Currently the SMeART consortium is preparing so called guidelines in order to support SMEs during their transformation process towards „Smart engineering companies“. Professor Leisenberg is manager of the project 
The SMeART project team with Prof. Leisenberg (in the centre)
For the above guideline publication we are trying to find valid scientific descriptions and definitions for related terms. In this regard I have found an interesting paper: „Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives“ (see reference). Here, the author would like to take the opportunity to introduce some thoughts of this publication:
In an extended introduction the article describes the environment very detailed: “…new
technologies are nowadays penetrating manufacturing and serving as key enablers for the
manufacturing industry to address current challenges such as increasingly customized requirements,
higher quality, and shorter time-to-market by converting manufacturing systems onto a smart level.
For example, by being deployed sensors (e.g. machine tools), manufacturing equipment can self-sense,
self-act, and can also communicate with each other”. In addition it says that ” it is possible to capture and share real-time production data, which could be used for rapid and accurate decision-making. In particular, the connection of physical manufacturing equipment and devices over the Internet together with big data analytics in the digital world (e.g. the cloud) brings about are revolutionary production pattern – Cyber-Physical Production Systems (CPPS)”.
Section 2 of the above paper presents a framework for Industry 4.0 smart manufacturing systems. In Section 3, a number of demonstrative scenarios are presented. Section 4discusses the current challenges and future perspectives. Section 5 concludes the reviewed paper.
The proposed framework of Industry 4.0 smart manufacturing systems in which research topics are categorized into smart design, smart machining, smart monitoring, smart control, smart scheduling,and industrial applications consists of:
* Smart design
* Smart machining. In Industry 4.0, smart machining can be achieved with the help of smart robots and various other types of smart objects that are capable of real-time sensing and interacting with each other.
* Smart monitoring
* Smart control
* Smart scheduling
* Industrial applications
In chapter 3 DEMONSTRATIVE SCENARIOS are described:
* For Smart Design a UX-based Personalized Smart Wearable Device: With this example customers become more actively involved in the product design process to co-creating personalized products with better UX and satisfaction, which is known as the manufacturing paradigm of mass personalization
* For Smart Machining CPS-based Smart Machine Tools: CPS-enabled smart machine tools are used for producing physical products. CPS are capable of bringing together the virtual and physical worlds to create a truly networked world in which intelligent objects communicate and interact with each other
* For Smart Monitoring Energy Consumption Monitoring: A cloud-based smart control system is proposed which takes CNC control for example to illustrate the key concepts. All of the non-real-time tasks will be executed in the cloud. Machining jobs are scheduled and distributed among connected machine tools taking into account their capability and availability, which are treated as local manufacturing resources. A local operator is also able to start a machining by logging a part programme. The cloud is able to interpret the part programme no matter it is in G/M code or in STEP-NC.
* For Smart Scheduling – Machine Scheduling in Smart Factories: Based on smart machines, smart monitoring (e.g. energy consumption monitoring), and smart control system from cloud, smart machine scheduling can be achieved. Machine scheduling is a classical problem that has been studied for decades, and in the context of Industry 4.0, there are a number of new characteristics and requirements. In Industry 4.0, machines are endowed with a certain degree of intelligence and can communicate with each other by being deployed various sensors and wireless communication devices (e.g. RFID).
* For Industrial Implementation – Smart 3D-scanning for Automated Quality Inspection: The inspection process begins with scanning an object and creating 3D files of points, called point clouds, as raw input. By means of filtering process, unreliable range measurements (outlier) are removed. Then point clouds are analyzed and compared with initial design. Finally, the results are visualized with different colors to show the degree of quality of each segment of the part. Data gathered from each process is stored inbig data storage. By using big data analytic tools, control chart, mathematical statistics knowledge and intelligent algorithm, the data can be processed to provide valuable information for manufacturers and customers. This system is also connected to the internet to provide real-time quality data of the processing parts or finished workpiece online for customer access.
Finally, the paper focusses current challenges and perspectives and provides hints on future of real-time data collection in manufacturing systems.
Importand contributions of this paper, that might be applied for the SMeART project, are as follows:
* Systematic framework for Industry 4.0 smart manufacturing systems is proposed, covering a number of relevant topics such as design, machining, monitoring, control, and scheduling. This framework provides an important reference for academia and practitioners to rethink the essence of Industry 4.0 from different perspectives.
* The paper reviews the key perspectives under the proposed framework. Insights for the future research directions of data collection, virtualization, and decision making are provided. It can provide the SMeART project with some insights into implementing Industry 4.0 and smart engineering.
Feedback and opinions are always welcome, Manfred Leisenberg
Reference: Zheng, P., Wang, H., Sang, Z. et al. Front. Mech. Eng. (2018) 13: 137. https://doi.org/10.1007/s11465-018-0499-5