consists of two main elements which are passive RFID tag (125 kHz) and RFID reader (ID-20LA) that has similar function with a barcode tag and a barcode scanner. For the hardware components, the Espresso Lite V2.0 acts as a microcontroller and UC00C acts as Universal Asynchronous Receiver/Transmitter (UART) converter and Arduino IDE is used as a platform to link between hardware and software. The system is completed with the Internet of Things (IoT) technique to make sure the system can be operated wirelessly. As a result, all the information from the reader will be sent to Google spreadsheet as a cloud to store the information about the item to generate GUI bill. In conclusion this proposed system also uses a real time tracking.

3D bare-hand interactions enabling ubiquitous interactions with smart objects

Ubiquitous augmented reality (UAR) implementation can benefit smart shop floor operations significantly. UAR from a user’s first-person view can support and provide the user with suitable and comprehensive information without him/her being distracted from ongoing tasks. A natural hand-based interaction interface, namely, a mobile bare-hand interface (MBHI), is proposed to assist a user in exploring and navigating a large amount of information for a task in the user’s first-person view. The integration of a smart shop floor and UAR-based MBHI is particularly challenging. A real shop floor environment is composed of challenging conditions for the implementation of UAR, e.g., messy backgrounds and significant changes in illumination conditions. Meanwhile, the MBHI is required to provide precise and quick responses to minimize the difficulty of a user’s task. In this study, a wearable UAR system integrated with an MBHI is proposed to augment the shop floor environment with smart information. A case study is implemented to demonstrate the practicality and effectiveness of the proposed UAR and MBHI system.

A Simple Smart Shop Floor Paradigm for Industry 4.0

This paper illustrates the needs and challenges of the smart shop floor in intelligent manufacturing and combines with insights from the field of current MES system needs, advantages and drawbacks, and describes the cloud platform enrich production service system. In a word, recent advanced technologies has paved way for a complete and systematic implement of it. the smart shop floor is in the initial stage of development in factory. At present, there is an urgent need for a viable paradigm of smart shop floor as a guideline. By integrating all relative applications and solutions, Chongqing Guangshu Robot Co., Ltd has designed a simple enough paradigm to adopt the Industry 4.0, within which all related information is closely monitored and synchronized between the physical shop floor and the network computational space.

Analyzing the cyber-physical system–based autonomous collaborations among smart manufacturing resources in a smart shop floor

Industry 4.0 focuses on the realization of smart manufacturing from shop floors to factories and to the whole supply chain. As a key technology of smart manufacturing, cyber-physical system has been widely discussed in the aspects of system design, data collection and processing, and cyber-physical synchronization. In a smart shop floor, manufacturing resources with intelligence and autonomy are abstracted as cyber-physical system units. They can communicate with each other autonomously to make optimal production decisions according to the real-time status of the shop floor. In this article, an autonomous collaboration network comprised of cyber-physical system–based smart manufacturing resources is modeled by using complex network theory. The collaboration activities among them are further analyzed, from which the information of key cyber-physical system units and key collaboration relationships are excavated. A demonstrative case is studied to verify the feasibility of the proposed model. From the case, it can be seen that (1) autonomous collaboration network has a small-world feature; (2) cyber-physical system units with bigger degrees and the collaborative relationships with bigger tightness are more important; (3) the workload of cyber-physical system units needs to be balanced because some cyber-physical system units have exceeded their capacities; and (4) cyber-physical system units with larger collaboration clustering coefficients will attract other nodes to form communities centered by them. Based on these results, the autonomous production control and management of smart shop floor will become more accurate, efficient, and balanced.

Hidden Markov model-based autonomous manufacturing task orchestration in smart shop floors

Abstract Smart manufacturing requires flexible production organization and management to handle the dynamic customer requirements rapidly and efficiently. In the context of smart manufacturing, work-in-progress (WIP), machines, and other physical resources in smart shop floors are endowed with intelligence, such as self-perception and self-decision-making. In this situation, the manufacturing task orchestration in such smart shop floors becomes autonomous, which is different from the traditional one that is centrally set and managed. The manufacturing tasks are accomplished with the help of autonomous communication between the WIP and the machines. This paper firstly clarifies the logic of autonomous manufacturing, in which the core idea is the autonomous communication and collaboration between the WIP and the machines during production. Furthermore, the autonomous manufacturing task orchestration (AMTO) problem is described. An improved hidden Markov model (HMM) is proposed to formulate the problem and generate an optimal AMTO solution for a certain process flow. A demonstrative case is implemented to verify the feasibility of the proposed model and method. The results show that HMM can give suggestions on AMTO and dynamically adjust the situation based on the real-time manufacturing data.

