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Production manufacture instruments for measuring dimensions in mechanical engineering

Production manufacture instruments for measuring dimensions in mechanical engineering

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Content:

A Classification Scheme for Smart Manufacturing Systems’ Performance Metrics

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This paper proposes a classification scheme for performance metrics for smart manufacturing systems. The discussion focuses on three such metrics: agility, asset utilization, and sustainability. For each of these metrics, we discuss classification themes, which we then use to develop a generalized classification scheme.

In addition to the themes, we discuss a conceptual model that may form the basis for the information necessary for performance evaluations. Finally, we present future challenges in developing robust, performance-measurement systems for real-time, data-intensive enterprises. Rapidly changing global markets have increased the competitive pressure on businesses across the world. Businesses in high- Gross Domestic Product countries must overcome high labor costs and increased regulations while continuing to provide innovative, quality products and services.

Manufacturing companies, in particular, must continue to develop and implement new technology solutions to remain competitive. These solutions, when implemented, must enable manufacturing systems to function at continually higher performance levels typified by lower operating costs, higher quality products, and better decision making. The consistent need for improved performance in manufacturing has fueled the development of a number of generalized production concepts [ 1 ].

Some examples include intelligent manufacturing [ 2 — 4 ], agent-based Manufacturing [ 5 — 7 ], agile Manufacturing [ 8 ], next generation manufacturing [ 9 ], advanced manufacturing [ 10 ], and digital manufacturing [ 11 ]. While these concepts have been implemented with varying degrees of success, opportunities still exist to increase performance by using advanced hardware and software systems that leverage sensor inputs in near real time.

Sensor-enabled manufacturing equipment and controller software have provided a new, data-rich environment that can help improve system performance. We generally refer to such systems as smart manufacturing systems SMS [ 12 ], which take advantage of the availability of affordable sensors, computing, and data-storage; Internet-of-Things architectures; machine learning and data-mining algorithms; and cloud-based storage and data-management systems.

Such systems are highly adaptive and have some level of autonomy. More importantly, SMS also have the capabilities to enable rapid realization of products, dynamic response to changing demand, and real-time performance optimization of production and supply-chain networks [ 13 ].

This paper reports on an effort to support the adoption and use of the SMS paradigm in industry through the development of structured approaches for selecting and computing metrics using the above technologies. Smart manufacturing systems use information and communications technology ICT to implement the capabilities described in Section 1 [ 13 ].

SMS provide information to decision makers by collecting, monitoring, and analyzing data collected from relevant sources within and beyond the manufacturing system itself. In this way, SMS can help manufacturers identify and respond to system-level manufacturing inefficiencies, which have been growing in importance given increasing market demands for improved flexibility, productivity, and sustainability [ 14 ].

The key to the success of SMS is to identify the right set of data needed to assure the desired performance of the system. In general, we can define the performance of a SMS using three inter-related criteria: agility, asset utilization, and sustainability [ 15 ]. Asset utilization refers to the efficient and effective use of manufacturing resources to create value. When considered together, an agile manufacturing system that best utilizes its assets is one that efficiently and effectively responds to external and internal changes and consistently provides value using minimal time, effort, and cost.

It is important to differentiate efficiency from effectiveness in this definition since they refer to two separate characteristics of a manufacturing system.

Duflou et al. Ideal performance demands that a smart manufacturing system be both efficient and effective. Sustainability is becoming a global concern. Generally speaking, there are three pillars of sustainability, which are generally referred to as the triple bottom line: the environment, the economy, and the society.

In this context, we can argue that sustainability includes agility and asset utilization considerations since both of the latter categories affect the environmental and economic impacts of manufacturing systems.

Much of the technical literature on sustainable manufacturing focuses on those processes and systems that minimize environmental and social impacts while simultaneously maximizing economic benefits [ 21 — 26 ]. For example, Rachuri et al. In all definitions, sustainability within a manufacturing system must be considered in the context of a closed system using spatially- and temporally-defined boundaries [ 18 ], [ 27 ].

