Experience-Based vs. Fact-Based Decisions: How AI/ML Is Changing the Way Engineers Work

July 28, 2020

Artificial intelligence (AI) and machine learning (ML) are quickly becoming common parts of the manufacturing process. The latest technologies are simplifying the way that engineers solve problems, reducing downtime and increasing production throughput.

Many of the benefits of AI and ML technologies stem from the ability to replace experience-based decision-making with fact-based decision-making. Precision and efficiency are essential characteristics in the manufacturing industry. Engineering decisions are often made based on a careful analysis of the facts.

When you are dealing with components that require precise dimensions and large-scale production runs, you cannot rely on guesses or estimates. Unfortunately, engineers do not always have the luxury to analyse every detail.

There are times when you need to use your experience to make decisions, which increases the risk of defects, waste, and delays. Machine learning is helping to remove those risks by eliminating the need for experience-based decisions.

Developing Products with More Value

Artificial intelligence can enhance manufacturing at every stage, starting with the product development stage. Engineers can use input from AI software to aid in the development of products that deliver more value to customers.

AI software is already being used to gain more insight into customer behaviour and needs. Manufacturers and engineers can use this insight to get a better understanding of the end users. They can eliminate unnecessary features and focus on meeting customer demands. After analysing the needs of customers, AI and ML technologies can assist with the design of the product.

For example, instead of using experience to determine the potential risks or costs associated with adding or removing a feature, engineers can rely on software to evaluate the decision. Companies may harness the power of big data to strengthen their decision-making processes during product planning, strategising, and modelling.

Improving Quality Control and Reducing Defects

Smart manufacturing is helping to reduce the need for manual input and labour when it comes to quality control and quality assurance processes. Quality control is intended to identify defects and prevent faulty products from reaching customers. Machine learning technology assists with finding defects and the root causes of them.

Traditionally, an engineer may analyse the output of a production run to detect defects. They then review each process to determine the cause, which is a time-consuming task. Even with a thorough quality control process, engineers may overlook certain details or fail to catch the problem.

Engineers no longer need to use their instinct and experience to detect faults and defects. Machine learning software monitors machinery and product data throughout the entire manufacturing process. This technology can even help predict quality control issues before they arise.

Revolutionising Predictive Maintenance

One of the roles of the engineer is as a maintenance worker. They need to ensure that machinery and equipment can meet production needs to prevent bottlenecks and reduce downtime.

Traditional procedures involve manually set thresholds and scheduled maintenance for handling the upkeep of machinery and equipment. Engineers may also use their gut and experience to estimate how frequently machines require inspections.

AI and machine learning software are changing the maintenance process. Along with constantly monitoring the output of the machinery to detect product defects, artificial intelligence can detect changes in the performance of the machinery. This allows the software to predict when a machine may require maintenance.

AI software may also assist with reallocating resources to take a machine offline without impacting production. These decisions could take an engineer an incredible amount of time and may not yield satisfactory results.

ML software provides instant feedback, allowing engineers to adapt quickly to unexpected maintenance issues and other setbacks. Experts predict that these benefits will lead to a 38% increase in the use of machine learning for predictive maintenance.

Increasing the Efficiency of Manufacturing

The combination of advantages discussed helps create more efficient manufacturing processes. There is less of a need for human input at every stage from product development to production and quality control.

Engineers can use big data to aid product development, quality control, and predictive maintenance. This takes some of the guesswork out of engineering, which saves time and reduces human errors.  

Using AI equipment to automate manufacturing processes provides machine learning software with consistently accurate data. Instead of requiring engineers to spend hours evaluating the outputs of sensors and equipment, the software can quickly compile and review data spanning multiple years.

Engineers can easily adjust production processes and designs to accommodate the insight provided by the software. This results in faster response to potential issues and the ability to adapt manufacturing processes for custom orders.

ML software can also be used as a simulation tool within digital twins. After obtaining data from various sources, the ML software creates a virtual representation of the manufacturing process and simulates multiple scenarios. The software “learns” from these scenarios to help improve the efficiency of any manufacturing stage.

Last Thoughts on AI and ML in Engineering and Manufacturing

People often associate AI and ML with automation on the manufacturing room floor. Smart manufacturing is helping to reduce the need for manual operations. However, the use of machine learning extends through every stage of manufacturing.

Engineers now have powerful tools that make their jobs easier. The assistance of ML software can aid or automate a wide range of decision-making tasks. From planning and modelling a product to monitoring its production run, engineers can use big data to boost efficiency and quality.

With enterprises increasing AI spending by 62% in the last year, more companies are adopting these technologies. As smart manufacturing becomes the standard, you can expect new benefits to appear.

In the next few years, AI and ML may completely change the way that engineers work. Instead of depending on their experience, they can rely on the facts provided by big data.

Feel free to also check out our other posts:

Machine Learning Projects in Manufacturing: Expectations vs. Reality

The Future of Six Sigma — Machine Learning Is Redefining Industrial Precision

Digital Twins in Quality Control — What If You Could Predict Changes in Quality