cnc程序加工中心_cnc加工自动可制造性评估的可制造性设计
cnc程序加工中心
An article by Yacine Mahdid and Ying Zhang.
Yacine Mahdid和Zhang Ying的文章。
The definition of design for manufacturability (DFM) goes as follow: “The general engineering practice of designing products in such a way that they are easy to manufacture” [0]. Doing so will invariably improve the return on investment by reducing cost and avoiding problems during the manufacturing process. DFM allows problems to be caught when the cost of error is the smallest (i.e. when the part isn’t being made yet). Even if a part is manufacturable, one should always consider whether it could be manufactured faster and/or cheaper using DFM principles.
可制造性設計(DFM)的定義如下：“以易于制造的方式設計產品的一般工程實踐” [0]。 這樣做將通過降低成本并避免制造過程中的問題而始終提高投資回報率。 當錯誤的代價最小時(例如，尚未制造零件時)，DFM允許發現問題。 即使零件是可制造的，也應始終考慮使用DFM原理是否可以更快和/或更便宜地制造它。
For computer numerical control machining (CNC machining) the cost is mostly driven by the machines time. This encompass the time the machine is being used, the setup time and the time to program the machine for the task. The time is also heavily affected by the material being used, the tolerances that are acceptable and the complexity of the part. If the part is to be designed for manufacturability, the designer needs to keep all these concepts in mind and balance them properly.
對于計算機數控加工(CNC加工)，成本主要由加工時間決定。 這包括使用機器的時間，設置時間和為任務編程機器的時間。 時間也受到所用材料，可接受的公差以及零件復雜性的嚴重影響。 如果要針對可制造性設計零件，則設計人員需要牢記所有這些概念并適當地加以平衡。
In this blog post, we will look at a few examples of DFM in action along with a review of tools and technique to programmatically automate the review process. If you want to learn more about different ways of saving up on cost in CNC machining, take a look at one of our previous blog post “10 méthodes pour économiser sur l’usinage CNC”. Here are a few examples of application of DFM.
在這篇博客文章中，我們將研究DFM的一些實際應用，以及對以編程方式自動執行審查過程的工具和技術的審查。 如果您想了解更多有關節省CNC加工成本的不同方法的信息，請閱讀我們以前的博客文章“ 10個使用CNC的新型機床”。 這是DFM應用的一些示例。
尖角 (Sharp Corners)
An obvious change that one can make to a design to make it easier to machine is avoiding sharp corner. If we look at a turning process [1] for two parts presented in figure 1, the panel 1A makes more aesthetic sense. However, since a turning process is a single point cutting tool removing material from a rotating workpiece to form a cylindrical shape, it is impossible to get this type of right angle with only the tool as every tool has some radius. Therefore, to have a sharp corner, one need to manually remove the rounded corner. Leaving this slight curvature that fits with the piece used for the turning process as seen in the panel 1B greatly simplifies the machining process and therefore reduces the machining time.
可以對設計進行簡化以使其更容易加工的一個明顯變化是避免出現尖角。 如果我們看一下圖1所示的兩個零件的車削過程[1]，則面板1A更具美感。 但是，由于車削加工是一種單點切削刀具，它從旋轉的工件上去除材料以形成圓柱形狀，因此僅靠刀具就不可能獲得這種直角，因為每個刀具都有一定的半徑。 因此，要形成一個尖角，需要手動去除圓角。 如在面板1B中所見，留下與車削過程中所使用的工件相適應的這種輕微曲率，極大地簡化了加工過程，從而減少了加工時間。
Figure 1圖1鉆Kong比 (Drilling Hole Ratio)
Another simple example of a mistake that can be made is long thin hole during the drilling process [2]. Standard drills are usually used to produce holes with a depth to diameter ratio of 3:1. If we look at figure 2, the red hole doesn’t respect this ratio and would require special tooling to avoid the drift that reduce the hole straightness which is difficult to correct and it is still an heavily studied topic [3]. This is problematic because special tooling will invariably increase the price of a quotation from a supplier.
