Arogundade: Inside the Mind of a Modern Mechanical Engineer

Every engineering journey has a starting point, and for Ifeoluwa Arogundade, it began not in a high-tech laboratory, but in a university workshop at the Federal University of Technology, Akure. As an undergraduate, he worked on a patented coconut de-husking machine, an experience that introduced him early to the realities of design, fabrication, and problem-solving beyond textbooks.

Now focused on research in adhesive bonding and skilled in simulation and data analytics, Arogundade believes engineering success lies in lifecycle thinking, collaboration, and human judgment. In this interview with Oluchi Chibuzor, Arogundade reflects on why understanding the full product lifecycle matters more than ever, how simulation reduces costly failures, why preventive maintenance is a strategic business function, and which human skills will remain indispensable as manufacturing becomes more automated and data driven.

Can we meet you?

My name is Ifeoluwa Arogundade. I am a master’s student at Georgia Institutes of Technology where I major in mechanical engineering. I’m a research student where I work on adhesive bonding and sustainability for structural applications in automotives and aerospace.

My coursework has been very heavy on manufacturing systems, manufacturing technologies, precision metrology, and process optimization. Prior to this, I earned my bachelor’s in industrial productions engineering at Federal University of Technology, Akure, where I was part of a team that worked on a patent coconut de-husking machine. So, my background is a blend of hands-on mechanical engineering and data-driven decision making.

Recently, I was a maintenance engineer at Berger Paint where I managed preventive maintenance schedules and cycles, and I worked on equipment validation. I was also able to collaborate with teams like the production team, the engineering team, and the procurement team to achieve seamless and efficient production. Before that, I spent some years in the automotive space where I performed root cause analysis on complex mechanical systems and I learnt the importance of preventive maintenance on cars, especially the engines.

Now, I have combined my technical knowledge with data analysis because I was able to gain skills in SQL, Power BI, Excel, and other analysis tools. And this was what I used to land an internship role at Pricepally where I was a data analyst helping them visualize and transform their marketing data into actionable insights.

Your work spans design, simulation, manufacturing and data analysis. How important is it for today’s engineers to understand the full product lifecycle rather than just one specialty?

I think understanding the full product lifecycle is very essential for today’s engineers because engineering decisions rarely exist in isolation anymore. What I mean is, nowadays many things come into play to make decisions in engineering, starting from the design stage to the simulation stage, then the manufacturing stage and the commercialization stage. So, a design choice at the CAD, (which is the design stage), directly affects how easy this machine is to manufacture, how easy it is to maintain, how the machine is going to be sustainable, the cost and the recycling. From my experience, I have seen how faulty design assumptions can create long-term maintenance challenges.

I remember during my internship in the maintenance environment, many equipment failures were not caused by misuse but by the manufacturer’s design choices that did not consider accessibility or repairability on their machines. So, I think being able to combine the design, the simulation, and the manufacturing in a very effective manner goes a long way to determine how a product will survive and how a product will be able to have effective maintenance and sustainability at the end of the day.

And all these experiences shaped me as a graduate student and now I think intentionally about how components will be assembled, serviced, disassembled, and reused. My goal is to make structures very strong and make parts in a way that they can be maintained, and they are sustainable over a long period of time.

You use advanced simulation tools like FEA, CFD and COMSOL. How do these virtual tests reduce real-world failure, cost and waste?

Simulation tools have allowed engineers to explore conditions in an ideal environment. I mean, we have been able to explore reality before physically committing resources. So, take for instance you are trying to manufacture a moving part, with simulation tools, you’re able to see how this part will move in an ideal environment where the conditions can be extreme or favorable before you go commit resources to build the part. So, the simulation tools have been able to help us see the reality of how a machine will work before we commit resources.

It has also changed how we design and build systems. Simulation tools like FEA and CFD make it possible to identify stress concentrations, thermal gradients, flow instability, and failure risk early in the design process. And then we’re able to make changes using these simulation tools and these changes save us prototyping costs during the manufacturing stage.

In my research, simulation has helped me to filter or prevent weak ideas from becoming costly failures. Every failed prototype represented some materials that could have been wasted, some energy or labour that could have been wasted. Therefore, I think it is important to value simulation as it does not replace engineering’s judgement, but it strengthens it. It allows engineers to design systems that are not only efficient, but also reliable, repairable, and responsible in how we use resources.