This and other requirements and characteristics of performance metrics selection and evaluation are the focus of Section 3. A large number of metrics and indicators have been defined to capture the performance of manufacturing systems from multiple perspectives — all related to the three pillars of sustainability.

For example, there has been a significant amount of work to expand or consolidate the three pillars into more detailed or easily applicable ideas, e.

While these specific and consolidated metrics are important and useful, they are not well suited for capturing the details needed to understand where improvements may be needed and should be used primarily in conjunction with more detailed performance metrics [ 24 ]. Metrics that attempt to address all three pillars of sustainability are called 3-D metrics. These metrics are very useful as in conducting an initial performance evaluation, but they are not usually detailed enough to find the source of a specific inefficiency.

In these cases, we can use more specific performance metrics by considering 2-D and 1-D metrics, which cover two or one of the pillars of sustainability, respectively [ 24 — 25 ].

It is important to assure that any selected performance metric captures useful and relevant information efficiently and effectively. Characteristics of ideal metrics include comprehensiveness, controllability, cost effectiveness, manageability, meaningfulness, robustness, and timeliness [ 33 ].

Singh et al. This index lists eight, product-specific indicators to account for the three pillars of sustainability: life-cycle global-warming potential, life-cycle air-quality potential, sustainable materials, restricted substances, drive-by-exterior noise, safety, mobility capability, and life-cycle ownership costs [ 36 ].

Other works have suggested more general performance metrics for process, equipment, and cell levels, but they are not considered complete catalogs [ 26 ]. A more detailed assessment can be conducted when evaluating specific processes and cell; but, these assessments are based on indicators related to emission, energy, material, and water intensities. These indicators are associated with two of the pillars only: environment and economy [ 37 — 40 ].

There is still a significant need to expand these lower-level assessments to include a more comprehensive list of society-related and manufacturing-specific metrics e.

Creating a comprehensive list of multi-level performance metrics that adequately accounts for all of the sustainability pillars remains elusive. A significant obstacle is the many types of manufacturing processes and the fact that many metrics are process or cell specific.

An exhaustive list would be useful if an accompanying selection manual allows users to choose appropriate performance metrics for specific stakeholders and across hierarchical levels. A careful balance is needed to consider the appropriate number of performance metrics for analysis since each additional metric can require significant time, money, and resources to determine. Jain [ 41 ] points out that the utility of available sustainability metrics and specific indicators is limited in making project choices and policy decisions.

He proposed the use of a multi-attribute utility framework for measuring sustainability progress. Gutowski et al. Jain and Rachuri [ 43 ] considered the dimension of maturity for sustainable manufacturing and proposed a basic set of metrics to use for manufacturing systems within small and medium enterprises.

SMS extend across the manufacturing enterprise and include the supply chain. Choosing metrics at the supply-chain level has been the focus of the Supply Chain Council, an industrial consortium with almost member companies.

The Council has used its own Supply Chain Operations Reference SCOR model, which provides a standard perspective for supply-chain planning and execution, to choose those metrics. To date, the SCOR model includes over metrics along five attributes: reliability, responsiveness, agility, cost, and asset-management efficiency. A variety of additional metrics have also been developed outside of the SCOR model. The literature includes other sets of supply-chain metrics, some of which overlap those defined in the SCOR model.

Lin and Li [ 45 ] define defects per opportunity, yield, and rolled-throughput yield as performance measures for supply chain based on the Six-Sigma philosophy. Clearly, there is no shortage of metrics. What is needed is an organizing scheme to assess and use appropriate metrics for any proposed performance evaluation.

There have been many attempts to develop such an organizing scheme for such a wide variety of metrics. These attempts have been motivated by the difficulty that manufacturers have had in using these metrics.

A number of repositories for performance metrics have been created, such as the Sustainable Manufacturing Indicators Repository [46]. The metrics in such repositories cover a range of aspects of organizational operations and associated impacts. Graedel and Allenby [ 47 ] recognize the challenge of grouping sustainability metrics to communicate aggregated achievement. They support setting up a hierarchical grouping scheme across local, national, and global levels.

