Making Machine Tools Smarter

Dec. 16, 2006
Companies have reengineered manufacturing to the extent that workers and capital assets are capable of four times the output of 20 years ago. That productivity growth helped yesterday's mass production succeed, but the new challenge for machine tools is m

If the smarter machine tools now being researched could also communicate in the vernacular, here's what they could be saying to their computer numerical control (CNC) or some higher level computer on the plant network:

"Hey, you've sent me this part program, I've analyzed it and from what I can tell, you need to change what you've programmed and here are my limits. And by the way, if you want me to hold five-tenths tolerance, I will not be able to do that because I am having a problem with my x drive. It's not running smoothly and you need to send maintenance down here to look at that drive."

Translate those words into computerese and that will be an example of the kind of small talk between the machine tools and controls of tomorrow, says Paul R. Warndorf, vice president, technology with the Association for Manufacturing Technology (AMT), based in McLean, Va. AMT is part of the Smart Machine Platform Initiative (SMPI) established by the Coalition for Manufacturing Technology Infrastructure (CMTI).

The following companies and organizations comprise the Advisory Team for the Smart Machine Program Initiative:

  • Association For Manufacturing Technology (AMT)
  • BAE Systems
  • Benet Labs, Watervliet Arsenal
  • Caterpillar
  • Cincinnati Machine
  • Defense Logistics Agency
  • Delphi Automotive
  • Ex-One
  • Ford Motor Co.
  • General Dynamics Land Systems
  • General Dynamics Ordnance and Tactical Systems
  • GE Aviation
  • Hurco Cos.
  • Lockheed Martin
  • NIST-Manufacturing Engineering Laboratory
  • NNSA Y-12 National Nuclear Complex
  • Picatinny Arsenal
  • Pratt & Whitney
  • Remmele Engineering
  • Rolls Royce
  • Sikorsky Aircraft
  • TechSolve
  • The Boeing Co.
  • U.S. Army Research

The government-funded initiative is a collaboration among commercial firms, government agencies and a variety of machine tool and equipment vendors. The program grew out of two workshops sponsored by the National Institute of Standards and Technology -- a Smart Machine Workshop in 2000 and a First Part Correct Workshop in 2002, says TechSolve's John Kohls, director, Smart Machine Program, Cincinnati.

Now in its second phase, SMPI is much more than a cost-cutting effort to pare operating costs. Look at it as a fundamental repositioning on how machine tools are applied, emphasizes Kohls. Cincinnati Machine, an SMPI advisory team member, emphasizes the competitive value. "The project's objective is to provide better information that will enable management to achieve the goal of making parts better, faster and at lower cost," says Richard Curless, vice president, engineering and development for the Hebron, Ky.-based machine tool maker.

Kohls describes the enhanced functionality: "A smart machine is one that can make decisions about the manufacturing process in real time. A smart machine knows itself. It is one that understands how to make a part. It can monitor, diagnose and correct when deviations occur. And it can learn for optimizing in the future. It would also tell us the remaining life of the cutting tools, the spindles, the bearings and the slides so we would know how long it could continue without a degradation in quality."

SMPI's intent is to rapidly accelerate the evolution of machine tool intelligence that began with the introduction of punched tape numerical control in the 1960s, followed by ever smarter computer numerical controls, adds Kohls. One example of current progress by vendors is the monitoring and analysis solution introduced by GE Fanuc Automation Inc. at last September's IMTS trade show. Proficy Machine Tool Efficiency 4.0, a Web-based solution, is designed to provide insightful, actionable analysis by collecting comprehensive root cause data from machine tools and other equipment. It also provides a suite of remote diagnostic tools to maximize mean-time between failures and minimize mean-time to repair, and it can be used to populate fields in computer maintenance management systems.

Another example of smarter machine progress is Cincinnati Machine's software feature for detecting imbalance conditions in rotary B-axis table of its multi-tasking machining centers. Integrated within the Siemens SINUMERIK 840D control, the new balance sensor feature monitors Z-axis following error to rapidly and accurately sense imbalance. Once detected, calculations by a balance assist program generate a graphical display to identify the location and how much compensating weight is needed. The technology is also applicable to the Giddings & Lewis vertical turning lathes.

With more organizations pursuing the smart direction, Kohls sees a growing need for SMPI to emphasize a collaborative role among the researchers, including both vendors and universities. "A lot of organizations are pursuing their own development to meet their specific needs," he says. "By bringing them together with collaborative leadership, we are hoping to use research partnering as a way to accelerate the transition to smarter machines."