另一個容易犯錯誤的例子是在鉆Kong過程中長而細的Kong[2]。 通常使用標準鉆頭來生產深徑比為3：1的Kong。 如果我們看圖2，紅Kong不遵守該比例，并且需要特殊的工具來避免漂移，因為漂移會降低Kong的直線度，這是很難校正的，它仍然是一個受到廣泛研究的話題[3]。 這是有問題的，因為特殊的工具總是會增加供應商的報價價格。
Figure 2圖2鉆Kong形狀 (Drilling Holes Shapes)
Also, the designer needs to take into consideration how the tool will interact with the surface during a drilling process. If we look at figure 3A, this part is aesthetically more pleasing than the one at panel B. However, if we think about a drill coming from the top it will make contact on the upper left section before the upper right. Similarly, it will exit unevenly at the bottom. Holes are easier and cheaper to drill if they are done on a flat surface and perpendicular to the drill motion as in panel B.
此外，設計人員還需要考慮到在鉆Kong過程中該工具將如何與表面相互作用。 如果我們看圖3A，這部分在美學上比面板B的要好。但是，如果我們考慮從頂部鉆一個鉆頭，它將在左上部分接觸右上角。 同樣，它將在底部不均勻地退出。 如果在平面上且垂直于鉆頭運動(如面板B中所示)進行打Kong，則鉆Kong更容易且更便宜。
Figure 3圖3特殊盲Kong處理 (Special Blind Holes Finish)
One final example that is obvious once we understand the why behind the modification is blind holes that finish in non-standard way. Even though through holes are preferred over blind ones, the finish surface should match standard drill endings. If we look at figure 4, the left hole has a very pointy finish surface. However, to produce such an ending, a good deal of fixture needs to be applied. In contrast, the right hole matches standard drill ending and doesn’t require costly extra steps.
一旦我們了解了修改背后的原因是盲Kong以非標準方式完成，那么最后一個明顯的例子就顯而易見了。 即使通Kong比盲Kong更可取，但精加工表面應與標準鉆頭匹配。 如果看圖4，左Kong的表面非常尖。 但是，要產生這樣的末端，需要使用大量的夾具。 相反，右Kong與標準鉆頭匹配，并且不需要昂貴的額外步驟。
Figure 4圖4自動可制造性評估 (Automatic Manufacturability Assessment)
Those were only few examples among many variables that can influence the machines time and the final cost of a part. So, having a specialist well versed into DFM in each and every design project would be ideal. However, even when a specialist is onboard human error can still happen, which can slow down the process of getting the part manufactured. Automatic manufacturability assessment is a competitive edge for business that want to optimize their production. For instance, the DFMPro software makes use of a rule-based checker system that identifies manufacturing issues such as thin walls and deep holes which has been shown to reduce the amount of work for the designers [4].
這些只是可能影響機器時間和零件最終成本的眾多變量中的少數幾個例子。 因此，在每個設計項目中都有一位精通DFM的專家將是理想的。 但是，即使專家在場，仍然可能發生人為錯誤，這會減慢零件制造的速度。 自動可制造性評估對于想要優化生產的企業來說是一項競爭優勢。 例如，DFMPro軟件利用基于規則的檢查系統來識別制造問題，例如薄壁和深Kong，這已證明可以減少設計人員的工作量[4]。
There are four main characteristics that influence the time a part spends in machining: visibility, reachability, accessibility and setup complexity [5]. Visibility depicts the view from the machine tool to the part. A part has high visibility if the surface area of the entire model can be seen from the view of the machine tool. Reachability stands for the lengths required for the machine tools to reach the surface of the model. The shorter length of the machine tool is preferred. Accessibility measures the ability of a model to be machined without tool collisions. Accessibility is found to be dependent on both the surface geometry and the tool size. Setup complexity measures the number of setups required to fabricate a part. When machining a complex geometry, the tool may need to be rotated in order to access certain features.
有四個主要特征會影響零件在加工上的時間：可見性，可達性，可及性和設置復雜性[5]。 可見性描繪了從機床到零件的視圖。 如果可以從機床的角度看到整個模型的表面積，則零件具有較高的可見性。 可達性代表機床到達模型表面所需的長度。 機床的長度越短越好。 可訪問性衡量的是在沒有工具碰撞的情況下加工模型的能力。 發現可及性取決于表面幾何形狀和工具尺寸。 設置復雜性可衡量制造零件所需的設置數量。 在加工復雜的幾何形狀時，可能需要旋轉工具才能訪問某些特征。
There are two main routes one can go to build a system that is aware of these constraints and that can detect and optimize a part geometry: Feature based and analytical feature-less based system. We will also discuss deep learning approach that are showing potential, however these are still quite new.