Designing for manufacturing and assembly (DFM/DFA) often determines whether a product succeeds or struggles. Can you describe a moment when early design decisions made a major difference in your career?

One moment that shaped my thinking happened early in my engineering career when I worked on the coconut de-husking machine, which was my capstone at Federal University of Technology, Akure. On paper, the machine met all functional requirements, but during fabrication and assembly, we realized several components were difficult to align and service.

Minor design choices, such as, where do you place your fasteners and how tight the tolerance should be, caused significant delays and rework. That experience taught me that a design is only successful if it respects how the components will be built, how the parts will be assembled, and how these parts will be maintained.

High-volume manufacturing leaves little room for error. How do tooling, jigs and fixtures help ensure consistency and quality at scale?

In high volume manufacturing, tooling, jigs, and features are essential because they remove variability from the process. Human skill and attention can vary, but a well-designed feature enforces correct alignments and repeatability every time, and this consistency is what allows quality to scale.

I mean, if you are trying to produce something on a large scale, take for instance, uh, you need to produce 1,000 units of something, or even 10,000 units of something. You need to maintain the same parameters day in, day out, day in, day out to achieve same results. You probably won’t achieve the same results, but the standard deviation will be something within the control range.

Preventive maintenance doesn’t always get public attention, yet it’s critical to reliability. Why do you see it as a strategic, not just technical, function in manufacturing?

First thing here is, what is preventive maintenance? Preventive maintenance is when we monitor machines in real time, and we can prevent these machines from failing, which means we are trying to improve the meantime between failure of these machines. There’s preventive maintenance and there’s corrective maintenance. Corrective maintenance is fixing these machines after they have failed.

Preventive maintenance is making sure these machines don’t fail. I mean, it’s not possible for the machine not to fail, but we are trying to keep it reliable and work efficiently for a long period of time before it fails. Preventive maintenance is an important strategy because it directly affects cost and throughput of a machine.

I’ll ask you a simple question about your car engine. Imagine if you must get a new car engine every six months or just service the engine every three to six months, which one is cheaper? We can all agree servicing the engine is a lot cheaper. It is the same in our manufacturing space, which means it is cheaper to maintain machines than to buy new ones every time.

I remember when I worked as a maintenance engineer and I learnt the effect of downtime across production schedules. Every machine failure increases downtime and every increase in downtime means our production team is not achieving their target. The only way to reduce this is to ensure preventive maintenance is done on the plant so the machines work more efficiently thereby reducing the downtime and ultimately production teams can reach their target.

You work with data tools like MATLAB, Power BI, Python and SQL. How has data changed the way manufacturing problems are identified and solved today?

Data has changed the way manufacturing works today from being an intuition driven problem to evidence-based decision making.

What this means is in the past, people often came up with some theory or some hypothesis, then they try to make sure it works or they try to see if it works. But right now, because we now have a lot of historical data that we can leverage on, we are able to use this data to make decisions before coming up with any form of assumption. And now, because of that data, we have more correct information to help us make decisions.

Instead of responding to failures as isolated events, engineers can now identify patterns across time shifts and equipment types. In the past, if a machine failed, we would probably just maybe look for why it failed and just fix that thing. But now, because we have data, we can see how often it fails, how often this machine has failed, when it failed, what time it failed, how long did it fail for.

All those things are things we can put together, and it will help us achieve a very good decision. With tools like Python, SQL, Excel, and so on, we can make actionable insights, and we can then even visualize these insights and present them to the stakeholders or help the operations team or the production team to work more effectively while using these machines.

Engineers are often portrayed as working alone, but your experience involves cross-functional collaboration. How do you translate complex technical ideas for non-engineers on a team?

So, I will talk about the first part, which is cross-functional collaboration. In my last internship, like I said, I worked at the maintenance department where I had to communicate with the operations team, the technicians, and the procurement team.

For the operations team, it was to come up with a preventive maintenance schedule where my technicians can work during low-impact times. That means when the workload is not much and they are able to just quickly maintain these machines either through just physical inspection or just replacing some worn-out parts. Now, I also communicated with the procurement team in getting spare parts as quickly as possible from the vendors.

All of these were essential to have an efficient running operation for the company. The second part says, how do you translate technical ideas for non-engineers on a team? When translating technical ideas, I think it is important to break it down to simple concepts in which you can communicate.

