Sustainability-Based Expert System for Additive Manufacturing and CNC Machining

The development of technologies which enable resource efficient production is of paramount importance for the continued advancement of the manufacturing industry. In order to ensure a sustainable and clean energy future, manufacturers should be able to contrast and validate existing manufacturing technologies on a sustainability basis. In the post COVID-19 era of enterprise management, the use of artificial intelligence to simulate human expert decision making abilities will open new doors to achieving heightened levels of productivity and efficiency. The introduction of innovative technologies such as CNC machining and 3D printing to production systems has redefined the manufacturing landscape in a way that has compelled users to investigate into their sustainability. For the purposes of this study, cost effectiveness, energy and auxiliary material usage efficiency have been considered to be key indicators of manufacturing process sustainability. The objective of this research study is to develop a set of expert systems which can aid metal manufacturing facilities in selecting Binder Jetting, Direct Metal Laser Sintering or CNC Machining based on viable product, process, system parameters and inherent sustainability aspects. The expert systems have been developed using the knowledge automation software, Exsys CorvidÒ. Comprehensive knowledge bases pertaining to the objectives of each expert system have been created using literature reviews and communications with manufacturing experts. An interactive environment which mimics the expertise of a human expert has been developed by the application of suitable logical rules and backward chaining. The programs have been verified by analyzing and comparing the sustainability impacts of Binder Jetting and CNC Machining during fabrication of a stainless steel 316L component. According to the results of the study, Binder Jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC Machining, for fabrication of components feasible for each technology

Characterizing traits of coordination

How can one recognize coordination languages and technologies? As this report shows, the common approach that contrasts coordination with computation is intellectually unsound: depending on the selected understanding of the word "computation", it either captures too many or too few programming languages. Instead, we argue for objective criteria that can be used to evaluate how well programming technologies offer coordination services. Of the various criteria commonly used in this community, we are able to isolate three that are strongly characterizing: black-box componentization, which we had identified previously, but also interface extensibility and customizability of run-time optimization goals. These criteria are well matched by Intel's Concurrent Collections and AstraKahn, and also by OpenCL, POSIX and VMWare ESX.Comment: 11 pages, 3 table

Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System

This paper presents the design, development and SIMULINK implementation of the lumped parameter model of C-axis drive from GEISS five-axis CNC machine tool. The simulated results compare well with the experimental data measured from the actual machine. Also the paper describes the steps for data acquisition using ControlDesk and hardware-in-the-loop implementation of the drive models in dSPACE real-time system. The main components of the HIL system are: the drive model simulation and input – output (I/O) modules for receiving the real controller outputs. The paper explains how the experimental data obtained from the data acquisition process using dSPACE real-time system can be used for the development of machine tool diagnosis and prognosis systems that facilitate the improvement of maintenance activities

Online on-board optimization of cutting parameter for energy efficient CNC milling

Energy efficiency is one of the main drivers for achieving sustainable manufacturing. Advances in machine tool design have reduced the energy consumption of such equipment, but still machine tools remain one of the most energy demanding equipment in a workshop. This study presents a novel approach aimed to improve the energy efficiency of machine tools through the online optimization of cutting conditions. The study is based on an industrial CNC controller with smart algorithms optimizing the cutting parameters to reduce the overall machining time while at the same time minimizing the peak energy consumption

Derivation of a cost model to aid management of CNC machine tool accuracy maintenance

Manufacturing industries strive to produce improved component accuracy while not reducing machine tool availability or production throughput. The accuracy of CNC production machines is one of the critical factors in determining the quality of these components. Maintaining the capability of the machine to produce in-tolerance parts can be approached in one of two ways: run to failure or periodic calibration and monitoring. The problem is analogous to general machine tool maintenance, but with the clear distinction that the failure mode of general machine tool components results in a loss of production, whereas that of accuracy allows parts to be produced, which are only later detected as non-conforming as part of the quality control processes. This distinction creates problems of cost-justification, since at this point in the manufacturing chain, any responsibility of the machine is not directly evident. Studies in the field of maintenance have resulted in cost calculations for the downtime associated with machine failure. This paper addresses the analogous, unanswered problem of maintaining the accuracy of CNC machine tools. A mathematical cost function is derived that can form the basis of a strategy for either running until non-conforming parts are detected or scheduling predictive CNC machine tool calibrations. This is sufficiently generic that it can consider that this decision will be based upon different scales of production, different values of components etc. Therefore, the model is broken down to a level where these variables for the different inputs can be tailored to the individual manufacturer

Thread Quality Control in High-Speed Tapping Cycles

Thread quality control is becoming a widespread necessity in manufacturing to guarantee the geometry of the resulting screws on the workpiece due to the high industrial costs. Besides, the industrial inspection is manual provoking high rates of manufacturing delays. Therefore, the aim of this paper consists of developing a statistical quality control approach acquiring the data (torque signal) coming from the spindle drive for assessing thread quality using different coatings. The system shows a red light when the tap wear is critical before machining in unacceptable screw threads. Therefore, the application could reduce these high industrial costs because it can work self-governance.This research was funded by the vice‐counseling of technology, innovation and competitiveness of the Basque Government grant agreements IT‐2005/00201, ZL‐2019/00720 (HARDCRAFT project) and KK‐2019/00004 (PROCODA project)