Enhancing smart shop floor management with ubiquitous augmented reality

This paper describes a framework for implementing Smart manufacturing Shop floor systems based on the Ubiquitous Augmented Reality technology (SSUAR). The proposed system makes use of data sharing between shop floor resources and a sensor network in order to optimise the production schedules for carrying out projects. The optimisation is performed in real-time and the production scheduling responds to new projects as well as the changing status of resources, such as machines and workers. Ubiquitous augmented reality interface has been developed and utilised as a user interface for the shop floor workers to receive information, instructions and guidance from the experts and manufacturing systems, and to update the systems on task parameters, such as estimated completion times, progress and machine status. A review of related work, methodology and implementation of the proposed system, and a case study are presented in this paper. Using this architecture, real-time scheduling of tasks in the smart shop floor can be achieved. The case study demonstrated the ability of SSUAR to integrate task scheduling with two-way communication between the system and the users.

Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors

Smart manufacturing is the core idea of the fourth industrial evolution. For a smart manufacturing shop floor, real-time monitoring, simulation and prediction of manufacturing operations are vital to improve the production efficiency and flexibility. In this paper, the Cyber-Physical System (CPS) and Digital Twin technologies are introduced to build the interconnection and interoperability of a physical shop floor and corresponding cybershop floor. A Digital Twin-based Cyber-Physical Production System (DT-CPPS) is further established, and the configuring mechanism, operating mechanism and real-time data-driven operations control of DT-CPPS are discussed in detail. It is expected that DT-CPPS will provide the basis for shop floors to march towards smart manufacturing.

Training a Hidden Markov Model-Based Knowledge Model for Autonomous Manufacturing Resources Allocation in Smart Shop Floors

As the manufacturing industry is heading toward the fourth industrial revolution, smart manufacturing is born at the right moment. By integrating new information technologies, such as the Internet of Things, the cyber-physical system (CPS), big data, and artificial intelligence, smart manufacturing has endowed the factories and shop floors with much intelligence, which is characterized by the organic cooperation among workers, machines, unmanufactured products, and other physical assets. In this situation, endowing these smart physical assets with self-X intelligence and autonomy to make manufacturing resources allocation decisions autonomously has been a vital problem that needs prompt solutions. To solve this problem, this paper deals with training a reasonable knowledge model from the historical shop floor data using a hidden Markov model (HMM) theory. In this model, the unmanufactured product's machining feature/process flow is considered as an observation sequence and the corresponding smart manufacturing resources (SMRs) sequence is considered as a hidden state sequence. The solving method to train the HMM-based knowledge model for autonomous manufacturing resources allocation (A-MRA) is further described in a step-by-step manner. Thereafter, a demonstrative case is studied to verify the proposed model and method. First, 123 pairs of historical data (i.e., process flow and SMR sequence) are used to learn the HMM-based knowledge model and another 5 pairs of historical data are used to test the feasibility and accuracy of the proposed model. The results show that only three elements (total $5\times 9$ elements) in the predicted SMR sequences are different from those in the historical SMR sequences, and the average vector angle between the five predicted and historical SMR sequences is 11.68°, which is relatively low considering that only nine elements exists in each SMR sequence.

Axiomatic Design based Design of a Software Prototype for Smart Shopfloor Management

Shopfloor management systems are currently undergoing a change. With the era of Industry 4.0, traditional shop floor management concepts are changing to new and digitally supported approaches for the coordination and management of production at the shop floor level. In order to investigate this change, the research project “Smart Shopfloor” is aiming to develop a software prototype for production management in the era of Industry 4.0. Requirements of industry are collected through literature review and workshops with industry and are translated into software functionalities. These functionalities lead, via an Axiomatic Design (AD) decomposition to design solutions for Smart Shop Floor Management systems. The results of the AD study form the basis for the software architecture and the definition of core and add-on functionalities of the software prototype. The focus of this paper lies on the AD decomposition of the design concept and further gives an overview of potential functionalities. In future, the developed concept will then be implemented in a lab environment before implementing and testing it in industrial case studies.

Proposal of 5-methoxy-N-methyl-N-isopropyltryptamine consumption biomarkers through identification of in vivo metabolites from mice

New psychoactive substances (NPS) are a new breed of synthetically produced substances designed to mimic the effects of traditional illegal drugs. Synthetic cannabinoids and synthetic cathinones are the two most common groups, which try to mimic the effects of the natural compounds 9Δ-tetrahydrocannabinol and cathinone, respectively. Similarly, synthetic tryptamines are designer compounds which are based on the compounds psilocin, N,N-dimethyltryptamine and 5-methoxy-N,N-dimethyltryptamine found in some mushrooms. One of the most important tryptamine compounds found in seizures is 5-methoxy-N,N-diisopropyltryptamine, which has been placed as controlled substance in USA and some European countries. The control of this compound has promoted the rising of another tryptamine, the 5-methoxy-N-methyl-N-isopropyltryptamine, which at the time of writing this article has not been banned yet. So, it is undeniable that this new substance should be monitored.5-methoxy-N-methyl-N-isopropyltryptamine has been reported by the Spanish Early Warning System and detected in our laboratory in two pill samples purchased in a local smart shop. This has promoted the need of stablishing consumption markers for this compound in consumers’ urine. In the present work, the metabolism and pharmacokinetic of 5-methoxy-N-methyl-N-isopropyltryptamine has been studied by an in vivo approach, using adult male mice of the inbred strain C57BLJ/6. The use of ultra-high performance liquid chromatography coupled to high resolution mass spectrometry allowed the identification of four metabolites. After the pharmacokinetic study in serum and urine, the O-demethylated metabolite and the non-metabolised parent compound are proposed as consumption markers in hydrolysed urine. Data reported in this work will help hospitals and forensic laboratories to monitor the consumption and potential intoxication cases related to this tryptamine.

