Spread the love

There are new possibilities offered by big data, 3D printing, machine learning and augmented reality in the manufacturing industry. Leveraging on these into a new way of doing business is a key factor in Industry 4.0 to gain a competitive edge, and for companies to be more profitable and scalable. By Farah Nazurah Binte Mohamed Sidek

The global Industrial Internet of Things (IIoT) market is expected to reach US$195 billion by 2022, growing from US$113 billion in 2015, at a compound annual growth rate of 7.89 percent between 2016 and 2022, according to a market research report by Markets and Markets. A key factor for the growth of the IIoT market is the need to implement predictive maintenance techniques in industrial equipment to monitor their health and avoid unscheduled downtimes in the production cycle.

In the metrology segment, the view for Industry 4.0 in terms of part inspection is to increase quality and maximise throughput, whilst reducing costs right down the production line, making manufacturing processes faster and more accurate. Multisensor metrology alone is not enough to seize the maximum potential of the production line; instead, an integration of autonomous processes and hardware as well as complete connectivity is needed to fully embrace manufacturing of the future.

Five Industry 4.0 trends will be discussed in this article—from big data, predictive maintenance, augmented reality, digital twin to cyber security. Industry players should be aware of these trends as they have already begun to affect many aspects of industrial automation going forward. Industry 4.0 is the no longer the future of the industry, and the time is now for companies to implement intelligent manufacturing practices.

1: Data/Data Analytics

Big data describes the large volume of data, both structured and unstructured. Insights from big data can enable better decisions to be made — deepening customer engagement, optimising operations, preventing threats and fraud, and capitalising on new sources of revenue.

The majority of the newly created data between now and 2020 will not be produced by people, but by machines as they communicate with each other over data networks. The insights gained from big data analytics and the IIoT to drive greater manufacturing intelligence and operations performance is considered essential by 68 percent of manufacturers, according to a recent survey by Honeywell. This highlights that manufacturers are increasingly aware of the importance of big data analytics and its potential in the industry.

Exponential Growth In Big Data

The global big data and business analytics market will grow to US$203 billion over the next few years, according to a report by International Data Corporation. The growth forecast for the global big data and business analytics market through 2020 is led by manufacturing and banking investments.

The rate at which data is being generated is rapidly outpacing the ability to analyse it, according to Dr Patrick Wolfe, a data scientist at the University College of London. It is key to turn these massive data streams from a liability into a strength. The complex nature of the information created requires solutions capable of addressing data security, privacy and flexibility issues.

Mazak, a machine tool manufacturer, has developed its own data collection and analysis system called Mazak SmartBox to connect machine tools securely and intelligently. The company also uses its own system at its facility in Kentucky, US; and the system is able to perform sophisticated analytics and improve part manufacturing processes, whilst having advanced cyber security protection.

Data Pooling

Data pooling refers to the integration and sharing of data. For manufacturing companies, merging data into one large pool has the capability to create insights that could not be obtained from data relating to just a single operation.

To lessen risks linked to data pooling, companies could choose to have their data sent to a third-party platform and get back compiled data without a trace of which company it was derived from. Companies could also limit the type of information they share — automotive companies could decide not to divulge information that would reveal information on their technology to rival companies. They should consider the balance between the price of the services offered by such platforms, and the value of the data that is being provided to them.

Data Analytics To Reduce Machine Downtime

A survey of manufacturing executives in the US by Honeywell revealed 67 percent of respondents have plans to invest in data analytics. The executives viewed data analytics as a fundamental component of the IIoT, and as a solution to unplanned downtime and lost revenue.

The survey revealed companies are feeling pressure to continue working under threats of unscheduled downtime and equipment breakdowns, which was viewed as the most crucial factor in maximising revenue. Employing data analytics to ensure machines are kept running at optimum level could vastly reduce and even eliminate unplanned downtime.

2: Predictive Maintenance

When repairs and maintenance are planned, it could save manufacturing companies 12 percent in cost savings, whereas a loss as much as 30 percent could be incurred when unplanned repairs occur, according to research by the World Economic Forum and the consultancy Accenture. Predictive maintenance foresees when equipment breakdowns might arise, and it prevents machine breakdowns by carrying out maintenance. With predictive maintenance, manufacturers can lessen maintenance and servicing costs, and boost reaction times within disruptive production processes.

The unchanging objective in metal cutting manufacturing is to further increase productivity, creating added value for the customer. Heller, a milling machines and systems manufacturer, has developed its own system to improve transparency of its current machine status, by evaluating data to allow purposeful diagnostics which yields higher productivity and reduces machine downtimes. The visualisation of specific information, including status displays of axes, spindles or other assemblies, enables users to determine wear and take preventive measures in order to avoid unscheduled downtimes.

Real-time Condition Monitoring

Machine and sensor data can be catalogued and displayed in real time using Industry 4.0 software, which provides support for condition monitoring. Data visualisation is not confined to the control station and can be accessed on any platform everywhere — from tablets, smartphones, and bigger screens, both on the production floor and in the cloud.

When the software has determined an imminent maintenance task from the pre-set specifications, the information would be sent immediately to maintenance staff. After maintenance has been carried out, staff can note down tips to improve subsequent maintenance works.

Flexible Evaluation Options

Data is assessed using highly customised rules and the analyses is easy to understand for production planners and maintenance staff. The software available today is designed for humans in mind, unlike before when software design was for a machine to machine communication. Limit values and rules can be specified to ensure unscheduled machine stoppages are displayed immediately and notifications are sent to maintenance staff.

