Expert Machinist Research Articles

The Design for Additive Manufacturing Worksheet

Additive manufacturing (AM) technologies have become integral to modern prototyping and manufacturing. Therefore, guidelines for using AM are necessary to help users new to the technology. Many others have proposed useful guidelines, but these are rarely written in a way that is accessible to novice users. Most guidelines (1) assume the user has extensive prior knowledge of the process, (2) apply to only a few AM technologies or a very specific application, or (3) describe benefits of the technology that novices already know. In this paper, we present a one-page, visual design for additive manufacturing worksheet for novice and intermittent users which addresses common mistakes as identified by various expert machinists and additive manufacturing facilities who have worked extensively with novices. The worksheet helps designers assess the potential quality of a part made using most AM processes and indirectly suggests ways to redesign it. The immediate benefit of the worksheet is to filter out bad designs before they are printed, thus saving time on manufacturing and redesign. We implemented this as a go-no-go test for a high-volume AM facility where users are predominantly novices, and we observed an 81% decrease in the rate of poorly designed parts. We also tested the worksheet in a classroom, but found no difference between the control and the experimental groups. This result highlights the importance of motivation since the cost of using AM in this context was dramatically lower than real-world costs. This second result highlights the limitations of the worksheet.

Development and Evaluation of a Virtual Reality Training System Based on Cognitive Task Analysis: The Case of CNC Tool Length Offsetting

This article reports on the development and evaluation of a virtual reality training system VRTS for a specific machining task. A cognitive task analysis of expert machinists was conducted to examine whether this can be effective in developing a VRTS concerning tool length offsetting for a machining center. This analysis provided the necessary information for development and calibration of such a system. Subsequently, the effectiveness of the VRTS was evaluated by conducting an experiment with 29 mechanical engineering students. The VRTS set-up comprised a video projection of the machining center and a physical mock-up of its interface. The system demonstrated positive training transfer for the toll length offsetting task in terms of task accomplishment and of time to complete the task. No positive transfer was observed in terms of task accuracy, probably due to perceptual biases induced by the detailed specification of the VRTS. The present work provides evidence that cognitive task analysis was effective in identifying a number of key skills pertaining to the tool length offsetting task and in implementing ways to facilitate training in such tasks in a virtual environment. This article also demonstrates that even for tasks that include subtle perceptual skills VRTS may be beneficial regardless of the level of physical fidelity, provided that the cognitive organization of a task is adequately mapped in the system.

Automatic Reasoning for Defining Lathe Operations for Mill-Turn Parts: A Tolerance Based Approach

With the increase in computer-controlled hybrid machining (e.g., mill-turn machining), one needs to discern what features of a part are created during turning (i.e., with a lathe cutter) versus those created by milling. Given a generic part, it is desirable to extract the turnable and nonturnable features in order to obtain feasible machining plans. A novel approach for automating this division and for defining the resulting turning operations in a hybrid process is proposed in this paper. Given a mill-turn part, the algorithm first identifies the dominant rotational-axis in order to quickly generate the axisymmetric “as-lathed” model. This model is then subtracted from the original part to isolate the nonturnable features. Next, the as-lathed model is translated to a label-rich graph, which is fed into a grammar reasoning algorithm to produce feasible turning sequences. During the turning process planning, the knowledge encapsulated in the design tolerances is used to guide the generation of feasible turning sequences. Two case studies are provided to explain the details of our algorithm. One of the suggested turning plans is compared with a manually proposed plan from an expert machinist and the results show the optimality of our plan in satisfying the prescribed tolerances.

Cutting parameters optimisation in milling: expert machinist knowledge vs. soft computing methods

In traditional machining operations, cutting parameters are usually selected prior to machining according to machining handbooks and user's experience. However, this method tends to be conservative and sub-optimal since part accuracy and non machining failures prevail over machining process efficiency. In this paper, a comparison between traditional cutting parameter optimisation by an expert machinist and an experimental optimisation procedure based on Soft Computing methods is conducted. The proposed methodology increases the machining performance in 6.1% and improves the understanding of the machining operation through the use of Adaptive Neuro-fuzzy Inference Systems.

Adequacy of machining features for describing machining jobs

In machining, a piece of material called the raw stock is subjected to a succession of cuts until it reaches the finished state. In feature-based machining, the cuts made are classified as machining features. Several attempts have been made by researchers to define a set of machining features. The set of features defined by ISO 10303–224 is the only independent one outside commercial CAD packages. Expert Machinist, a module in the Pro/Engineer suite defines another such set. However, their adequacy to define each product shape has not been investigated. This present paper studies the machining features (three-axis milling machining features) defined by Expert Machinist to investigate adequacy of the set for describing every product shape. A heuristic approach has been taken by studying 62 components with differing complexities. The results show that the features indeed were adequate to define this relatively small sample of components.

