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The trillion-dollar automotive industry is currently facing multiple, far-reaching changes. These changes are set to drastically reshape the way vehicles are manufactured and distributed in the near future. They include automation, Industry 4.0, electrification and additive manufacturing. Increasing competition from new automotive companies that are small enough to quickly incorporate these radical new technologies and systems into their manufacturing activities, are applying significant pressure to larger, more established brands. Whether it be for startups or established titans of industry, new manufacturing machinery must be purchased. These more advanced machines must be capable of easily integrating with new, game changing technologies.
The manufacturing industry of the future is one that will be characterised by the integration of more efficient hardware systems. Examples are robotics, that can work seamlessly alongside human technicians, as well as advanced software systems like machine learning (ML) and artificial intelligence (AI) that can be used to dramatically improve efficiencies and production rates. In order to stay abreast of these new systems, new machines are required that can integrate seamlessly into these more advanced factories. It is therefore critical to select machines that will help grow manufacturing activities, while also ensuring they are able to adapt to current and unseen technologies.
This whitepaper will address the main factors that need to be considered when purchasing capital equipment for the automotive sector, keeping in mind these new and emerging technologies.
Industry 4.0 is the integration of physical and cyber technology in a manufacturing and distribution setting. As factories become more data-centric, machines will need to be able to integrate with software systems that are designed to collect a wide array of very specific data. This can be anything from cycle time, temperature build up, tool life, etc. The data is collected across multiple manufacturing lines that can then be used to optimise the manufacturing of individual parts, as well as plant-wide systems and processes. This data is typically analysed by machine learning algorithms that can look at large data sets and extract actionable information which can then be reviewed by a plant manager and the recommended changes implemented. More radically, the plant can implement incremental changes to processes in order to increase efficiency without any input from a human operator. In order to gain the most benefit from this trend, machines need to be ready to not only supply the data required to make these optimisations, but also be receptive to adjustments based on the analyses of this data.
In 2007, less than 1000 electric vehicles were sold, in 2017 this number had grown to 1 million, the year after that it was 2 million, with 2019 predicted to top almost 3 million electric vehicles sold. This amounts to only 2% of the total number of vehicles sold globally, however this does not mean that electrification is a short-lived trend that will soon fade. In fact many major governments are outright banning the manufacture of new internal combustion vehicles and implementing emission rules in major cities. Some of the more optimistic predictions of electric vehicle market adoption state that it will grow to 50% of total vehicle sales by 2033.
Electric vehicles have far fewer moving parts than a typical internal combustion vehicle and thus the volume of parts being manufactured will reduce just as certainly as their nature will also change. One of the major differences when it comes to electric vehicles is that automakers now have the opportunity to not only build the vehicle, but also provide the energy source. Companies that do not invest in battery tech will be outmanoeuvred by those who produce their own batteries. The UK is aiming to become a leader in battery manufacture in order to reinvigorate its manufacturing sector on the back of the new electrification wave.
The general understanding is that automation begins and ends with robotics; specifically robotic arms. However, this is far from the entire picture. Automation aims to remove human interaction from as many processes as possible – from material loading, machine programming, part removal, assembly, quality control, optimisation to self-correction. The removal of humans from processes that they are verifiably bad at – like repetitive, high speed and long duration work – is the goal of automation. Automation, if applied correctly, can drastically improve the efficiency and output of a manufacturing plant. If done incorrectly it can overcomplicate the process by having too many interlinking systems. It is in fact entirely possible to over-automate a factory. Many new machine tools have autonomous systems built into them from day one, such as auto tool changers, bar feeders, part conveyors and robotic part pick and place systems.
Additive manufacturing technology
Additive technology has been around for decades, and despite the brief popularity and ultimate stagnation of consumer-focused machines, industrial additive manufacturing (AM) has steadily been growing in capability and production volume. It must be noted that AM is not considered a high-production, volume technology that can keep up with traditional machines like CNC mills, metal-injection moulding machines and stamping. However, the move towards design by algorithm has resulted in lightweight parts that are complex and difficult to machine; this is where additive machines are beneficial. It is highly unlikely that an entire vehicle body will be additively manufactured because alternative and cheaper technologies are simply too fast and efficient. However, parts like suspension wishbones are definitely on the cards.
There is no doubt that upgrading the machinery in an automotive machine shop is an expensive endeavour. A common thought process is that in order to justify the purchase of a machine it needs to be able to pay for itself in as short a period as possible. However, this is not necessarily the best way to approach the problem. It is important to determine the future cost per manufactured part instead of the initial capital investment. It may seem like a good idea to buy a cheaper machine as it can pay for itself a lot faster than a more expensive and capable machine, however the cost per part will start out being very low but over time, as the cheap machine fails and needs repairs and replacement parts, that cost per part will gradually climb. It will climb much faster once lost production output is factored into the cost per part. Soon the cost per part will overtake that of a more expensive machine that has been consistently producing parts reliably and without fail at a stable cost. Therefore, it is important to purchase machines that will allow for the biggest financial savings in the long term.