Joung et al. Fiskel et al. While both approaches present useful classification schemes, they address only sustainability-related metrics. The SCOR model discussed earlier presents a model for linking metrics. That linking is based on a three-level hierarchical structure, where lower-level metrics serve as diagnostics for higher-level metrics SCC The level 1 metrics assess high-level measures that go across SCOR processes.

The lower-level metrics generally focus on a subset of SCOR processes. Various efforts described above to develop an organizing scheme, such as repositories and hierarchies, offer a promising direction of work.

The presented efforts have helped in providing structure to evaluate agility, asset utilization, and sustainability as well as better manage processes, facilities, and supply chains. However, efforts are required to provide a similar scheme for SMS metrics. The scheme in turn can support the implementation of infrastructure for the collection of metrics and their use for real-time decision making in SMS [ 2 — 4 ].

We now present our approach to developing such a scheme. Classification schemes or frameworks for manufacturing metrics have been presented in the past, but most schemes or frameworks do not focus on the three criteria described in Section 2 for smart manufacturing systems: agility, asset utilization, and sustainability.

Blackburn and Valerdi [ 51 ] recommend an open-minded, value-focused approach to understanding performance before identifying metrics and setting up measurement processes to collect data to support performance characterization and analysis.

SMS must account for measurement throughout the manufacturing enterprise, including the process, machine, and facility levels. In this work, we develop a classification scheme to be used in creating a repository of metrics for smart manufacturing. This section describes the motives behind the classification and a review of existing classification and data structure for the repository illustrated with example metrics.

Competitive priorities can differ significantly between manufacturing companies; hence, a suitable set of performance metrics for one company might not be suitable for another. Additionally, the metrics-selection process should capture the proper definitions of the performance metrics to improve their selection and use [ 52 — 53 ].

A significant challenge in this regard is the development of classification methods for an increasingly large number of existing performance measures [ 54 ].

The ability of an organization to choose a specific metric for a particular situation is hindered when the purpose for the performance measure is not captured anywhere.

Many of the performance measures contain information on when they should be used but not where they would be efficient and effective measurements. The unavailability of classification schemes that deal with these aspects leaves organizations with little practical guidance on how to decide what performance measures are suitable for their specific needs [ 55 ].

Another important driver for classifications is the ability to provide a performance measurement system with contextual information. These factors are in turn interrelated and change over time, which makes the task of classification challenging. It should also focus on short-term as well as long-term results. The content information for performance measurement should include the formulas used to compute the metrics.

The requirements for simplicity and accuracy of performance measures are not always compatible with each other, which makes compromises unavoidable.

This involves quality assurance, quality control and metrology. We use quality assurance to gain confidence that quality requirements will be fulfilled.

However, as manufacturers attempt to grow their trade in the global business environment, the need for measurement accuracy has become an even higher priority due to the time- and cost-savings that businesses can potentially achieve. Some common applications that make use of these advanced technologies include Parts Inspection, Alignment, Reverse Engineering, and Dimensional Measurement. All four categories are similar in the way that their need for measurement and documentation accuracy is tightly woven into each of their core activities. To elaborate, measurement accuracy is widely agreed to be a most important aspect in mechanical parts inspections. Machine misalignment on the other hand can delay an entire manufacturing line.

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Manufactured items differ in size and dimensions from the original CAD model due to variations in the manufacturing processes. With SLA 3D printing, you can create accurate prototypes and custom parts cost-effectively. See and feel Formlabs quality firsthand. For example, when drilling a mounting hole, the hole had to be within a specified X-Y area. An accurate tolerancing specification, however, would define the position of the hole in relation to the intended position, the accepted area being a circle.

Mechanical Engineering

Mechanical gauges are instruments that measure pressure, dimensions, levels, etc. They can be mechanical or electro-mechanical devices and offer displays ranging from direct-reading rules to digital LCDs. Gauges which measure pressure are classified as analog or digital depending on their readouts. Dimensional gauges are classified by what they measure, be it bore diameter, depth, or height, and are specific to machining processes. Level gauges measure the level of fluid in tanks and pressure vessels. Other gauges are used in very specific measuring applications from spark plug gaps to screw threads. Below we list the different types of gauges used in industries.

