An Empirical Study to Evaluate Machine Tool Production Readiness and Performance

Purchasing the correct machine tool can have strategic implications for manufacturers, because incorrect selection will eventually lead to quality and productivity losses. Prior research has concentrated on developing an analytical decision support system to select and compare machine tools based on machine functionality – not capability. Machine tool selection decision analyses concentrate on machine specifications and characteristics, which disregard the actual machine accuracy and dynamic performance. In this paper, the need to include machine accuracy and performance in terms of cycle time, tool wear, and surface finish is described with a case study of manufacturing a typical aerospace component using three different production methodologies on three computer numeric controller (CNC) milling machines. Even though the three selected machine tools have similar technical specifications, which are adequate to manufacture the sample aerospace part, the machine accuracy and dynamic machining performance of the machines is significantly different. We conclude that it is necessary to include machine tool performance and production readiness attributes and not solely rely on the specifications when considering machine tool selection and purchasing.    

Reference: Deshpande A., An Empirical Study to Evaluate Machine Tool Production Readiness and Performance, The International Journal of Advanced Manufacturing Technology. DOI: 10.1007/s00170-012-4086-3.

Machine Tools Market to Hit $80.7B by 2015

A new research study predicts that the global machine tool industry is in the first stage of a rebound that will lift it to a market capitalization of $80.7 billion by 2015. Titled "Machine Tools: A Global Strategic Business Report," the report by Global Industry Analysts Inc. reviews market trends, drivers, product overview, competition, product introductions/innovations, and recent industry activity. The complete story available at American Machinist.

Reference: Machine Tools Market to Hit $80.7 Billion by 2015, Research Finds, American Machinist.

Feature Level Energy Assessments for Discrete Part Manufacturing

The next generation innovation will be driven by affordability and sustainability. Manufacturers are implementing energy efficiency and sustainable manufacturing practices not only from a cost reduction perspective but as a business strategy to succeed. This paper presents a bottom-up methodology for autonomous real-time energy consumption analytics at multiple level of abstraction from the lowest machining operation to enterprise level using MTConnect. The temporal aspect of data enables correlation of machining parameters and energy consumption at the most granular component feature and operation level. Decision support capability to drive part cost reduction and assessing the effect of adding or modifying a certain feature, operation or quality constraint is explained with help of a case study. Cost structure transparency and ability to justify improvement changes presents a significant opportunity for improved productivity and energy efficiency. Utilizing smart machine analysis tools, return-on-investment is quantified for production of an aerospace component on two machines.

Reference: Feature Level Energy Assessments for Discrete Part Manufacturing; Deshpande A., Snyder J., Scherrer D.; Proceedings of NAMRI/SME, Vol. 39, 2011, Corvallis, OR.

Legacy Machine Monitoring Using Power Signal Analysis

A typical manufacturing job shop comprises of legacy machine tools, new (modern) machine tools, material handling devices, and peripheral manufacturing equipments. Automated monitoring of legacy machine tools has been a long-standing issue for the manufacturing industry primarily because of the computer numeric controller (CNC) closed architecture and limited external communication functionality. This paper describes a non-invasive methodology and development of a software application to monitor real-time machine status, energy usage, and other machining parameters for a legacy machine tool using power signal analysis. State machine algorithm is implemented to detect tool changes and part count. The system architecture, implementation, benefits, limitations, and future work needed for the legacy machine tool monitoring application is explained in detail.

Reference: Legacy Machine Monitoring Using Power Signal Analysis; Deshpande A., Pieper R.; Proceedings of the ASME 2011 International Manufacturing Science and Engineering Conference, Corvallis, OR.