3: Augmented Reality

Augmented reality (AR) is an enhancement of a real-time display using real images alongside computer-generated information. AR is associated with Industry 4.0 practices relating to smart manufacturing and has tremendous potential to influence manufacturing industries.

With augmented reality, challenges which arise with conventional 3D measurement can be eliminated. For example, with Keyence’s XM Series handheld probe coordinate measuring machine, it allows for an operator to perform intuitive 3D measurements with high accuracy. The process is carried out on an onscreen interactive visual guide and touch probe.

Augmented-reality guidance images are created automatically, and the system overlays the measurement points along with their 3D elements. Shared programmed work instructions and measurement promotes consistent measurement regardless of the operator, environment or other circumstances.

Potential Usage Scenarios

AR has numerous uses, involving different types of operations that can be executed on the factory floor — manufacturing activities such as production, and support processes such as maintenance and training.

“Some companies are concerned or even hesitant to adopt industry 4.0 practices as they are not even at the Industry 3.0 stage. However, it is possible to jump straight from Industry 2.0 to 4.0. For example, to improve standard operating procedures among plant staff that are still using physical papers for instructions, they could make use of augmented reality to simplify and learn new procedures,” said Lim Yew Heng, partner and managing director, The Boston Consulting Group.

  • Operations: any kind of operation which requires some step by step procedure can benefit from the adoption of AR — installation, assembly and machinery tool change
  • Maintenance and remote assistance: AR is efficient at reducing execution times, minimising human errors and sending the relevant performance analytics to maintenance staff
  • Safety management: AR allows risk and safety of operators and equipment to be managed
  • Design and visualisation: AR provide tools that improve design, prototyping and visualisation in the design phase
  • Training: for companies where training is a critical process involving many field technicians, AR-guided training can be effective at training staff, especially in the beginning where there is a learning curve
  • Quality control: AR support in quality control processes enables staff to determine if products meet manufacturing standards

“Everything you do on the digital front has to be anchored around the company’s business objectives and strategies; and how it creates value for your business. Companies should be clear on why they want to adopt Industry 4.0 practices for their businesses,” Mr Lim continued.

4: Digital Twin

A digital twin is a virtual copy of the factory or product parts to enable companies in performing simulation, testing, and optimisation in a virtual environment before dedicating actual resources. The primary benefit of the digital twin is to provide a comprehensive outlook of the project at any time in the entire span of a product lifecycle. Moreover, it allows collaboration across different departments, and even outside the organisation.

There is a distinction between AR and virtual reality, in that AR relates more to smart manufacturing as it more connected to the physical world. The digital twin approach is built on three foundations — a physical product in real space, a virtual product in virtual space, and the connection of data and information that ties the virtual and real products together. Due to leaner development cycles and increased collaboration, both internally and externally with suppliers and partners, manufacturing companies have been able to cut development time on products by 25 percent, translating to cost savings of 10–15 percent.

Companies like General Electric have developed technologies and processes to create a digital twin, which is practised in their own manufacturing and design service businesses. As an example, for their customers in the aerospace industry, they have created a digital twin model of engines at the time of design and engineering, with the same model employed throughout all phases of product development and product lifecycle management. The company works with their airline customers to help them operate more effectively through the digital twin of its aeroplane parts.

5: Cyber Security

The integrated nature of Industry 4.0-driven operations means that cyber attacks can have devastating effects, evident in the unprecedented ‘WannaCrypt’ global cyber attack in May this year. Cyber security strategies should be secure and fully integrated into organisational and information technology. Picking the right cybersecurity provider is essential in ensuring data is protected.

“Some of our clients have come to us and said they do not think they will be able to put up their data on the cloud as they have very sensitive data. Within their operating database, there are certain data that are more sensitive, and there are those which contain less sensitive information,” said Mr Heng, partner and MD, The Boston Consulting Group.

“They could start out with putting less sensitive data on the cloud and understand how it works first, and understand how cybersecurity providers can help them. From there, they can move towards a more balanced approach.”

Data Sharing: Increased Access To Data

Companies should consider which data should be shared and how to protect the systems, and which data that are proprietary or have privacy risks. Companies should leverage tools such as encryption for data which are at rest or in transit, to safeguard communications should they be intercepted or if the systems are compromised. It is important for manufacturing companies to perform risk assessments across their environment — including enterprise, DSN, industrial control systems, and connected products. Data evaluations should then be applied to update cyber risk strategies.

Protecting Data

Sensitive data are not limited to sensor and process information; it also includes a company’s intellectual property or even data related to privacy regulations. As more IoT devices are connected to networks, the risk of potential attack increases, along with risk from compromised devices. The first step companies should take is to discover all assets, especially industrial controllers. Picking the right cybersecurity provider who understands what your company needs is essential in protecting your data against cyber attacks. Transparency is important for companies with highly sensitive data, therefore, ensure that third-party cybersecurity providers inform you where the information goes.

The Right Strategy Is Important

“Companies have to ask themselves why they want to create a fully automated manufacturing factory, and what value it creates for the end users. Once the staff in the company knows why this is being done, it will change the company’s culture, and they will start focusing on value is delivered to the customers,” said Scott Maguire, global engineering director, Dyson. “At the end of the day, these are big investments, and companies have to plan strategies for the long-term and be willing to change their company culture.”

Manufacturing companies should embrace the positive disruptive changes that Industry 4.0 practices can bring. A digitalisation strategy which is tailored to your company’s needs should be mapped out; and disseminated to staff so they understand it is a part of the company’s new culture.

Mechatronics Drive System Ensures Positioning Accuracy Of CT Machine
Mechatronics Drive System Ensures Positioning Accuracy Of CT Machine

Related Post