建立一個可以了解這些約束并可以檢測和優化零件幾何形狀的系統的主要途徑有兩條：基于特征的系統和基于無特征分析的系統。 我們還將討論深度學習方法，這些方法顯示出了潛力，但是它們仍然是很新的。
基于特征的系統 (Feature Based Systems)
These systems require the data scientist to define a set of features that the model will take into consideration in its analysis of a given part [6]. Features that characterize holes (amount, depth, width) or total machinable surface would be example of input into such a model. One caveat with this type of methodology is that it requires deep knowledge about what can influence the price of a part and require to develop the feature extraction modules that will generate a feature data frame. This module is usually coded in Python, MATLAB or R depending on the background of the data scientist with heavy help from battle-tested numerical computing library. Often time if the data scientist is dealing with complex input files, he will need to either parallelize his code for increased throughput or go closer to the metal with entire submodule built on C++ with library like LAPACK [8] or Armadillo [9]. This feature engineering step is costly and requires a lot of efforts. However, once done correctly, the tree-based models that one can use are both powerful and reliable (i.e. Random Forest or XGBoost, see [10] and [11] for an overview of these methods)
這些系統要求數據科學家定義一組特征，模型在分析給定零件時會考慮這些特征[6]。 表征Kong(數量，深度，寬度)或可加工表面總數的特征將成為此類模型的輸入示例。 這種方法的一個警告是，它需要對影響零件價格的因素有深入的了解，并需要開發將生成特征數據框的特征提取模塊。 該模塊通常使用Python，MATLAB或R進行編碼，具體取決于數據科學家的背景，并經過久經考驗的數值計算庫的大力幫助。 通常，如果數據科學家正在處理復雜的輸入文件，他將需要并行化代碼以提高吞吐量，或者需要使用CAP構建的整個子模塊(如LAPACK [8]或Armadillo [9])來接近金屬。 此功能工程步驟成本很高，并且需要大量的努力。 但是，一旦正確完成，一個人就可以使用的基于樹的模型既強大又可靠(例如，Random Forest或XGBoost，有關這些方法的概述，請參見[10]和[11])。
基于無特征的系統 (Feature-less Based Systems)
These types of system work directly with the generic model of the design instead of pre-calculated features. These systems work by analyzing the surface representation of the model and can work with any geometry [5]. Many such system have been built do deal with single process and more recently with multiple process like ANA [5]. They work mainly by calculating manufacturability metrics across the surface or in aggregate which can then be color coded back onto the generic model to provide direct feedbacks to the designer (Figure 5).
這些類型的系統直接與設計的通用模型一起使用，而不是預先計算的功能。 這些系統通過分析模型的表面表示來工作，并且可以與任何幾何形狀一起工作[5]。 已經建立了許多這樣的系統，它們處理的是單個流程，最近處理的是諸如ANA [5]的多個流程。 他們的工作主要是通過計算整個表面或總體上的可制造性指標，然后將它們進行顏色編碼回通用模型，以向設計人員提供直接反饋(圖5)。
There are many different variants on this type of system. One of them work with an octotree decomposition of the generic model [6] which has been shown to be useful in decomposing one complex model into multiple submodules. Another direction for such system is to work with the configuration space of a generic model, which means aggregating valid spatial configurations for a moving mechanism in an environment with obstacles around it [7], and optimizing for manufacturability within that space.
這種類型的系統有許多不同的變體。 其中之一與通用模型[6]的八叉樹分解一起工作，已證明在將一個復雜模型分解為多個子模塊中很有用。 這種系統的另一個方向是使用通用模型的配置空間，這意味著在周圍有障礙物的環境中聚集運動機構的有效空間配置[7]，并針對該空間內的可制造性進行優化。
The implementation of such systems is varied, but they are usually developed in a similar way than the feature-based system, with MATLAB or C++, as they can benefit from numerical optimization provided by heavily optimized linear algebra library.
這樣的系統的實現方式各不相同，但是它們通常使用MATLAB或C ++與基于特征的系統類似的方式開發，因為它們可以從高度優化的線性代數庫提供的數值優化中受益。
Figure 5圖5深度學習系統 (Deep Learning Systems)
Finally, deep learning method that has proven to yield great performance in computer vision have started to be used for DFM. There is no feature engineering steps as the model analyzes the surface representation of the model to determine the manufacturability. The model itself is doing the feature extraction as it learns to optimize for a set of desired outcomes. The type of deep neural network architecture best suited for this type of task are 3D convolutional neural network [12] which are models specifically well suited for computer vision in 3D space (See figure 6 for an example architecture applied to fMRI brain images which are 3D structure). These are extension of the classical convolutional neural network which excel at image recognition.