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What They Do : Mechanical engineering technicians help mechanical engineers design, develop, test, and manufacture mechanical devices. Work Environment : Mechanical engineering technicians assist with manufacturing processes in factories or with development phases in research and development labs before manufacturing takes place.

This paper proposes a classification scheme for performance metrics for smart manufacturing systems. The discussion focuses on three such metrics: agility, asset utilization, and sustainability. For each of these metrics, we discuss classification themes, which we then use to develop a generalized classification scheme. In addition to the themes, we discuss a conceptual model that may form the basis for the information necessary for performance evaluations. Finally, we present future challenges in developing robust, performance-measurement systems for real-time, data-intensive enterprises. Rapidly changing global markets have increased the competitive pressure on businesses across the world. Businesses in high- Gross Domestic Product countries must overcome high labor costs and increased regulations while continuing to provide innovative, quality products and services. Manufacturing companies, in particular, must continue to develop and implement new technology solutions to remain competitive.

Looking for other ways to read this?

Gauge , also spelled gage , in manufacturing and engineering, a device used to determine, either directly or indirectly, whether a dimension is larger or smaller than another dimension that is used as a reference standard. Some devices termed gauges may actually measure the size of the object to be gauged, but most gauges merely indicate whether the dimensions of the test object are sufficiently close to those of the standard—i. Gauges may operate mechanically or electrically.

Main research topics are: a surface metrology, b coordinate metrology, and c process metrology. The latest research in surface metrology deals with traceability, modelling and characterization of polished surfaces and multifunctional surfaces obtained through hard machining followed by Robot Assisted Polishing.

To save this word, you'll need to log in. The earliest evidence we have for the noun gauge goes back to the 15th century, when English spelling was not yet standardized, and the word in question was spelled gauge and gage with roughly equal frequency. Gauge began to be preferred in the late 19th century for most general uses. Some claim that gage appears as a variant more frequently in the U. Nonetheless, total use of the word gage is small when compared to the total use of the word gauge. The verb gauge , which refers to measuring or estimating, also has a variant gage. This variant appears to show up primarily in informal sources, though not often. Gauge is by far the preferred spelling in general usage for both the noun and the verb; we encourage you use it. First Known Use of gage Noun 2 14th century, in the meaning defined at sense 1 Verb 15th century, in the meaning defined at sense 1 Noun 3 , in the meaning defined above History and Etymology for gage Noun 2 Middle English, "pledge, formal pledge of a person's appearance to do battle," borrowed from Anglo-French — more at wage entry 1 Verb borrowed from Anglo-French gager "to offer surety, give as a pledge," derivative of gage "pledge, gage entry 2 " Noun 3 by shortening Keep scrolling for more Learn More about gage Share gage Post the Definition of gage to Facebook Share the Definition of gage on Twitter Time Traveler for gage. See more words from the same century From the Editors at Merriam-Webster.

mechanical engineering faction. • High tech manufacturing and test mm in diameter x mm in height measurement equipment, as well as.

Manufacturing metrology

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Types of Gauges - A ThomasNet Buying Guide

David A. Madsen is an emeritus faculty member in drafting technology and the Autodesk Premier Training Center at Clackamas Community College in Oregon City, Oregon, where he also served as an instructor and department chairperson for nearly 30 years. In addition to his community college experience, David served as a drafting technology instructor at Centennial High School in Gresham, Oregon.

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Machinery onboard ships require regular care and maintenance so that their working life and efficiency can be increased, and the cost of operation, which includes unnecessary breakdowns and spares, can be reduced. For different types of machinery and systems, various measuring tools, instruments and gauges are used on a ship. Measuring instruments and gauges are used to measure various parameters such as clearance, diameter, depth, ovality, trueness, etc. These are critical engineering parameters, which describe the condition of the working machinery.

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