By Steve Meyer, Inventor
For better decisions and better results in your automation projects, prioritize the performance parameters.
In automation projects and machinery design, defining the performance goals is the starting point for any project. Meeting these goals is the primary objective of the engineering effort. Performance is what sells cars, computers, cell phones and just about every other product in our daily lives. It is what customers spend money on.
This industrial machine is an example of a design where throughput is a higher priority than accuracy or absolute precision for the application.
Achieving performance, though, requires:
• a thorough understanding of the attributes and behaviors desired, which is a crucial part of how the product will be used
• the know-how to create those features and behaviors
The most important part of project design is figuring out the steps to take in order to arrive at the working machine or process. The mental process is a little bit like imagining a destination and then working out the steps necessary to get there in reverse order.
Some aspects of defining and meeting performance goals depend on the economics of your industry. The amount of money that can, or will, be spent on a machinery project or in the design of a new product is directly connected to the market for that product or the economic benefit a new machine or process will create.
In the competitive world of production machinery, performance also has cost associated with the development of a unique process or component.
There are complex interactions between the competing performance metrics and the costs associated with achieving them. These interactions are not always obvious and that fact impedes decision making during the execution of a new machine project or product development program. When managing costs and development resources, how do we decide where to put time and money? For example, will additional cost in the motor feedback be beneficial to the overall results? How will selection of heavy duty components impact the life expectancy of the final design? Good decision-making requires good context for the decision making process.
The performance metrics by themselves are important to quantify, but additional and very valuable information becomes available to the project when the metrics are prioritized by their importance. Such a performance hierarchy makes clear what is most important and facilitates the decision making process during the implementation of the project.
The following is a discussion of a short list of four metrics you can apply in any machine design project (see table). You can come up with more parameters depending on your specific industry or situation. For the sake of simplicity, the four are contrasted as they might occur in two different scenarios.
The “prioritization problem”
Speed or part throughput is a top priority in any machine design. But is it exclusively the top priority, or are there other constraints that must be balanced with throughput? A prioritized framework of metrics clarifies the project objectives and improves decision-making. Based on the priorities of Case 1, for example, it might be more valuable to the project to spend time in reducing the cost of the machinery instead of spending more money on precision feedback. In the priorities of Case 2, the cost of the machine and the development of the machine are less important than achieving the precision requirement and part throughput.
While performance metrics help quantify design features, valuable information is gained when these features are prioritized by their importance to the customer, design goal, or cost.
Accuracy or part precision
The priority for accuracy or part precision can take many forms. At one level it can be as simple as dimensional accuracy. In hydraulic systems position accuracy may be 0.020 in. In CNC applications typical machine accuracy is 0.001 in. or better. High precision machined surface finishes are typically 50 millionths of an inch, which takes some effort to accomplish.
As precision goes, the most demanding industry is certainly semiconductor manufacturing where accuracy is measured in nanometers and Angstroms. Nanometer precision has a significant price tag. Feedback technology alone can cost thousands of dollars per axis in any given machine.
But another aspect of accuracy can be scrap reduction or increased yield. Often, improving machine accuracy leads to high quality parts and fewer rejects. The result is better overall economic performance for the manufacturer and better stewardship of the raw materials that go into production. The gigantic drop in flat screen television pricing in recent years was largely due to reduced scrap for increased yield.
What is higher yield worth? Plenty. Simply multiply the cost per part times the number of parts per year that are recovered by the increased quality and you have a one year recovery cost for the increased quality investment in your machine or process. As speed goes up in the particular process or machine, the benefit of increased yield goes up proportionately.
Speed or part throughput
Machinery manufacturing has important metrics for part production. The electronic assembly machinery market, for example, has one of the highest throughput requirements, where equipment is segmented by “part placements per hour.” At the high throughput end of the spectrum, machines with 50,000 PPH can ring in at $250,000 and higher depending on options. So in the race for higher performance, what would it cost to get another 10,000 PPH? Based on this example, it should sell for another $50,000.
So there are two questions that are important to consider in contemplating future machine development:
1) For a given improvement in speed how much can we collect in sales revenue versus what can actually be built?
2) How much will it cost to find out and can that cost be justified?
The cost threshold for these questions has dropped dramatically in recent years. Many vendors now provide extensions to the solid model design environment so that motion can be analyzed and successive iterations can easily be tested for best solutions before hardware is built. Custom machinery components can be fabricated cheaply using 3D printing technology either directly as metal parts or using a plastic sample part for a short run casting. These trends make it possible to investigate new solutions more quickly and economically than ever before.
The Lab Automation systems are very close in behavior to electronic assembly with comparable accuracy requirements.
The SMT pick-and-place system is an example of electronic assembly where the accuracy criteria is more important and more difficult to achieve. Electronic assembly has higher throughput values in parts per minute, but these values are relative across different industries. Moving a chip is a lot easier than moving a bottle of beer.
There are three constituents to cost:
• actual hardware cost for the machine
• development cost, which may include software and IP development costs
• maintenance or operating cost
In the course of project management, the three costs can be treated as separate. The available budget for the machinery project has to anticipate all three constituents. Engineering development programs need to be sensitive to the different natures of these costs and seek to balance them. As an example, reducing the hardware cost of the machine will likely show up as increased maintenance cost over the life of the machine.
Life expectancy or machine durability has a huge impact in the evaluation of machinery. If a $1 million machine will last 10 years, is $1.5 million machine with a 20-year life worth the additional investment? Will there be sufficient technology change in ten years that would make a 20-year life expectancy a liability?
Tax implications are often a consideration. Capital equipment may have an amortization of 5 to 20 years depending on the situation. Accelerated depreciation allows the purchaser to take the maximum deduction in typically 2-5 years. So the cost of the equipment may be discounted by the tax rebate available to the purchaser.
In the competitive sale of production machinery, similar machines compete for market share at different prices and throughputs. The amortized cost or the “cost per part” might be a way to make better comparisons. But as a plant user would evaluate the purchase of the equipment, only the hardware cost and expected maintenance cost are important. The development costs are borne by the machinery builder.
Steve Meyer is a published author and inventor in the field of electric motors, motion control, machine design, and control systems.
Filed Under: Factory automation, Automation components, Mechanical, Motion control • motor controls, Mechatronics