Intelligent tool wear identification based on optical scattering image and hybrid artificial intelligence techniques

Tool wear monitoring is crucial for an automated machining system to maintain consistent quality of machined parts and prevent damage to the parts during the machining operation. A vision-based approach is presented for tool wear identification in finish turning using an adaptive resonance theory (ART2) neural network embedded with fuzzy classifiers. The proposed approach is established upon the fact that the optical scattering image of a turned surface is related to the wear of the cutting tool. By applying the technique of the ART2 neural network embedded with fuzzy classifiers, the state of wear of the turning tool is determined from captured images obtained by laser scattering from the machined surfaces of the workpiece. This approach is not unlike the visual inspection of the surface of a machined workpiece to determine the state of wear of a cutting tool by an expert machinist. However, experimental results indicate that the conventional technique of measuring surface finish does not give values that correlate well with tool wear. On the other hand, the laser scattering image provides a good indication of the tool wear as it is not readily affected by buildup edge or cold-welded material, scratches and other disruptive defects on the turned surface as the tool wears. In this paper, the theory on the laser scattering image and the principle of tool wear identification are described. Based on the scattering images, the proposed approach can correctly identify the condition of ‘significant wear’ prior to the rapid tool wear stage for the cutting tool.

Learning to monitor a machine tool

This paper deals with the issue of automatic learning and recognition of various conditions of a machine tool. The ultimate goal of the research discussed in this paper is to develop a comparehensive monitor and control (M&C) system that can substitute for the expert machinist and perform certain critical in-process tasks to assure quality production. The M&C system must reliably recognize and respond to qualitatively different behaviours of the machine tool, learn new behaviors, respond faster than its human counterpart to quality threatening circumstances, and interface with an existing controller. The research considers a series of face-milling anomalies that were subsequently simulated and used as a first step towards establishing the feasibility of employing machine learning as an integral component of the intelligent controller. We address the question of feasibility in two steps. First, it is important to know if the process models (dull tool, broken tool, etc.) can be learned (model learning). And second, if the models are learned, can an algorithm reliably select an appropriate model (distinguish between dull and broken tools) based on input from the model learner and from the sensors (model selection). The results of the simulation-based tests demonstrate that the milling-process anomalies can be learned, and the appropriate model can be reliably selected. Such a model can be subsequently utilized to make compensating in-process machine-tool adjustments. In addition, we observed that the learning curve need not approach the 100% level to be functional.

Toward an intelligent machine tool for flexible manufacturing

The need to manufacture accurate and inexpensive components in small batches has spurred the development of flexible manufacturing systems (FMSs). This paper reviews the basic components of FMSs and describes efforts currently underway at Carnegie-Mellon University to create an autonomous machine tool for the factory of the future. The machine tool would operate under computer control for part handling (manipulation), part fixturing, and metal cutting. This work argues for model-based engineering approaches integrated with expert systems that attempt to encapsulate the craftsmanships skills of human expert machinists.

Principles for part setup and workholding in automated manufacturing

Abstract The future automation of small batch manufacturing will critically depend on part fixturing. Fixturing involves the workholding components and the associated planning that must be done to properly position and finally clamp a part. In the past, part setup and clamping problems have been dealt with as a craft: human expert machinists, after an accumulation of many years of experience, derive part process planning strategies in a reasonable amount of time. This paper describes work that aims to create a science base for clamping: distinct patterns have been discovered that characterize how expert machinists plan setups and clamp parts. A core set of tradeoffs employed by the experts to clamp and machine parts has been established, and these tradeoffs are parameterized by models based on first principles of mechanical engineering and experimental results. Knowledge based planning methods of template matching, feature interaction guide selection, and attribute value approximation have been identified. The craftmen's knowledge based methods and the analytical tradeoffs are integrated to form setup and workholding guides within a hierarchical framework.

Did He Sham On The Gallows? A Review of the Windrath Case.

Chicago, June 22, 1896. To the Editor: —Joseph Windrath, a German 29 years of age, married, three children, an expert machinist, was hanged in Chicago June 5th last for the murder of a very popular man, the cashier of the West Chicago Street Ry. Co., when committing a robbery with an accomplice. The sentence set the execution for May 15th, but a reasonable doubt of his sanity being established since the date of sentence a respite was granted for a trial on that score, as the law of Illinois does not permit the execution of a lunatic. The State charged that he was shamming insanity and presented six medical witnesses, viz: Drs. N. S. Davis, Sanger Brown, Archibald Church, Walter Kempster, James Lydston and Dr. Fortner who is the jail physician, all of whom testified that he was both sane and shamming. On the other side Drs. Florence Hunt, O.