最后，事實證明，深度學習方法已在計算機視覺中產生了出色的性能，已開始用于DFM。 由于模型會分析模型的表面表示來確定可制造性，因此沒有特征工程步驟。 模型本身在學習針對一組所需結果進行優化時正在進行特征提取。 最適合此類任務的深度神經網絡體系結構類型是3D卷積神經網絡[12]，這是特別適合3D空間中計算機視覺的模型(有關應用于3d fMRI腦圖像的示例體系結構，請參見圖6)結構體)。 這些是經典卷積神經網絡的擴展，擅長圖像識別。
There is no free lunch however, these types of models are notoriously finicky [13] and require a great deal of expertise to train and optimize properly. Furthermore, they require a massive amount of data in order to generalize well to unseen instance. If, however, one such model is trained properly, the performance usually far outperforms the feature-based system and can even attain superhuman performance in some tasks [14, 15, 16]. In DFM however, analytical methods still outperform geometric deep learning algorithm [17], although there is hope that these models will scale well with enough data provided.
但是，這里沒有免費的午餐，眾所周知，這些類型的模型有些挑剔[13]，需要大量的專業知識才能正確地進行培訓和優化。 此外，它們需要大量數據才能很好地推廣到看不見的實例。 但是，如果一個這樣的模型得到了正確的訓練，其性能通常會遠遠超過基于特征的系統，甚至在某些任務中甚至可以達到超人的性能[14、15、16]。 然而，在DFM中，分析方法仍勝過幾何深度學習算法[17]，盡管希望這些模型能夠在提供足夠數據的情況下很好地擴展。
The tools that are usually used to build such models are less varied than the non-deep learning feature-less system, they almost always fall within the realm of Python with library like Tensorflow or Pytorch being the frontrunner of deep learning packages.
通常用于構建此類模型的工具的多樣性要比非深度學習無功能的系統少，它們幾乎總是屬于Python領域，而Tensorflow或Pytorch之類的庫是深度學習軟件包的領先者。
Figure 6圖6結論 (Conclusion)
Manufacturability assessment system doesn’t stop at only the model being used. Once the model is deployed and being actively used by users, there is intense monitoring that needs to happen in order to avoid the model becoming stale. DFM is a very dynamic subject. What was very difficult to produce 2 years ago might be common practice today. Therefore, keeping track of what is costly and what is not is an ongoing process. A tool that keeps itself up to date to the current trend and that can automatically detect potential costly features is a must for CNC part designer.
可制造性評估系統并不僅限于所使用的模型。 一旦模型被部署并被用戶積極使用，就需要進行嚴格的監控，以避免模型過時。 DFM是一個非常動態的主題。 2年前很難生產的東西可能在今天已經成為慣例。 因此，跟蹤什么是昂貴的什么不是什么是正在進行的過程。 CNC零件設計人員必須使用一種能夠及時了解當前趨勢并能夠自動檢測潛在的昂貴功能的工具。
Likewise, the optimization also doesn’t stop at DFM. Once you design for manufacturability, the next logical step is to design for assembly (DFA) [18], which means to design single parts to make the whole easily assemblable. However, like most technological advances, it is easy to get overwhelmed by every potential optimization possible. Taking incremental steps toward a more streamlined production is cautioned, starting at the most glaring pain point in the industry which is currently the outsourcing process and making your way up to tap your designers directly into DFM and DFA best practices.
同樣，優化也不止于DFM。 一旦針對可制造性進行設計，下一個邏輯步驟便是進行組裝設計(DFA)[18]，這意味著設計單個零件以使其整體易于組裝。 但是，像大多數技術進步一樣，很容易被所有可能的優化所淹沒。 從目前行業外包過程中最明顯的痛點開始，并采取漸進的步驟來朝著更簡化的生產方向告誡，并逐步將您的設計師直接運用到DFM和DFA最佳實踐中。
The type of system discussed in this blog post is currently being developed at GRAD4 through a partnership with McGill university and Mitacs. GRAD4 is currently developing it closely with their suppliers in order to detect the most common problematic features in a CNC part. This system will be available on their platform in the next few months!