Using autonomous intelligence to build a smart shop floor

The vision of smart shop floor is based on the notion of Industry 4.0 that denotes technologies and concepts related to Cyber-Physical Production Systems (CPPS). In smart shop floors, CPPS monitors physical processes, creates a virtual copy of the physical world, and makes decentralized decisions. CPPS allows virtual world to store data, process data, communicate, and cooperate with each other in real time. This paper presents architecture of smart shop floor based on physical, logical, and communication layers that embed intelligent approaches within manufacturing processes. Every physical entity in the smart shop floor is regarded as an autonomous intelligent logical unit that performs operations guided by distributed control functions. Moreover, computing power and optimization approaches are embedded into each logical unit to make decisions to agilely respond to frequent occurrence of unexpected disturbances at the shop floor. A test platform has been set up to demonstrate how physical entities can be cooperative and autonomous logical units that can automatize the shop floor operations. The results verify the feasibility and efficiency of the proposed method.

Using Autonomous Intelligence to Build a Smart Shop Floor

The vision of smart factory is based on the notion of Industry 4.0 that denotes technologies and concepts related to cyber-physical systems and the Internet of Things (IoT). In smart factories cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, cyber-physical systems communicate and cooperate with each other in real time. This paper presents a smart factory architecture based on communication and computing layers that embed scheduling mechanisms within a mechanical shop floor. Every physical entity in the shop floor is seen as an autonomous intelligent agent that performs tasks guided by dynamic scheduling functions. A test bed has been set up to show how physical entities can be cooperative and autonomous units that can automatize the shop floor operation processes. The results verify the feasibility and efficiency of proposed method.

Uyuşturucu Maddelerde Yeni Trend Sentetik Kannabinoidler

Doğal kannabis (Δ9-THC, tetrahidrokanabinol) hint keneviri bitkisinden (Cannabis Sativa) elde edilir ve CB1, CB2 olarak ifade edilen kannabinoid reseptörler üzerine etki eder. Kannabisin tedavi edici etkileri çok eskiden beri bilinmektedir. Günümüzde tıbbi amaçla kullanılan kannabinoid içeren bazı ilaçlar bulunmaktadır. Sentetik kannabinoidlerin kimyasal yapısı doğal kannabisten oldukça farklıdır. Kannabinoid reseptör afiniteleri ve aktiviteleri doğal kannabisten yüksektir. Sentetik kannabinoid içeren maddeler genel olarak yurdışında “Spice”, “K2”, Türkiye’de ise “Bonzai” ya da “Jamaika” olarak adlandırılmaktadır. Sentetik kannabinoidleri içeren bitkisel karışımların bazı ülkelerde “head shop” ve “smart shop” denilen yerlerde yasal olarak satılması ve bunlara internetten kolayca erişilebilmesi kullanıcılar için çekici bir özelliktir. Ayrıca kannabisten daha güçlü bir etki, ekonomiklik, kolay ulaşılabilirlik ve standart madde testlerinden kurtulması, sentetik kannabinoidlerin artan kullanımına katkıda bulunmaktadır. Sentetik kannabinoidlerin referans standartları bulunmadığından tespit edilmeleri kolay değildir. Yasal engelleri aşabilmek için piyasaya sürekli olarak yeni kannabimimetik analoglar sunulmaktadır. Sentetik kannabinoid kullanımındaki artış göz önünde bulundurulduğunda, yakın zamanda en problemli uyuşturucu maddelerden biri olacağı öngörülmektedir. Sentetik kannabinoidlerin yaygın kötüye kullanımı nedeniyle, farmakoloji ve toksikolojilerinin daha iyi tanımlanması ve uygun yasal planlama ile düzenlemelerin yapılabilmesi için daha ileri düzeyde incelenmelerine gerek duyulmaktadır.Anahtar kelimeler: Sentetik kannabinoid, bonzai, uyuşturucu madde.

Smart shop floor scheduling using knowledge based simulation

In this paper, the need for knowledge-based simulation technique in shop floor scheduling are addressed. A prototype integrated system in Feed Mill manufacturing utilizing an integrated approach of Artificial Intelligence (AI) and simulation is discussed. The system is designed to support the production planner in scheduling and controlling the shop floor on real-time and on-line basis. System overview with the emphasis on knowledge-based simulation module is described.