Automated green innovation for computerized numerical-controlled machining design

Abstract

With the development of manufacturing technology and strict international environmental regulations, green production has become an imperative research topic in the manufacturing industry. Because cutting is affected by several factors, the development of green processes is vital for automated cutting using machines. These factors are often subjective and are set up merely based on the manual and experience of engineers, reducing tool life and deteriorating tool precision; ultimately, this approach increases production costs and reduces production efficiency. Moreover, the ecological environment can be seriously affected. Furthermore, the parameters must be adjusted according to variations in the processing state, which is a considerable drawback for the automated cutting industry. In this study, we investigated the precision computerized numerical-controlled cutting process as an example. We first assessed the literature and investigated tool wear and cutting noise as quality standards for green computerized numerical-controlled cutting. The cutting depth, cutting speed, feed rate, and the tip of the center were selected as control parameters. The consistency of the results was verified through an expert questionnaire conducted using analytic hierarchy process, and the weighted values of the control parameters were obtained. Simultaneously, seven environmental efficiency elements of the World Business Council for Sustainable Development and TRIZ 39 engineering parameters of the CSI project were used to establish the engineering parameters for the green production design. Furthermore, 40 inventive principles from a contradiction matrix were used to design an optimization strategy to develop and verify an innovation strategy of singular quality. Finally, the experimental results revealed that implementing the analytic hierarchy process coupled with the TRIZ innovative thinking mode and green production concept enables enterprises to reduce their consumption of raw materials and waste production during the design process. This approach effectively reduces the burden on the environment and thus facilitates industry competitiveness and sustainability.

Research Progress of Noise in High-Speed Cutting Machining

“…Noise is one of the factors contributing to industrial pollution [ 83 ]. Noise in machining has attracted the attention of the academic community regardless of whether for ensuring the quality and stability of machining or meeting the current green manufacturing standards.…”

Parameter Optimization for Computer Numerical Controlled Machining Using Fuzzy and Game Theory

“…Most of the cutting parameter optimization literature obviously does not meet the needs of the industry as it either considers only a single quality (only one target quality) or has overly costly research. According to the research on cutting parameters, using Analytic Hierarchy Process (AHP) to combine the innovative thinking model of Teoriya Resheniya Izobretatelskikh Zadatch (TRIZ) and the concept of green production reduces the impact on the environment [1]. The optimal turning parameters obtained by using fuzzy semantic quantification can indeed be used as a method of analyzing parameters for practical cutting operations under environmental and cost considerations [2].…”

A Low‐Carbon Design Method Integrating Structure Design and Injection Process Design for Injection Molding Machines

“…is IMM is a mechatronics equipment which consists of injection molding components (3,4,10,13), feeding components (5), clamping components (1,2,14,15), heating components (12), and hydraulic components (6,7,8,9,11).…”

“…Among the existed LCD researches, scholars focus on two major fields, product structure LCD and product process LCD. Plenty of research studies have been conducted in the study field of product structure LCD [4,5]. Song and Lee [6] divided the product into several structure parts to analyze the greenhouse gas (GHG) emissions separately.…”

“…5.823 × 10 − 2 h4 3 + 4.524 × 10 − 4 L HB β S h 3 δ S + 1.587 × 10 − 5 L HB β 2 S δ S + 3.241 × 10 − 5 L HB β 2 S h 3 + 6.421 × 10 − 3 β S h 2 3 δ S − 12.531× 7.3 × 10 − 3 × 1.06 + 7.5 × 10 5 × τ HB1 L HB + 1.65 × 10 6 × τ HB2 L HB + 1.134 × 10 5…”