Modular Cell 1 – 3
The STORAGE MODULE by Pilz is an intelligent, automated warehousing module, used to store the workpiece transporters: If the central server sends a new work order to the module, thereby, requesting an additional transporter, the storage module supplies an empty workpiece carrier from storage. Empty workpiece carriers or semi-finished products can also be returned to the warehouse.
1b. Bottom Engraving
The module Bottom Engraving by Festo initializes the digital product memory to a specific production order via RFID. The production order is loaded from a Web Server of the superordinate Enterprise Resource Planning system (ERP) via http-protocol by means of a specially developed Web Client. Corresponding to the product memory, an individual engraving is performed via a CNC engraving control.
1c. Clip Mounting
In the following production step, the CLIP MODULE by Bosch Rexroth mounts a retaining clip to the plastic bottom.
1d. Force Fitting
The FORCE FITTING module by Harting performs the central mounting of the two housing parts. The bottom with a mounted retaining clip is assembled with the cover chosen in one of the two available colors, as per customer request. The Harting module puts the lid on the base plate with a robot and embosses both parts together.
1e. Laser Marking
The module LASER MARKING by the company Phoenix Contact uses a laser system to put an individual engraving on the topside of the business card holder. The engraver displays the digital business card as QR code. Individual data can be flexibly changed upon the client’s request right up to the corresponding process step.
The WEIGHING MODULE by Mettler Toledo determines the weight of each product by means of a precision scale. The metrological quality control compares the product memory with the actual assembly status.
1g. Quality Control
The module QUALITY CONTROL by Lapp Kabel performs two tasks: Product end control by means of a high-resolution camera as well as the final output of the finished business card holder.
Flexible Transport Systems (FTS)
The ROBOT PLATFORM by Festo is a self-directed transport system, responsible for flexible material transport between the various production lines and the manual assembly station. Additionally, the Flexible Transport System (FTS) is equipped with an optical quality control system from Huawei. The use of 5G wireless technology enables a fast, secure, and location-independent linking of quality data to the service provider cloud.
That is why the infrastructure of the Industrie 4.0 production plant was given a new scalable, star-shaped design concept this year. The infrastructure nodes support independent production cells consisting of several stations. The computing power provided by edge devices in these nodes is now available for industrial intelligence applications. This solution illustrates the next step towards the practical introduction of a standardized module interface. The production plant can now be quickly and flexibly reconfigured. In addition, a tablet display at the nodes enables a near real-time visualization of the captured process data. Another feature of the new infrastructure nodes is the real-time data transmission over the TSN network technology (Time-Sensitive Networking). Data is now prioritized using TSN. Through this prioritization process, flexible decision-making is possible about what data must be immediately available and where delayed transmissions are acceptable. TSN guarantees, for example, that critical safety data will be sent with the highest priority and will arrive even if the network is heavily loaded. This ensures that even in an overloaded network, reliable processing continues at that module. The new infrastructure nodes were successfully developed in cooperation with our partners B&R, Huawei, Phoenix Contact, Bosch Rexroth, and Weidmüller.
The human in the Industrie 4.0 environment
The robot platform simultaneously connects the process to a MANUAL WORKSTATION from MiniTec. A perfectly designed ergonomic workplace, enabled with Internet and communication technologies, supports the worker in completing assembly activities. The Augmented Reality technologies developed at SmartFactoryKL are provided to carry out individual process steps or an entire production process manually. Augmented Reality – the super-positioning of real-time images with suggested actions – promises many advantages, particularly, for training processes and manual assembly tasks. A built-in RFID read/write device allows the worker to retrieve the current production progress of the product as well as individual customer information. Augmented Reality technologies assist in the completion of variable tasks.
In future manufacturing operations, ARTIFICIAL INTELLIGENCE (AI) is sure to be a major partner to the operators. Capable of quickly and reliably evaluating the increasingly large data volumes collected in modern production plants, it can provide filtered, context-related results to the operators. The deployment of AI in the Industrie 4.0 environment is demonstrated in several use cases at the SmartFactoryKL production plant:
For demonstration purposes, an anomaly during operations is generated in the form of an unusual vibration at one of the stations of the Industrie 4.0 production plant at SmartFactoryKL. An edge device records the vibration and transmits the values to the cloud. The signal is analyzed there with the aid of AI algorithms to detect the anomaly.
Another simulated anomaly, this one appearing as a grinding noise, is also generated. Acoustic recordings are continuously made of the noise environment within the production stations and then analyzed by AI algorithms. AI is able to classify any unusual noise and recognize it as an anomaly.