I think this is also very important for me as a person to be able to understand my audience and speak in a way where everyone understands what I’m saying. If I’m trying to explain the concept of work done. Work done is force times distance. If I’m telling a physicist work done is force times distance, they probably already know.

If I’m telling someone from law department, I’ll say, for work to be done, something must move. And for something to move, something must make it move. Your laptop that you left somewhere will not leave that position except someone pushes it. And that push is applying the force. And now, the distance with which they’ve moved, that means from their initial position to the final position, is distance. Then, all of this must be done before we have work. I think it is very important to understand the audience and break technical ideas down to every individual in a way where they understand what you’re saying.

Process optimisation can mean saving seconds off a cycle time or reducing defects by small percentages. Why do these ‘small wins’ matter so much large-scale in your industry as innovation gains momentum?

I think on a scale, small improvements are small. In high volume production, a few seconds saved per cycle or a slight reduction in defect rates can translate into significant cost saving, higher throughput and reduced waste over time. Let’s say I make a revenue of 100 naira per product, and my profit is one naira. If I sell two, that means my profit is two naira. But then, if I sell one million, then my profit is one million naira.

Like small scale, these numbers might be small. But at large scale, nothing is small at large scale. Because if we can save 30 seconds of downtime on every production, that means at the end of the day, let’s say we’re making 5,000 barriers in throughput, we might be able to make 6,000.

And if our machine fails by 30 seconds every day, that might reduce our production as well to about 4,000. So, I don’t think anything is small in high volume manufacturing. And with these concepts, we understand the nature of cost, the nature of throughput, and the nature of waste.

Because anyhow we tilt our small change it has an effect. If it is tilted in the good way, then we save more money, we increase throughput, we reduce waste. But if it tilts downwards towards the negative, then we lose more money, we reduce throughput, and we increase waste.

As manufacturing becomes more automated and data-driven, what human skills do you believe will matter most for engineers in the future?

Human skills will always remain valuable because right now we still need humans to think. We still need to make changes. We are still adaptable. We understand things both at the high level and at the low level. And we can still look at very vague data and make decisions.

So, I think some skills that will be left even for engineers are one, your communication skills. How are you able to break things down? How are you able to communicate what you know to people?

Two; Collaboration skills. I think majorly soft skills will probably remain. Any soft skill you can think of. How are you able to break high concepts down? How are you able to see through the surface and understand the deeper meaning of things and how you can work with the data they have?

Also, how are you as an engineer able to work with what you don’t have? Those are some skills that will probably never leave even if AI replaces a lot of technical aspects. I think for me, a lot of soft skills will remain important. I also think ethical judgments and awareness will matter as well.

You recently co-authored an article on a journal of engineering and reports,titled, ‘Design, Simulation and Fabrication of a Coconut Dehusking Machine’. What can the country learn from the empowering cottage industries with small scale machines?

Thank you for the question. Nigeria is one of the top producers of coconut in the world, ranking in the top five globally with annual production often exceeding 1.5–2 million metric tons.

Empowering cottage industries with small-scale processing machines like the coconut dehusking machine we developed has several important implications for the country:

First, it dramatically improves productivity. Traditionally, coconuts are dehusked manually, which is labor-intensive, slow, and physically demanding. This limits how much product farmers and processors can handle in a day and increases costs. With mechanized dehusking, the same labor can process several hundred coconuts per hour, ours can process up to 240 coconuts per hour which will reduce time and effort and increase throughput.

Second, this kind of appropriate technology opens pathways to value-added coconut products. Once coconuts can be processed quickly and efficiently, farmers and small processors can focus on producing products such as copra, coconut oil, desiccated coconut, coconut milk, coir fiber, activated carbon from shells, and even cosmetic and food ingredients. These products have higher economic value than raw husks and offer better income streams for rural communities.

Third, improving mechanization at the cottage and small-scale level has broader economic benefits. It enhances farmer incomes, reduces post-harvest losses, and helps keep more of the value chain within Nigeria instead of exporting raw materials for processing overseas. For the engineering sector, it creates demand for locally designed and fabricated machines, fostering innovation, technical skills development, and jobs for technicians and manufacturers.

The key takeaway here is empowering cottage industries with small-scale machines like this coconut dehusker does more than save time: it supports rural livelihoods, catalyzes value-addition, drives local engineering solutions, and contributes to broader economic growth in Nigeria’s agricultural sector.