Labour costs are becoming less of a concern in advanced manufacturing as the level of automation increases. Low labour cost manufacturing hubs initially gained an advantage in the manufacturing industry by using cheap, unskilled labour, like an automated plant uses robots. This cheap labour force could be deployed en masse to outperform other manufacturers in countries where labour laws were stricter. As automation becomes more economically and practically viable, countries that historically dominated manufacturing can begin regaining their place in the market by leveraging their higher levels of technology and a highly trained and efficient workforce.
It is important to not only analyse the cost of a machine but to also take note of the lifetime cost of the machine in terms of spare parts and maintenance. A machine may seem cheap at first but over its total lifespan as parts fail and production time is lost, the cost becomes much higher than a more expensive and better built machine. Another major point of consideration is the availability of spare parts. Cheaper machines are typically made by companies that do not have far reaching logistics networks like the larger machine makers. This means that if a cheap machine breaks down (which it will), the parts will take far longer to arrive.
Furthermore, the cost of regular maintenance also needs to be considered both in terms of machine downtime and the costs and availability of service technicians and the recommended replacement parts.
An efficient and capable customer support team is critical when deciding on a new machinery purchase. The automotive industry is renowned for its high production rates. Therefore, the loss of production on a critical machine can have a knock-on effect that results in much larger production loss down the line. As such, it is critical to select a machine supplier that has a track record of fast response times in terms of technical support as well as a spare parts division that keeps stock of the more critical components and has an efficient logistics network to get replacement parts where they need to be as quickly as possible.
Customer support is not only related to troubleshooting and spare parts. Good customer support also refers to training of operators on new machines to ensure they are able to operate the machine at peak efficiency. A machine is only as effective as its operator’s knowledge and expertise.
With the current pace of technological progress, it is difficult to buy machines based on predicted industry trends, so major industries require machines that are adaptable. The rapid rise of electric vehicles was not anticipated to be on it’s way to dominating the automotive industry until relatively recently. As such, decisions made to buy machinery best suited to internal-combustion-based technology did not age well. It is notable that the UK automotive industry is currently experiencing itslargest downturn since 2012 – so manufacturing companies need to be able to diversify to other sectors while the industry re-tools for the electric revolution. The large reduction in metal parts between internal combustion and electric means that companies may be overcapitalised in terms of machinery. The key is to focus on machinery that can be adapted to many different workflows from different industries.
The key to survival in a highly competitive market is to out-innovate the competition. Nothing converts more consumers to a product than a well-implemented innovation programme. A key example of this is how Tesla vehicles are eroding the luxury vehicle market in the USA. This is because Tesla delivers an innovative product that offers more than the traditional luxury brands did. The established brands were certainly innovative in various aspects, but the fact of the matter is they were out-innovated and on a larger scale than they were used to. It is important to have a machine shop that is capable of keeping up with a company’s innovation. It’s great to have a highly effective innovation programme, but if those innovations cannot be converted from concepts into products, other companies will take that innovation and implement it into their own products. A highly flexible and capable machine shop is a crucial ingredient to keeping on top of innovation.
Choosing the correct machine to meet your production throughput requirements is a critical part of machine selection. Some machines are better suited to high volume manufacture where others are designed to optimise machine time by increasing the number of spindles that are engaged on the part at any one time. Multi-spindle machines can typically produce parts 8 times quicker than single-spindle machines. Furthermore horizontal mills can allow for multiple setups to be undertaken in one operation – then the spindle can move to each fixtured part, thereafter the finished parts can be removed without interrupting manufacturing.
When purchasing a new machine there are some key features to consider based on the application, such as:
Machine Speed – The speed of a CNC machine determines how quickly it can move the tool through the work as well as how quickly it can move between operations like changing tools and moving the tool to different areas of the part. Typically, the time a tool spends engaging the work accounts for the largest percentage of time.
Accuracy – The more rigid a machine and the higher the quality of its linear actuation hardware, the more accurate the parts it can produce.
Production – The type of production will determine the style of the machine; batch manufacturing requires a different style of machine from continuous manufacturing.
Repeatability – The quality of the thousandth part must ideally be the same as that of the first part.
Reliability – The reliability of a machine is extremely important in high production throughput applications. A breakdown during a production run can result in significant financial losses.
Tool capacity – The more tools a machine can store, the more efficient it will be. The machine will spend more time cutting metal if it does not have to be stopped for a tool change by an operator. Large tool libraries allow for multiple operations to be run without stopping the machine.
Flexibility – Highly specialised machines are not always a good idea. If market requirements change then you will be stuck with an expensive and redundant machine. Machines that are highly flexible can more readily adapt to these changes. This capability is a big part of Industry 4.0 principles; if a machine breaks down the work can be quickly distributed to other machines with minimal loss of production. This would be impossible with a factory full of highly specialised machines.
Autonomy compatibility – Machines must be able to integrate with autonomous hardware and software to be ready for Industry 4.0 factories. For example, machines with conveyors are ideal as they can seamlessly integrate with systems that allow for continuous operation.
The manufacturing industry is undergoing a tidal wave of innovation and the resulting changes it brings. Automotive manufacturing companies are by no means immune. It is important for any OEMs, or automotive parts manufacturer in the industry to remain in touch with the raging current of change and be able to react at a moment’s notice. Kingsbury offers all the hardware and expertise required to become a master of the disruptive technologies that are set to change the world of manufacturing forever.