A third use case is based on cameras installed at the stations that film the production flow. This also results in the accumulation of large amounts of data, which can only be meaningfully evaluated by AI. This task is carried out by an intelligent computer vision service, which recognizes and analyzes the workpiece to include its processing status. Using this data, the computer vision AI algorithm determines if the processing step was carried out error-free. To specify the adaptive learning process, various well-trained models of the AI algorithm are used.
A key tool in the practical implementation of the findings obtained from AI is the technology known as AUGMENTED REALITY (AR).
Integrated IT systems for the Industrie 4.0 production plant
5. Enterprise Resource Planning System- ERP
The Industrie 4.0 demonstrator uses the integrated ERP-SYSTEM manufactured by proALPHA to control the processes and ensure transparency. The software is capable of integrating the layers of the classic automation pyramid and provides a user interface for the customer. The customer uses the product configurator at the unit and, when linked to a web service, can enter intuitive and site-independent orders directly from the browser – down to lot size 1. Feedback from the system is displayed in real time in the job order and the customer receives a current status of job progress. In addition, the ERP system automatically generates service orders triggered by AI-based anomaly detection. Service orders and other helpful information on maintenance are then passed on, for example, via a tablet to the technician. Any related feedback from the technician is also recorded and stored in the system.
EPLAN Software & Service is developing cloud-based ENGINEERING in an Industrie 4.0 environment. An interdisciplinary management approach to the automation of the individual production modules is critical for the design, efficiency, and operational safety of Cyber Physical Systems (CPS). The documented control system for the entire supply chain and the complete product life cycle is used as a comprehensive consistent database. The goal is to consider insights from the entire supply chain early in the design phase or the PLM process (product life cycle management). The generated documentation, for example, is suitable for a professionalized maintenance scenario or to optimize the energy balance.
7. Data Analytics
The plant data collected and provided by the integration layer is collected by KIST‘s DATA ANALYTICS product. This enables the generation of chronological data models, which are used to derive analytical insights, for
example, about the behavior of a component prior to recurring maintenance cases. Bottlenecks, rejects, rework, and downtimes can all be avoided at an early stage by comparing the historical sensor data with live data from
the plant. Over the course of time and the increasing amount of data, the insights and statements have more and more precision and the process flow can be constantly optimized.
8. Modular Certification / Safety
The CERTIFICATION CONCEPT of TÜV Süd is designed for the certification of a modular and, consequently, a constantly changing production line. The certification is explained using the example of the flexible transport system: The moment the transport system docks to a production cell, it is assigned to this machine group. An emergency stop triggered at one part of the assembly module only causes an emergency stop of the transport system when it is part of this group. Besides enabling the connection of new modules with little effort, this safety concept also ensures that only relevant sections of the system are stopped in the event of a safety-related shutdown. The unaffected production lines continue to operate without any risk of operational safety.
9. Integration Layer
The INTEGRATION LAYER of SmartFactoryKL is achieved with the help of IBMs Watson IoT platform. The platform forms the central interface between OT (operation technology) and IT (information technology) and serves as a cross-layer instance for connecting the manufacturer‘s clouds. In terms of the vertical integration of all production processes, the integration layer takes on the role of a compiler: simultaneously, exercising data sovereignty over the shop floor. It captures content, protocols, and data formats from the plant (for example, condition data from the modules and Edge Devices) and, in a targeted manner, forwards them to the associated cloud. In addition, various analytical, cognitive, and DevOps services from the Watson IoT platform are used to map aspects like operational excellence, predictive maintenance, and quality assurance in a multi-cloud scenario in the context of horizontal integration. This makes it possible to show the product, all modules, and even decisions from the IT systems to the worker in real time (Digital Twin).
The SmartFactoryKL production plant employs multiple cloud-based computing solutions:
COMPANY CLOUDS from Phoenix Contact and METTLER TOLEDO are connected to the corresponding edge devices. The training of the adaptive AI systems takes place in the cloud-based solutions from IBM. The SmartFactoryKL CLOUD, as a higher-level cloud platform, consolidates the data from the manufacturer clouds, checks it, and distributes it to appropriate targets – for example, to smart data glasses.
The SERVICE PROVIDER CLOUD is connected directly via 5G technology to the flexible transport system. It performs a quality control check using the optical recognition data from the workpiece. Early detection of potentially defective products enables their removal from the production flow.