目前，GRAD4正在與麥吉爾大學和Mitacs合作開發此博客文章中討論的系統類型。 GRAD4當前正在與供應商緊密合作，以檢測CNC零件中最常見的問題特征。 此系統將在未來幾個月內在其平臺上可用！
關于GRAD4 (About GRAD4)
GRAD4 is an innovative technology company that standardizes and automates the outsourcing process for buyers and suppliers in the manufacturing sector. It has developed the best technological solution for all companies that have needs or manufacturing capabilities in CNC, sheet metal and welded assembly. Incubated at Centech and accelerated by Ecofuel and NextAI, GRAD4 is financially supported by Front Row Ventures, the Ecofuel Funds and PME MTL. GRAD4 has won several competitions and recognitions, including the “Elevator Pitch” competition presented by National Bank, the Startupfest pitch competition by PME MTL, and the Centech’s Unicorn award as the most promising company in its cohort.
GRAD4是一家創新技術公司，致力于為制造業中的買家和供應商標準化和自動化外包流程。 它為所有在CNC，鈑金和焊接裝配方面有需求或制造能力的公司提供了最佳技術解決方案。 GRAD4在Centech孵化并由Ecofuel和NextAI加速發展，由Front Row Ventures，Ecofuel Funds和PME MTL提供資金支持。 GRAD4贏得了數項競賽和認可，包括國家銀行舉辦的“電梯瀝青”競賽，PME MTL舉辦的Startupfest瀝青競賽以及Centech的Unicorn獎，這是該組中最有前途的公司。
關于作者 (About the authors)
Yacine Mahdid is the Chief Technology Officer at GRAD4. He is leading the technical development of the platform and the R&D division along his marvelous team of talented developers and scientists. He is also a graduate student at McGill University trained in computational neuroscience (B.A.Sc.) with a specialization in machine learning. Currently working at the Biosignal Interaction and Personhood Technology Lab, his area of research is focused on creating predictive and diagnostic models to detect consciousness in individuals who are not able to speak or move.
Yacine Mahdid是GRAD4的首席技術官。 他帶領著一支由才華橫溢的優秀開發人員和科學家組成的團隊，負責平臺和研發部門的技術開發。 他還是麥吉爾大學(McGill University)的研究生，專門研究機器學習的計算神經科學(BASc。)。 目前，他在生物信號交互和人格技術實驗室工作，他的研究領域專注于創建預測和診斷模型，以檢測無法講話或移動的個體的意識。
Ying Zhang is a Mitacs Research Intern at GRAD4. Her main responsibility is to develop an automated manufacturability assessment for the GRAD4 platform. Currently, she is a Ph.D. candidate in mechanical engineering at McGill University and holds a bachelor’s and master’s degree in mechanical engineering at Georgia Tech. Her research focuses on the manufacturability analysis for the LPBF process, which is one of the AM techniques. Before starting her Ph.D. degree, she worked as a process and software engineer. She has more than six years’ experience in design, manufacturing, research, and lab.
張穎是GRAD4的Mitacs研究實習生。 她的主要職責是為GRAD4平臺開發自動化的可制造性評估。 目前，她是一名博士學位。 麥吉爾大學機械工程專業的候選人，并在佐治亞理工學院獲得機械工程學士和碩士學位。 她的研究專注于LPBFCraft.io的可制造性分析，這是AM技術之一。 在開始博士學位之前學位，她曾擔任流程和軟件工程師。 她在設計，制造，研究和實驗室方面擁有超過六年的經驗。
[0] DFM Retrieved July 20, 2020, from https://en.wikipedia.org/wiki/Design_for_manufacturability
[0] DFM于2020年7月20日從https://en.wikipedia.org/wiki/Design_for_manufacturability檢索
[1] Turning Retrieved July 20, 2020, from https://en.wikipedia.org/wiki/Turning
[1] Turning于2020年7月20日從https://en.wikipedia.org/wiki/Turning檢索
[2] Drilling Retrieved July 20, 2020, from https://en.wikipedia.org/wiki/Drilling
[2]鉆井(Drilling)于2020年7月20日從https://en.wikipedia.org/wiki/Drilling檢索
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翻譯自: https://medium.com/@admin_22931/design-for-manufacturability-for-cnc-machining-automatic-manufacturability-assessment-f69972943f17
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