TechSolve will make test beds available to spur collaboration among researchers, whether they be universities or providers of equipment or software. Kohls says the intent is to validate smart concepts for both developers and users.

Putting the Plan Together

In SMPI's technology plan, the initiative is described as a reinvention of the basic manufacturing environment, enabling dramatic improvements in the productivity and cost of designing, planning, producing and delivering high-quality product within short cycle times.

In phase one, which concluded in December 2005, NCMS members Cincinnati Machine, Caterpillar and Advanced Technology Services collaborated with the Red River Army Depot and Cherry Point Naval Depot in implementing four proof-of-concept "Smart Machine" installations focused on maintenance support functions.

Additionally, the pilot project defined specific, achievable objectives for the subsequent phase two project. This is the current initiative that is intended to lead toward a more advanced, intelligent, next-generation factory.

Among the benefits noted in phase one was a reduction in manufacturing process variation in a rubber compression molding application and an improvement in process efficiency of a diesel engine transfer line through better insight into equipment utilization and effectiveness, according to Cincinnati Machine.

The current phase two project expanded the pilot project's original four sites to nine depot and industrial partner sites. In addition to further demonstrating smart machine functionality on a broad mix of applications, an objective of phase two is to demonstrate a means of providing secure management access to the shop floor data produced by the installations.

According to Tony Haynes, NCMS director, advanced technology programs, "In this project, we will be installing the infrastructure of a variety of new and legacy machine types to allow the equipment to automatically monitor and log their 24/7 condition in a consistent fashion. We will then process the data into concise, factual information that can be conveniently and securely accessed, even from remote locations, to enable managers and support personnel to optimize factory asset performance. NCMS is very pleased to sponsor this important project that promises to achieve smart machine goals long discussed, but never -- until now -- accomplished."

Project participants cite several key benefits. These include further improvements in process efficiency, process health monitoring, process variation reduction, implementation of automated maintenance management systems and development of algorithms for facilitating predictive functions.

The New Node

In the initiative's technology plan, the basic machine tool is moving from being a passive asset with a single operator, to an element, or node, in an electronically integrated environment where the three basic attributes of a machine tool are pulsed for information: its availability, capacity and capability. The vision: the machine tool of tomorrow will interact with the design functions, macro- and micro-planning, the verification of quality, and the scheduling and management of product flow.

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In addition, tomorrow's machine tool will have an active role in its maintenance and contribute to problem solving and learning for the optimization of the process it performs. Prediction: In an integrated enterprise, the machine tool will evolve to be a peripheral device in the computer network.

The plan calls for the machine tool to be a smart platform for manufacturing operations. Capabilities are to include the detection, reaction and correction of deviations. By enabling the capability for first product correct, the machine tool will eliminate much of the trial-and-error associated with a new part or process, adds AMT's Warndorf.

The primary technology goals for smart operation and control are based on instantaneously sensing the condition of the machine, workpiece and the tool. Comparisons to a process model will determine if adjustments are needed.

Today, says Kohls, there is little capability to achieve model-based control of processes except for a single, or possibly a few, select parameters. Machine modeling that has been done has been designed to relate to ideal conditions as opposed to incorporating real-world effects with uncertainty accommodated. Kohls says one of the challenges is that the smart system must solve all of the challenges -- not just the trivial. Response to the grossly abnormal occurrence is the challenge, he adds.

In most machine operations, a significant amount of data is generated, but there is no good mechanism to utilize the information. Data are usually not time-stamped nor is there any means to correlate machine condition data and process data, rendering the information as nonactionable. The initiative's plan concludes that the majority of the data gathered is ultimately lost and represents a significant opportunity for productivity increases and achieving the goal of first part correct.

The initiative's plan does not call for a completely autonomous maintenance strategy. Instead, when action is required, the enterprise is alerted with instructions about what action is needed.

The drive toward smarter machine tools is not a challenge confined to individual machines and their controls, Warndorf notes. At the same time that efforts are being made to integrate the connectivity and performance of a sector of the economy, its business strategies must accommodate how that affects brand differentiation.

However, the biggest challenge posed by smarter machines may be the one posed to manufacturing management, suggests Cincinnati Machine's Tom Kuhnell, manager of electrical and software engineering. The emerging era of smart machines offers new, rapidly expanding competitive potential for machine tool builders and users alike.

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